Design of 2D massless Dirac fermion systems and quantum spin Hall insulators based on sp–sp2 carbon sheets npj Comput. Mater. (IF 8.941) Pub Date : 2018-10-18 Minwoo Park, Youngkuk Kim, Hoonkyung Lee
Grapheneis a massless Dirac fermion system, featuring Dirac points in momentum space. It was also first identified as a quantum spin Hall (QSH) insulator when considering spin–orbit coupling (SOC), which opens a band gap at the Dirac points. This discovery has initiated new research efforts to study the QSH effect, towards its application for quantum computing and spintronics. Although the QSH effect has been observed in HgTe quantum wells, the SOC strength of graphene is too small (~1 µeV) to induce the topological insulator phase in an experimentally achievable temperature regime. Here, we perform a systematic atomistic simulation to design two-dimensional sp–sp2 hybrid carbon sheets to discover new Dirac systems, hosting the QSH phase. 21 out of 31 newly discovered carbon sheets are identified as Dirac fermion systems without SOC, distinct from graphene in the number, shape, and position of the Dirac cones occurring in the Brillouin zone. Moreover, we find 19 out of the 21 new Dirac fermion systems become QSH insulators with a sizable SOC gap enhanced up to an order of meV, thus allowing for the QSH effect at experimentally accessible temperatures. In addition, based on the 26 Dirac fermion systems, we make a connection between the number of Dirac points without SOC and the resultant QSH phase in the presence of SOC. Our findings present new prospects for the design of topological materials with desired properties.
Mechanism of contact pressure-induced friction at the amorphous carbon/alpha olefin interface npj Comput. Mater. (IF 8.941) Pub Date : 2018-09-26 Xiaowei Li, Aiying Wang, Kwang-Ryeol Lee
Combining an amorphous carbon (a-C) film with a lubricating oil can significantly improve the friction performance and lifetime of moving mechanical components. However, the friction mechanism is not well understood owing to a lack of information regarding the structure of the interface when exposed to high contact pressure. Here, we select linear alpha olefin, C5H10, as a lubricant and study the evolution of the structure of the a-C/C5H10/a-C sliding interface under contact pressure via reactive molecular dynamics simulation. Our results suggest that introducing C5H10 into the a-C/a-C interface reduces the friction coefficient by up to 93% compared with no lubricant, although the lubricating efficiency strongly depends on the contact pressure. In particular, increasing the contact pressure not only induces the binding of the lubricant with a-C, but also facilitates the dissociation of the C5H10 carbon-carbon skeleton by specific scissions, which governs the friction behavior. These results disclose the underlying lubrication mechanism and could enable the development of new and effective lubricating systems with long lifetimes.
Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials npj Comput. Mater. (IF 8.941) Pub Date : 2018-09-11 Lauri Himanen, Patrick Rinke, Adam Stuart Foster
Automated and verifiable structural classification for atomistic structures is becoming necessary to cope with the vast amount of information stored in various computational materials databases. Here we present a general recursive scheme for the structural classification of atomistic systems and introduce a structural materials map that can be used to organize the materials structure genealogy. We also introduce our implementation for the automatic classification of two-dimensional structures, especially focusing on surfaces and 2D materials. This classification procedure can automatically determine the dimensionality of a structure, further categorize the structure as a surface or a 2D material, return the underlying unit cell and also identify the outlier atoms, such as adsorbates. The classification scheme does not require explicit search patterns and works even in the presence of defects and dislocations. The classification is tested on a wide variety of atomistic structures and provides a high-accuracy determination for all of the returned structural properties. A software implementation of the classification algorithm is freely available with an open-source license.
Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization npj Comput. Mater. (IF 8.941) Pub Date : 2018-09-10 Henry C. Herbol, Weici Hu, Peter Frazier, Paulette Clancy, Matthias Poloczek
Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional” space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10%) needed to complete an exhaustive search. We find an optimal composition within 15 ± 10 iterations in a HOIP compositional space containing 72 combinations, and within 31 ± 9 iterations when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.
Microstructure design using graphs npj Comput. Mater. (IF 8.941) Pub Date : 2018-09-07 Pengfei Du, Adrian Zebrowski, Jaroslaw Zola, Baskar Ganapathysubramanian, Olga Wodo
Thin films with tailored microstructures are an emerging class of materials with applications such as battery electrodes, organic electronics, and biosensors. Such thin film devices typically exhibit a multi-phase microstructure that is confined, and show large anisotropy. Current approaches to microstructure design focus on optimizing bulk properties, by tuning features that are statistically averaged over a representative volume. Here, we report a tool for morphogenesis posed as a graph-based optimization problem that evolves microstructures recognizing confinement and anisotropy constraints. We illustrate the approach by designing optimized morphologies for photovoltaic applications, and evolve an initial morphology into an optimized morphology exhibiting substantially improved short circuit current (68% improvement over a conventional bulk-heterojunction morphology). We show optimized morphologies across a range of thicknesses exhibiting self-similar behavior. Results suggest that thicker films (250 nm) can be used to harvest more incident energy. Our graph based morphogenesis is broadly applicable to microstructure-sensitive design of batteries, biosensors and related applications.
Mapping the elastic properties of two-dimensional MoS2 via bimodal atomic force microscopy and finite element simulation npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-29 Yuhao Li, Chuanbin Yu, Yingye Gan, Peng Jiang, Junxi Yu, Yun Ou, Dai-Feng Zou, Cheng Huang, Jiahong Wang, Tingting Jia, Qian Luo, Xue-Feng Yu, Huijuan Zhao, Cun-Fa Gao, Jiangyu Li
Elasticity is a fundamental mechanical property of two-dimensional (2D) materials, and is critical for their application as well as for strain engineering. However, accurate measurement of the elastic modulus of 2D materials remains a challenge, and the conventional suspension method suffers from a number of drawbacks. In this work, we demonstrate a method to map the in-plane Young’s modulus of mono- and bi-layer MoS2 on a substrate with high spatial resolution. Bimodal atomic force microscopy is used to accurately map the effective spring constant between the microscope tip and sample, and a finite element method is developed to quantitatively account for the effect of substrate stiffness on deformation. Using these methods, the in-plane Young’s modulus of monolayer MoS2 can be decoupled from the substrate and determined as 265 ± 13 GPa, broadly consistent with previous reports though with substantially smaller uncertainty. It is also found that the elasticity of mono- and bi-layer MoS2 cannot be differentiated, which is confirmed by the first principles calculations. This method provides a convenient, robust and accurate means to map the in-plane Young’s modulus of 2D materials on a substrate.
Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-24 Hongxiang Zong, Ghanshyam Pilania, Xiangdong Ding, Graeme J. Ackland, Turab Lookman
Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium.
Entropy contributions to phase stability in binary random solid solutions npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-22 Anus Manzoor, Shubham Pandey, Debajit Chakraborty, Simon R. Phillpot, Dilpuneet S. Aidhy
High entropy alloys contain multiple elements in large proportions that make them prone to phase separation. These alloys generally have shallow enthalpy of mixing which makes the entropy contributions of similar magnitude. As a result, the phase stability of these alloys is equally dependent on enthalpy and entropy of mixing and understanding the individual contribution of thermodynamic properties is critical. In the overall vision of designing high entropy alloys, in this work, using density functional theory calculations, we elucidate the contributions of various entropies, i.e., vibrational, electronic and configurational towards the phase stability of binary alloys. We show that the contribution of electronic entropy is very small compared to the vibrational and configurational entropies, and does not play a significant role in the phase stability of alloys. The configurational and vibrational entropies can either destabilize or can collectively contribute to stabilize the solid solutions. As a result, even those systems that have negative mixing enthalpy can show phase instability, revealed as a miscibility gap; conversely, systems with positive mixing enthalpy can be phase stable due to entropic contributions. We suggest that including entropic contributions are critical in the development of theoretical framework for the computational prediction of stable, single-phase high entropy alloys that have shallow mixing enthalpies, unlike ordered intermetallics.
Online search tool for graphical patterns in electronic band structures npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-20 Stanislav S. Borysov, Bart Olsthoorn, M. Berk Gedik, R. Matthias Geilhufe, Alexander V. Balatsky
Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a “Mexican hat” pattern or an effectively free electron gas, characterized by a parabolic dispersion. To find material realizations of these features, manual inspection of electronic band structures represents a relatively easy task for a small number of materials. However, the growing amount of data contained within modern electronic band structure databases makes this approach impracticable. To address this problem, we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The source code of the developed tool is freely available and can be adapted to any other electronic band structure database.
Interplay between epidermal stem cell dynamics and dermal deformation npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-20 Yasuaki Kobayashi, Yusuke Yasugahira, Hiroyuki Kitahata, Mika Watanabe, Ken Natsuga, Masaharu Nagayama
Tissue growth is a driving force of morphological changes in living systems. Whereas the buckling instability is known to play a crutial role for initiating spatial pattern formations in such growing systems, little is known about the rationale for succeeding morphological changes beyond this instability. In mammalian skin, the dermis has many protrusions toward the epidermis, and the epidermal stem cells are typically found on the tips of these protrusions. Although the initial instability may well be explained by the buckling involving the dermis and the basal layer, which contains proliferative cells, it does not dictate the direction of these protrusions, nor the spatial patterning of epidermal stem cells. Here we introduce a particle-based model of self-replicating cells on a deformable substrate composed of the dermis and the basement membrane, and investigate the relationship between dermal deformation and epidermal stem cell pattering on it. We show that our model reproduces the formation of dermal protrusions directing from the dermis to the epidermis, and preferential epidermal stem cell distributions on the tips of the dermal protrusions, which the basic buckling mechanism fails to explain. We argue that cell-type-dependent adhesion strengths of the cells to the basement membrane are crucial factors influencing these patterns.
Ductile deformation mechanism in semiconductor α-Ag2S npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-13 Guodong Li, Qi An, Sergey I. Morozov, Bo Duan, William A. Goddard III, Qingjie Zhang, Pengcheng Zhai, G. Jeffrey Snyder
Inorganic semiconductor α-Ag2S exhibits a metal-like ductile behavior at room temperature, but the origin of this high ductility has not been fully explored yet. Based on density function theory simulations on the intrinsic mechanical properties of α-Ag2S, its underlying ductile mechanism is attributed to the following three factors: (i) the low ideal shear strength and multiple slip pathways under pressure, (ii) easy movement of Ag–S octagon framework without breaking Ag−S bonds, and (iii) a metallic Ag−Ag bond forms which suppresses the Ag–S frameworks from slipping and holds them together. The easy slip pathways (or easy rearrangement of atoms without breaking bonds) in α-Ag2S provide insight into the understanding of the plastic deformation mechanism of ductile semiconductor materials, which is beneficial for devising and developing flexible semiconductor materials and electronic devices.
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-06 Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian S. Alexandrov
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.
Multiobjective genetic training and uncertainty quantification of reactive force fields npj Comput. Mater. (IF 8.941) Pub Date : 2018-08-02 Ankit Mishra, Sungwook Hong, Pankaj Rajak, Chunyang Sheng, Ken-ichi Nomura, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta
The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO3 by H2S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.
Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-30 Binbin Wang, Liangshun Luo, Enyu Guo, Yanqing Su, Mingyue Wang, Robert O. Ritchie, Fuyu Dong, Liang Wang, Jingjie Guo, Hengzhi Fu
The hidden order of atomic packing in amorphous structures and how this may provide the origin of plastic events have long been a goal in the understanding of plastic deformation in metallic glasses. To pursue this issue, we employ here molecular dynamic simulations to create three-dimensional models for a few metallic glasses where, based on the geometrical frustration of the coordination polyhedra, we classify the atoms in the amorphous structure into six distinct species, where “gradient atomic packing structure” exists. The local structure in the amorphous state can display a gradual transition from loose stacking to dense stacking of atoms, followed by a gradient evolution of atomic performance. As such, the amorphous alloy specifically comprises three discernible regions: solid-like, transition, and liquid-like regions, each one possessing different types of atoms. We also demonstrate that the liquid-like atoms correlate most strongly with fertile sites for shear transformation, the transition atoms take second place, whereas the solid-like atoms contribute the least because of their lowest correlation level with the liquid-like atoms. Unlike the “geometrically unfavored motifs” model which fails to consider the role of medium-range order, our model gives a definite structure for the so-called “soft spots”, that is, a combination of liquid-like atoms and their neighbors, in favor of quantifying and comparing their number between different metallic glasses, which can provide a rational explanation for the unique mechanical behavior of metallic glasses.
