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  • 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.

    更新日期:2018-07-20
  • 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.

    更新日期:2018-07-19
  • 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.

    更新日期:2018-07-16
  • 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.

    更新日期:2018-07-14
  • 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.

    更新日期:2018-07-12
  • 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

    更新日期:2018-07-09
  • 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.

    更新日期:2018-07-08
  • 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.

    更新日期:2018-07-01
  • 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.

    更新日期:2018-07-01
  • 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.

    更新日期:2018-06-22
  • 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.

    更新日期:2018-06-07
  • 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.

    更新日期:2018-05-16
  • 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.

    更新日期:2018-05-14
  • 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.

    更新日期:2018-04-25
  • 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.

    更新日期:2018-04-23
  • 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

    更新日期:2018-04-19
  • 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.

    更新日期:2018-04-16
  • 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.

    更新日期:2018-04-11
  • 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.

    更新日期:2018-04-07
  • 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.

    更新日期:2018-04-03
  • 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.

    更新日期:2018-04-03
  • 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.

    更新日期:2018-03-28
  • 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.

    更新日期:2018-03-26
  • 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.

    更新日期:2018-03-22
  • 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.

    更新日期:2018-03-20
  • 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.

    更新日期:2018-03-20
  • 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.

    更新日期:2018-03-16
  • 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.

    更新日期:2018-03-15
  • 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.

    更新日期:2018-03-11
  • 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.

    更新日期:2018-03-08
  • 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.

    更新日期:2018-02-28
  • 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.

    更新日期:2018-02-05
  • 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.

    更新日期:2018-01-29
  • 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.

    更新日期:2018-01-25
  • 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.

    更新日期:2018-01-22
  • 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.

    更新日期:2018-01-17
  • 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.

    更新日期:2018-01-10
  • 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.

    更新日期:2017-12-14
  • 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.

    更新日期:2017-12-14
  • 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.

    更新日期:2017-12-14
  • 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.

    更新日期:2017-12-14
  • 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.

    更新日期:2017-12-14
  • 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.

    更新日期:2017-12-14
  • Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe
    npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-10
    Anuj Goyal, Prashun Gorai, Eric S. Toberer, Vladan Stevanović

    Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe, Published online: 10 November 2017; doi:10.1038/s41524-017-0054-7 Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe

    更新日期:2017-12-14
  • Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques
    npj Comput. Mater. (IF 8.941) Pub Date : 2017-11-08
    Giorgos Borboudakis, Taxiarchis Stergiannakos, Maria Frysali, Emmanuel Klontzas, Ioannis Tsamardinos, George E. Froudakis

    Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques, Published online: 08 November 2017; doi:10.1038/s41524-017-0051-x Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques

    更新日期:2017-12-14
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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