Evaluation of thermodynamic equations of state across chemistry and structure in the materials project npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-25 Katherine Latimer, Shyam Dwaraknath, Kiran Mathew, Donald Winston, Kristin A. Persson
Thermodynamic equations of state (EOS) for crystalline solids describe material behaviors under changes in pressure, volume, entropy and temperature, making them fundamental to scientific research in a wide range of fields including geophysics, energy storage and development of novel materials. Despite over a century of theoretical development and experimental testing of energy–volume (E–V) EOS for solids, there is still a lack of consensus with regard to which equation is indeed optimal, as well as to what metric is most appropriate for making this judgment. In this study, several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature. Our findings do not indicate a clear “best” EOS, but we identify three which consistently perform well relative to the rest of the set. Furthermore, we find that for the aggregate data set, the RMSrD is not strongly correlated with the nature of the compound, e.g., whether it is a metal, insulator, or semiconductor, nor the bulk modulus for any of the EOS, indicating that a single equation can be used across a broad range of classes of materials.
Characterization of domain distributions by second harmonic generation in ferroelectrics npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-24 Yuan Zhang, Yi Zhang, Quan Guo, Xiangli Zhong, Yinghao Chu, Haidong Lu, Gaokuo Zhong, Jie Jiang, Congbing Tan, Min Liao, Zhihui Lu, Dongwen Zhang, Jinbin Wang, Jianmin Yuan, Yichun Zhou
Domain orientations and their volume ratios in ferroelectrics are recognized as a compelling topic recently for domain switching dynamics and domain stability in devices application. Here, an optimized second harmonic generation method has been explored for ferroelectric domain characterization. Combing a unique theoretical model with azimuth-polarization-dependent second harmonic generation response, the complex domain components and their distributions can be rigidly determined in ferroelectric thin films. Using the proposed model, the domain structures of rhombohedral BiFeO3 films with 71° and 109° domain wall, and, tetragonal BiFeO3, Pb(Zr0.2Ti0.8)O3, and BaTiO3 ferroelectric thin films are analyzed and the corresponding polarization variants are determined. This work could provide a powerful and all-optical method to track and evaluate the evolution of ferroelectric domains in the ferroelectric-based devices.
Phase-field model of pitting corrosion kinetics in metallic materials npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-24 Talha Qasim Ansari, Zhihua Xiao, Shenyang Hu, Yulan Li, Jing-Li Luo, San-Qiang Shi
Pitting corrosion is one of the most destructive forms of corrosion that can lead to catastrophic failure of structures. This study presents a thermodynamically consistent phase field model for the quantitative prediction of the pitting corrosion kinetics in metallic materials. An order parameter is introduced to represent the local physical state of the metal within a metal-electrolyte system. The free energy of the system is described in terms of its metal ion concentration and the order parameter. Both the ion transport in the electrolyte and the electrochemical reactions at the electrolyte/metal interface are explicitly taken into consideration. The temporal evolution of ion concentration profile and the order parameter field is driven by the reduction in the total free energy of the system and is obtained by numerically solving the governing equations. A calibration study is performed to couple the kinetic interface parameter with the corrosion current density to obtain a direct relationship between overpotential and the kinetic interface parameter. The phase field model is validated against the experimental results, and several examples are presented for applications of the phase-field model to understand the corrosion behavior of closely located pits, stressed material, ceramic particles-reinforced steel, and their crystallographic orientation dependence.
Machine learning hydrogen adsorption on nanoclusters through structural descriptors npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-19 Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Lauri Himanen, Adam S. Foster
Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.
Automated defect analysis in electron microscopic images npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-18 Wei Li, Kevin G. Field, Dane Morgan
Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-16 Andrea Rovinelli, Michael D. Sangid, Henry Proudhon, Wolfgang Ludwig
The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.
Co6Se8(PEt3)6 superatoms as tunable chemical dopants for two-dimensional semiconductors npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-13 Arthur C. Reber, Shiv N. Khanna
Electronic, optoelectronic, and other functionalities of semiconductors are controlled by the nature and density of carriers, and the location of the Fermi energy. Developing strategies to tune these parameters holds the key to precise control over semiconductors properties. We propose that ligand exchange on superatoms can offer a systematic strategy to vary these properties. We demonstrate this by considering a WSe2 surface doped with ligated metal chalcogenide Co6Se8(PEt3)6 clusters. These superatoms are characterized by valence quantum states that can readily donate multiple electrons. We find that the WSe2 support binds more strongly to the Co6Se8 cluster than the PEt3 ligand, so ligand exchange between the phosphine ligand and the WSe2 support is energetically favorable. The metal chalcogenide superatoms serves as a donor that may transform the WSe2 p-type film into an n-type semiconductor. The theoretical findings complement recent experiments where WSe2 films with supported Co6Se8(PEt3)6 are indeed found to undergo a change in behavior from p- to n-type. We further show that by replacing the PEt3 ligands by CO ligands, one can control the electronic character of the surface and deposited species.
Fine-grained optimization method for crystal structure prediction npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-10 Kei Terayama, Tomoki Yamashita, Tamio Oguchi, Koji Tsuda
Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures. Relaxing a structure requires multiple optimization steps. It is time consuming to fully relax all the initial structures, but it is difficult to figure out which initial structure leads to the optimal solution in advance. In this paper, we propose a optimization method for crystal structure prediction, called Look Ahead based on Quadratic Approximation, that optimally assigns optimization steps to each candidate structure. It allows us to identify the most stable structure with a minimum number of total local optimization steps. Our simulations using known systems Si, NaCl, Y2Co17, Al2O3, and GaAs showed that the computational cost can be reduced significantly compared to random search. This method can be applied for controlling all kinds of local optimizations based on first-principles calculations to obtain best results under restricted computational resources.
Author Correction: Design of high-strength refractory complex solid-solution alloys npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-09 Prashant Singh, Aayush Sharma, A. V. Smirnov, Mouhamad S. Diallo, Pratik K. Ray, Ganesh Balasubramanian, Duane D. Johnson
Author Correction: Design of high-strength refractory complex solid-solution alloysAuthor Correction: Design of high-strength refractory complex solid-solution alloys, Published online: 09 July 2018; doi:10.1038/s41524-018-0087-6Author Correction: Design of high-strength refractory complex solid-solution alloys
Computational design of bimetallic core-shell nanoparticles for hot-carrier photocatalysis npj Comput. Mater. (IF 8.941) Pub Date : 2018-07-06 Luigi Ranno, Stefano Dal Forno, Johannes Lischner
Computational design can accelerate the discovery of new materials with tailored properties, but applying this approach to plasmonic nanoparticles with diameters larger than a few nanometers is challenging as atomistic first-principles calculations are not feasible for such systems. In this paper, we employ a recently developed material-specific approach that combines effective mass theory for electrons with a quasistatic description of the localized surface plasmon to identify promising bimetallic core-shell nanoparticles for hot-electron photocatalysis. Specifically, we calculate hot-carrier generation rates of 100 different core-shell nanoparticles and find that systems with an alkali-metal core and a transition-metal shell exhibit high figures of merit for water splitting and are stable in aqueous environments. Our analysis reveals that the high efficiency of these systems is related to their electronic structure, which features a two-dimensional electron gas in the shell. Our calculations further demonstrate that hot-carrier properties are highly tunable and depend sensitively on core and shell sizes. The design rules resulting from our work can guide experimental progress towards improved solar energy conversion devices.
Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images npj Comput. Mater. (IF 8.941) Pub Date : 2018-06-28 Rama K. Vasudevan, Nouamane Laanait, Erik M. Ferragut, Kai Wang, David B. Geohegan, Kai Xiao, Maxim Ziatdinov, Stephen Jesse, Ondrej Dyck, Sergei V. Kalinin
Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn toward the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS2, which allows tracking and determination of growth rate of voids. We highlight two key aspects of these results: (1) it shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical “real image” cases and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images.
Machine learning modeling of superconducting critical temperature npj Comput. Mater. (IF 8.941) Pub Date : 2018-06-28 Valentin Stanev, Corey Oses, A. Gilad Kusne, Efrain Rodriguez, Johnpierre Paglione, Stefano Curtarolo, Ichiro Takeuchi
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of Tc for cuprate, iron-based, and low-T c compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify >30 non-cuprate and non-iron-based oxides as candidate materials.
Sequential piezoresponse force microscopy and the ‘small-data’ problem npj Comput. Mater. (IF 8.941) Pub Date : 2018-06-21 Harsh Trivedi, Vladimir V. Shvartsman, Marco S. A. Medeiros, Robert C. Pullar, Doru C. Lupascu
The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets. This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be ‘small’ in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution.
Learning local, quenched disorder in plasticity and other crackling noise phenomena npj Comput. Mater. (IF 8.941) Pub Date : 2018-06-07 Stefanos Papanikolaou
When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution. While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g., stress/strain) timeseries in models of crackling noise. We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples.
Effect of ionic composition on thermal properties of energetic ionic liquids npj Comput. Mater. (IF 8.941) Pub Date : 2018-05-16 Chihyun Park, Minsu Han, Jinbo Kim, Woojae Lee, Eunkyoung Kim
A model to predict the effect of ionic composition on the thermal properties of energetic ionic liquids was developed by quantitative structure-property relationship modeling, which predicted the detonation velocity, pressure, and melting temperature of energetic ionic liquids. A hybrid approach was used to determine the optimal subset of descriptors by combining regression with the genetic algorithm as an optimization method. The model showed the high accuracy, reaching a correlation factor of R2 as 0.71, 0.73 and 0.68 for the correlation between the calculated detonation velocity, pressure and melting temperature against reported values. It was validated extensively and compared to the Kamlet–Jacobs equation. The effect of ion composition on the thermal properties of energetic ionic liquids could be quantitatively analyzed through the developed model, to give an insight for the design of new energetic ionic liquids.
A strategy to apply machine learning to small datasets in materials science npj Comput. Mater. (IF 8.941) Pub Date : 2018-05-14 Ying Zhang, Chen Ling
There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.
Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-25 Dhiren K. Pradhan, Shalini Kumari, Evgheni Strelcov, Dillip K. Pradhan, Ram S. Katiyar, Sergei V. Kalinin, Nouamane Laanait, Rama K. Vasudevan
We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition, with the voltage spectroscopy data being used as a proxy for the (unknown) microscopic state or thermodynamic parameters of materials. Given the discrete nature of the measurement points, we use Gaussian processes to reconstruct hysteresis loops in temperature and voltage space, and compare the results with the raw data and bulk dielectric spectroscopy measurements. The results indicate that the surface transition temperature is similar for all but one composition with respect to the bulk. Through clustering algorithms, we recreate the main features of the bulk diagram, and provide statistical confidence estimates for the reconstructed phase transition temperatures. We validate the method by using Gaussian processes to predict hysteresis loops for a given temperature for a composition unseen by the algorithm, and compare with measurements. These techniques can be used to map phase diagrams from functional materials in an automated fashion, and provide a method for uncertainty quantification and model selection.
Electrochemically driven conversion reaction in fluoride electrodes for energy storage devices npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-23 Chilin Li, Keyi Chen, Xuejun Zhou, Joachim Maier
Exploring electrochemically driven conversion reactions for the development of novel energy storage materials is an important topic as they can deliver higher energy densities than current Li-ion battery electrodes. Conversion-type fluorides promise particularly high energy densities by involving the light and small fluoride anion, and bond breaking can occur at relatively low Li activity (i.e., high cell voltage). Cells based on such electrodes may become competitors to other envisaged alternatives such as Li-sulfur or Li-air systems with their many unsolved thermodynamic and kinetic problems. Relevant conversion reactions are typically multiphase redox reactions characterized by nucleation and growth processes along with pronounced interfacial and mass transport phenomena. Hence significant overpotentials and nonequilibrium reaction pathways are involved. In this review, we summarize recent findings in terms of phase evolution phenomena and mechanistic features of (oxy)fluorides at different redox stages during the conversion process, enabled by advanced characterization technologies and simulation methods. It can be concluded that well-designed nanostructured architectures are helpful in mitigating kinetic problems such as the usually pronounced voltage hysteresis. In this context, doping and open-framework strategies are useful. By these tools, simple materials that are unable to allow for substantial Li nonstoichiometry (e.g., by Li-insertable channels) may be turned into electroactive materials.
Author Correction: Automated generation and ensemble-learned matching of X-ray absorption spectra npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-19 Chen Zheng, Kiran Mathew, Chi Chen, Yiming Chen, Hanmei Tang, Alan Dozier, Joshua J. Kas, Fernando D. Vila, John J. Rehr, Louis F. J. Piper, Kristin A. Persson, Shyue Ping Ong
Author Correction: Automated generation and ensemble-learned matching of X-ray absorption spectra Author Correction: Automated generation and ensemble-learned matching of X-ray absorption spectra, Published online: 19 April 2018; doi:10.1038/s41524-018-0080-0 Author Correction: Automated generation and ensemble-learned matching of X-ray absorption spectra
Ultra-low thermal conductivity of two-dimensional phononic crystals in the incoherent regime npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-16 Guofeng Xie, Zhifang Ju, Kuikui Zhou, Xiaolin Wei, Zhixin Guo, Yongqing Cai, Gang Zhang
Two-dimensional silicon phononic crystals have attracted extensive research interest for thermoelectric applications due to their reproducible low thermal conductivity and sufficiently good electrical properties. For thermoelectric devices in high-temperature environment, the coherent phonon interference is strongly suppressed; therefore phonon transport in the incoherent regime is critically important for manipulating their thermal conductivity. On the basis of perturbation theory, we present herein a novel phonon scattering process from the perspective of bond order imperfections in the surface skin of nanostructures. We incorporate this strongly frequency-dependent scattering rate into the phonon Boltzmann transport equation and reproduce the ultra low thermal conductivity of holey silicon nanostructures. We reveal that the remarkable reduction of thermal conductivity originates not only from the impediment of low-frequency phonons by normal boundary scattering, but also from the severe suppression of high-frequency phonons by surface bond order imperfections scattering. Our theory not only reveals the role of the holey surface on the phonon transport, but also provide a computation tool for thermal conductivity modification in nanostructures through surface engineering.
Improved phase field model of dislocation intersections npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-11 Songlin Zheng, Dongchang Zheng, Yong Ni, Linghui He
Revealing the long-range elastic interaction and short-range core reaction between intersecting dislocations is crucial to the understanding of dislocation-based strain hardening mechanisms in crystalline solids. Phase field model has shown great potential in modeling dislocation dynamics by both employing the continuum microelasticity theory to describe the elastic interactions and incorporating the γ-surface into the crystalline energy to enable the core reactions. Since the crystalline energy is approximately formulated by linear superposition of interplanar potential of each slip plane in the previous phase field model, it does not fully account for the reactions between dislocations gliding in intersecting slip planes. In this study, an improved phase field model of dislocation intersections is proposed through updating the crystalline energy by coupling the potential of two intersecting planes, and then applied to study the collinear interaction followed by comparison with the previous simulation result using discrete dislocation dynamics. Collinear annihilation captured only in the improved phase field model is found to strongly affect the junction formation and plastic flow in multislip systems. The results indicate that the improvement is essential for phase field model of dislocation intersections.
Spatial correlation of elastic heterogeneity tunes the deformation behavior of metallic glasses npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-06 Neng Wang, Jun Ding, Feng Yan, Mark Asta, Robert O. Ritchie, Lin Li
Metallic glasses (MGs) possess remarkably high strength but often display only minimal tensile ductility due to the formation of catastrophic shear bands. Purposely enhancing the inherent heterogeneity to promote distributed flow offers new possibilities in improving the ductility of monolithic MGs. Here, we report the effect of the spatial heterogeneity of elasticity, resulting from the inherently inhomogeneous amorphous structures, on the deformation behavior of MGs, specifically focusing on the ductility using multiscale modeling methods. A highly heterogeneous, Gaussian-type shear modulus distribution at the nanoscale is revealed by atomistic simulations in Cu64Zr36 MGs, in which the soft population of the distribution exhibits a marked propensity to undergo the inelastic shear transformation. By employing a mesoscale shear transformation zone dynamics model, we find that the organization of such nanometer-scale shear transformation events into shear-band patterns is dependent on the spatial heterogeneity of the local shear moduli. A critical spatial correlation length of elastic heterogeneity is identified for the simulated MGs to achieve the best tensile ductility, which is associated with a transition of shear-band formation mechanisms, from stress-dictated nucleation and growth to structure-dictated strain percolation, as well as a saturation of elastically soft sites participating in the plastic flow. This discovery is important for the fundamental understanding of the role of spatial heterogeneity in influencing the deformation behavior of MGs. We believe that this can facilitate the design and development of new ductile monolithic MGs by a process of tuning the inherent heterogeneity to achieve enhanced ductility in these high-strength metallic alloys.
Computational discovery of p-type transparent oxide semiconductors using hydrogen descriptor npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-03 Kanghoon Yim, Yong Youn, Miso Lee, Dongsun Yoo, Joohee Lee, Sung Haeng Cho, Seungwu Han
The ultimate transparent electronic devices require complementary and symmetrical pairs of n-type and p-type transparent semiconductors. While several n-type transparent oxide semiconductors like InGaZnO and ZnO are available and being used in consumer electronics, there are practically no p-type oxides that are comparable to the n-type counterpart in spite of tremendous efforts to discover them. Recently, high-throughput screening with the density functional theory calculations attempted to identify candidate p-type transparent oxides, but none of suggested materials was verified experimentally, implying need for a better theoretical predictor. Here, we propose a highly reliable and computationally efficient descriptor for p-type dopability—the hydrogen impurity energy. We show that the hydrogen descriptor can distinguish well-known p-type and n-type oxides. Using the hydrogen descriptor, we screen most binary oxides and a selected pool of ternary compounds that covers Sn2+-bearing and Cu1+-bearing oxides as well as oxychalcogenides. As a result, we suggest La2O2Te and CuLiO as promising p-type oxides.
Statistical variances of diffusional properties from ab initio molecular dynamics simulations npj Comput. Mater. (IF 8.941) Pub Date : 2018-04-03 Xingfeng He, Yizhou Zhu, Alexander Epstein, Yifei Mo
Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.
Design of high-strength refractory complex solid-solution alloys npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-28 Prashant Singh, Aayush Sharma, A. V. Smirnov, Mouhamad S. Diallo, Pratik K. Ray, Ganesh Balasubramanian, Duane D. Johnson
Nickel-based superalloys and near-equiatomic high-entropy alloys containing molybdenum are known for higher temperature strength and corrosion resistance. Yet, complex solid-solution alloys offer a huge design space to tune for optimal properties at slightly reduced entropy. For refractory Mo-W-Ta-Ti-Zr, we showcase KKR electronic structure methods via the coherent-potential approximation to identify alloys over five-dimensional design space with improved mechanical properties and necessary global (formation enthalpy) and local (short-range order) stability. Deformation is modeled with classical molecular dynamic simulations, validated from our first-principle data. We predict complex solid-solution alloys of improved stability with greatly enhanced modulus of elasticity (3× at 300 K) over near-equiatomic cases, as validated experimentally, and with higher moduli above 500 K over commercial alloys (2.3× at 2000 K). We also show that optimal complex solid-solution alloys are not described well by classical potentials due to critical electronic effects.
Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-26 Aiping Wang, Sanket Kadam, Hong Li, Siqi Shi, Yue Qi
A passivation layer called the solid electrolyte interphase (SEI) is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows Li+ transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions. The formation and growth mechanism of the nanometer thick SEI films are yet to be completely understood owing to their complex structure and lack of reliable in situ experimental techniques. Significant advances in computational methods have made it possible to predictively model the fundamentals of SEI. This review aims to give an overview of state-of-the-art modeling progress in the investigation of SEI films on the anodes, ranging from electronic structure calculations to mesoscale modeling, covering the thermodynamics and kinetics of electrolyte reduction reactions, SEI formation, modification through electrolyte design, correlation of SEI properties with battery performance, and the artificial SEI design. Multi-scale simulations have been summarized and compared with each other as well as with experiments. Computational details of the fundamental properties of SEI, such as electron tunneling, Li-ion transport, chemical/mechanical stability of the bulk SEI and electrode/(SEI/) electrolyte interfaces have been discussed. This review shows the potential of computational approaches in the deconvolution of SEI properties and design of artificial SEI. We believe that computational modeling can be integrated with experiments to complement each other and lead to a better understanding of the complex SEI for the development of a highly efficient battery in the future.
Bismuth and antimony-based oxyhalides and chalcohalides as potential optoelectronic materials npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-22 Zhao Ran, Xinjiang Wang, Yuwei Li, Dongwen Yang, Xin-Gang Zhao, Koushik Biswas, David J. Singh, Lijun Zhang
In the last decade the ns2 cations (e.g., Pb2+ and Sn2+)-based halides have emerged as one of the most exciting new classes of optoelectronic materials, as exemplified by for instance hybrid perovskite solar absorbers. These materials not only exhibit unprecedented performance in some cases, but they also appear to break new ground with their unexpected properties, such as extreme tolerance to defects. However, because of the relatively recent emergence of this class of materials, there remain many yet to be fully explored compounds. Here, we assess a series of bismuth/antimony oxyhalides and chalcohalides using consistent first principles methods to ascertain their properties and obtain trends. Based on these calculations, we identify a subset consisting of three types of compounds that may be promising as solar absorbers, transparent conductors, and radiation detectors. Their electronic structure, connection to the crystal geometry, and impact on band-edge dispersion and carrier effective mass are discussed.
Automated generation and ensemble-learned matching of X-ray absorption spectra npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-20 Chen Zheng, Kiran Mathew, Chi Chen, Yiming Chen, Hanmei Tang, Alan Dozier, Joshua J. Kas, Fernando D. Vila, John J. Rehr, Louis F. J. Piper, Kristin A. Persson, Shyue Ping Ong
X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.
An effective method to screen sodium-based layered materials for sodium ion batteries npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-20 Xu Zhang, Zihe Zhang, Sai Yao, An Chen, Xudong Zhao, Zhen Zhou
Due to the high cost and insufficient resource of lithium, sodium-ion batteries are widely investigated for large-scale applications. Typically, insertion-type materials possess better cyclic stability than alloy-type and conversion-type ones. Therefore, in this work, we proposed a facile and effective method to screen sodium-based layered materials based on Materials Project database as potential candidate insertion-type materials for sodium ion batteries. The obtained Na-based layered materials contains 38 kinds of space group, which reveals that the credibility of our screening approach would not be affected by the space group. Then, some important indexes of the representative materials, including the average voltage, volume change and sodium ion mobility, were further studied by means of density functional theory computations. Some materials with extremely low volume changes and Na diffusion barriers are promising candidates for sodium ion batteries. We believe that our classification algorithm could also be used to search for other alkali and multivalent ion-based layered materials, to accelerate the development of battery materials.
Insight into point defects and impurities in titanium from first principles npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-16 Sanjeev K. Nayak, Cain J. Hung, Vinit Sharma, S. Pamir Alpay, Avinash M. Dongare, William J. Brindley, Rainer J. Hebert
Titanium alloys find extensive use in the aerospace and biomedical industries due to a unique combination of strength, density, and corrosion resistance. Decades of mostly experimental research has led to a large body of knowledge of the processing-microstructure-properties linkages. But much of the existing understanding of point defects that play a significant role in the mechanical properties of titanium is based on semi-empirical rules. In this work, we present the results of a detailed self-consistent first-principles study that was developed to determine formation energies of intrinsic point defects including vacancies, self-interstitials, and extrinsic point defects, such as, interstitial and substitutional impurities/dopants. We find that most elements, regardless of size, prefer substitutional positions, but highly electronegative elements, such as C, N, O, F, S, and Cl, some of which are common impurities in Ti, occupy interstitial positions.
Active cell-matrix coupling regulates cellular force landscapes of cohesive epithelial monolayers npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-14 Tiankai Zhao, Yao Zhang, Qiong Wei, Xuechen Shi, Peng Zhao, Long-Qing Chen, Sulin Zhang
Epithelial cells can assemble into cohesive monolayers with rich morphologies on substrates due to competition between elastic, edge, and interfacial effects. Here we present a molecularly based thermodynamic model, integrating monolayer and substrate elasticity, and force-mediated focal adhesion formation, to elucidate the active biochemical regulation over the cellular force landscapes in cohesive epithelial monolayers, corroborated by microscopy and immunofluorescence studies. The predicted extracellular traction and intercellular tension are both monolayer size and substrate stiffness dependent, suggestive of cross-talks between intercellular and extracellular activities. Our model sets a firm ground toward a versatile computational framework to uncover the molecular origins of morphogenesis and disease in multicellular epithelia.
Efficient first-principles prediction of solid stability: Towards chemical accuracy npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-09 Yubo Zhang, Daniil A. Kitchaev, Julia Yang, Tina Chen, Stephen T. Dacek, Rafael A. Sarmiento-Pérez, Maguel A. L. Marques, Haowei Peng, Gerbrand Ceder, John P. Perdew, Jianwei Sun
The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.
Displacement Current in Domain Walls of Bismuth Ferrite npj Comput. Mater. (IF 8.941) Pub Date : 2018-03-08 Sergey Prosandeev, Yurong Yang, Charles Paillard, L. Bellaiche
In 1861, Maxwell conceived the idea of the displacement current, which then made laws of electrodynamics more complete and also resulted in the realization of devices exploiting such displacement current. Interestingly, it is presently unknown if such displacement current can result in large intrinsic ac current in ferroic systems possessing domains, despite the flurry of recent activities that have been devoted to domains and their corresponding conductivity in these compounds. Here, we report first-principles-based atomistic simulations that predict that the transverse (polarization-related) displacement currents of 71° and 109° domains in the prototypical BiFeO3 multiferroic material are significant at the walls of such domains and in the GHz regime, and, in fact, result in currents that are at least of the same order of magnitude than previously reported dc currents (that are likely extrinsic in nature and due to electrons). Such large, localized and intrinsic ac currents are found to originate from low-frequency vibrations at the domain walls, and may open the door to the design of novel devices functioning in the GHz or THz range and in which currents would be confined within the domain wall.
Temperature-dependent phonon spectra of magnetic random solid solutions npj Comput. Mater. (IF 8.941) Pub Date : 2018-02-28 Yuji Ikeda, Fritz Körmann, Biswanath Dutta, Abel Carreras, Atsuto Seko, Jörg Neugebauer, Isao Tanaka
A first-principles-based computational tool for simulating phonons of magnetic random solid solutions including thermal magnetic fluctuations is developed. The method takes fluctuations of force constants due to magnetic excitations as well as due to chemical disorder into account. The developed approach correctly predicts the experimentally observed unusual phonon hardening of a transverse acoustic mode in Fe–Pd an Fe–Pt Invar alloys with increasing temperature. This peculiar behavior, which cannot be explained within a conventional harmonic picture, turns out to be a consequence of thermal magnetic fluctuations. The proposed methodology can be straightforwardly applied to a wide range of materials to reveal new insights into physical behaviors and to design materials through computation, which were not accessible so far.
Nanotwinned and hierarchical nanotwinned metals: a review of experimental, computational and theoretical efforts npj Comput. Mater. (IF 8.941) Pub Date : 2018-02-05 Ligang Sun, Xiaoqiao He, Jian Lu
The recent studies on nanotwinned (NT) and hierarchical nanotwinned (HNT) face-centered cubic (FCC) metals are presented in this review. The HNT structures have been supposed as a kind of novel structure to bring about higher strength/ductility than NT counterparts in crystalline materials. We primarily focus on the recent developments of the experimental, atomistic and theoretical studies on the NT and HNT structures in the metallic materials. Some advanced bottom-up and top-down techniques for the fabrication of NT and HNT structures are introduced. The deformation induced HNT structures are available by virtue of severe plastic deformation (SPD) based techniques while the synthesis of growth HNT structures is so far almost unavailable. In addition, some representative molecular dynamics (MD) studies on the NT and HNT FCC metals unveil that the nanoscale effects such as twin spacing, grain size and plastic anisotropy greatly alter the performance of NT and HNT metals. The HNT structures may initiate unique phenomena in comparison with the NT ones. Furthermore, based on the phenomena and mechanisms revealed by experimental and MD simulation observations, a series of theoretical models have been proposed. They are effective to describe the mechanical behaviors of NT and HNT metals within the applicable scope. So far the development of manufacturing technologies of HNT structures, as well as the studies on the effects of HNT structures on the properties of metals are still in its infancy. Further exploration is required to promote the design of advanced materials.
Data analytics and parallel-coordinate materials property charts npj Comput. Mater. (IF 8.941) Pub Date : 2018-01-29 Jeffrey M. Rickman
It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes.
Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling npj Comput. Mater. (IF 8.941) Pub Date : 2018-01-25 Tetsuro Ueno, Hideitsu Hino, Ai Hashimoto, Yasuo Takeichi, Masahiro Sawada, Kanta Ono
Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.
Accelerating evaluation of converged lattice thermal conductivity npj Comput. Mater. (IF 8.941) Pub Date : 2018-01-22 Guangzhao Qin, Ming Hu
High-throughput computational materials design is an emerging area in materials science, which is based on the fast evaluation of physical-related properties. The lattice thermal conductivity (κ) is a key property of materials for enormous implications. However, the high-throughput evaluation of κ remains a challenge due to the large resources costs and time-consuming procedures. In this paper, we propose a concise strategy to efficiently accelerate the evaluation process of obtaining accurate and converged κ. The strategy is in the framework of phonon Boltzmann transport equation (BTE) coupled with first-principles calculations. Based on the analysis of harmonic interatomic force constants (IFCs), the large enough cutoff radius (rcutoff), a critical parameter involved in calculating the anharmonic IFCs, can be directly determined to get satisfactory results. Moreover, we find a simple way to largely (~10 times) accelerate the computations by fast reconstructing the anharmonic IFCs in the convergence test of κ with respect to the rcutof, which finally confirms the chosen rcutoff is appropriate. Two-dimensional graphene and phosphorene along with bulk SnSe are presented to validate our approach, and the long-debate divergence problem of thermal conductivity in low-dimensional systems is studied. The quantitative strategy proposed herein can be a good candidate for fast evaluating the reliable κ and thus provides useful tool for high-throughput materials screening and design with targeted thermal transport properties.
Theoretical potential for low energy consumption phase change memory utilizing electrostatically-induced structural phase transitions in 2D materials npj Comput. Mater. (IF 8.941) Pub Date : 2018-01-17 Daniel A. Rehn, Yao Li, Eric Pop, Evan J. Reed
Structural phase-change materials are of great importance for applications in information storage devices. Thermally driven structural phase transitions are employed in phase-change memory to achieve lower programming voltages and potentially lower energy consumption than mainstream nonvolatile memory technologies. However, the waste heat generated by such thermal mechanisms is often not optimized, and could present a limiting factor to widespread use. The potential for electrostatically driven structural phase transitions has recently been predicted and subsequently reported in some two-dimensional materials, providing an athermal mechanism to dynamically control properties of these materials in a nonvolatile fashion while achieving potentially lower energy consumption. In this work, we employ DFT-based calculations to make theoretical comparisons of the energy required to drive electrostatically-induced and thermally-induced phase transitions. Determining theoretical limits in monolayer MoTe2 and thin films of Ge2Sb2Te5, we find that the energy consumption per unit volume of the electrostatically driven phase transition in monolayer MoTe2 at room temperature is 9% of the adiabatic lower limit of the thermally driven phase transition in Ge2Sb2Te5. Furthermore, experimentally reported phase change energy consumption of Ge2Sb2Te5 is 100–10,000 times larger than the adiabatic lower limit due to waste heat flow out of the material, leaving the possibility for energy consumption in monolayer MoTe2-based devices to be orders of magnitude smaller than Ge2Sb2Te5-based devices.
Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study npj Comput. Mater. (IF 8.941) Pub Date : 2018-01-10 Won-Mi Choi, Yong Hee Jo, Seok Su Sohn, Sunghak Lee, Byeong-Joo Lee
Although high-entropy alloys (HEAs) are attracting interest, the physical metallurgical mechanisms related to their properties have mostly not been clarified, and this limits wider industrial applications, in addition to the high alloy costs. We clarify the physical metallurgical reasons for the materials phenomena (sluggish diffusion and micro-twining at cryogenic temperatures) and investigate the effect of individual elements on solid solution hardening for the equiatomic CoCrFeMnNi HEA based on atomistic simulations (Monte Carlo, molecular dynamics and molecular statics). A significant number of stable vacant lattice sites with high migration energy barriers exists and is thought to cause the sluggish diffusion. We predict that the hexagonal close-packed (hcp) structure is more stable than the face-centered cubic (fcc) structure at 0 K, which we propose as the fundamental reason for the micro-twinning at cryogenic temperatures. The alloying effect on the critical resolved shear stress (CRSS) is well predicted by the atomistic simulation, used for a design of non-equiatomic fcc HEAs with improved strength, and is experimentally verified. This study demonstrates the applicability of the proposed atomistic approach combined with a thermodynamic calculation technique to a computational design of advanced HEAs.
Machine learning in materials informatics: recent applications and prospects npj Comput. Mater. (IF 8.941) Pub Date : 2017-12-13 Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, Arun Mannodi-Kanakkithodi, Chiho Kim
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.
Virtual screening of inorganic materials synthesis parameters with deep learning npj Comput. Mater. (IF 8.941) Pub Date : 2017-12-01 Edward Kim, Kevin Huang, Stefanie Jegelka, Elsa Olivetti
Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO3 synthesis parameter sets, propose driving factors for brookite TiO2 formation, and identify correlations between alkali-ion intercalation and MnO2 polymorph selection.
First-principles screening of structural properties of intermetallic compounds on martensitic transformation npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-24 Joohwi Lee, Yuji Ikeda, Isao Tanaka
Martensitic transformation with good structural compatibility between parent and martensitic phases are required for shape memory alloys (SMAs) in terms of functional stability. In this study, first-principles-based materials screening is systematically performed to investigate the intermetallic compounds with the martensitic phases by focusing on energetic and dynamical stabilities as well as structural compatibility with the parent phase. The B2, D03, and L21 crystal structures are considered as the parent phases, and the 2H and 6M structures are considered as the martensitic phases. In total, 3384 binary and 3243 ternary alloys with stoichiometric composition ratios are investigated. It is found that 187 alloys survive after the screening. Some of the surviving alloys are constituted by the chemical elements already widely used in SMAs, but other various metallic elements are also found in the surviving alloys. The energetic stability of the surviving alloys is further analyzed by comparison with the data in Materials Project Database (MPD) to examine the alloys whose martensitic structures may cause further phase separation or transition to the other structures.
First-principles prediction of high-entropy-alloy stability npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-21 Rui Feng, Peter K. Liaw, Michael C. Gao, Michael Widom
High entropy alloys (HEAs) are multicomponent compounds whose high configurational entropy allows them to solidify into a single phase, with a simple crystal lattice structure. Some HEAs exhibit desirable properties, such as high specific strength, ductility, and corrosion resistance, while challenging the scientist to make confident predictions in the face of multiple competing phases. We demonstrate phase stability in the multicomponent alloy system of Cr–Mo–Nb–V, for which some of its binary subsystems are subject to phase separation and complex intermetallic-phase formation. Our first-principles calculation of free energy predicts that the configurational entropy stabilizes a single body-centered cubic (BCC) phase from T = 1700 K up to melting, while precipitation of a complex intermetallic is favored at lower temperatures. We form the compound experimentally and confirm that it develops as a single BCC phase from the melt, but that it transforms reversibly at lower temperatures.
Rethinking phonons: The issue of disorder npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-16 Hamid Reza Seyf, Luke Yates, Thomas L. Bougher, Samuel Graham, Baratunde A. Cola, Theeradetch Detchprohm, Mi-Hee Ji, Jeomoh Kim, Russell Dupuis, Wei Lv, Asegun Henry
Current understanding of phonons treats them as plane waves/quasi-particles of atomic vibration that propagate and scatter. The problem is that conceptually, when any level of disorder is introduced, whether compositional or structural, the character of vibrational modes in solids changes, yet nearly all theoretical treatments continue to assume phonons are still waves. For example, the phonon contributions to alloy thermal conductivity (TC) rely on this assumption and are most often computed from the virtual crystal approximation (VCA). Good agreement is obtained in some cases, but there are many instances where it fails—both quantitatively and qualitatively. Here, we show that the conventional theory and understanding of phonons requires revision, because the critical assumption that all phonons/normal modes resemble plane waves with well-defined velocities is no longer valid when disorder is introduced. Here we show, surprisingly, that the character of phonons changes dramatically within the first few percent of impurity concentration, beyond which phonons more closely resemble the modes found in amorphous materials. We then utilize a different theory that can treat modes with any character and experimentally confirm its new insights.
Theory, simulations and the design of functionalized nanoparticles for biomedical applications: A Soft Matter Perspective npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-13 Stefano Angioletti-Uberti
Functionalised nanoparticles for biomedical applications represents an incredibly exciting and rapidly growing field of research. Considering the complexity of the nano–bio interface, an important question is to what extent can theory and simulations be used to study these systems in a realistic, meaningful way. In this review, we will argue for a positive answer to this question. Approaching the issue from a “Soft Matter” perspective, we will consider those properties of functionalised nanoparticles that can be captured within a classical description. We will thus not concentrate on optical and electronic properties, but rather on the way nanoparticles’ interactions with the biological environment can be tuned by functionalising their surface and exploited in different contexts relevant to applications. In particular, we wish to provide a critical overview of theoretical and computational coarse-grained models, developed to describe these interactions and present to the readers some of the latest results in this fascinating area of research.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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