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Data-driven models for ground and excited states for Single Atoms on Ceria npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-18 Julian Geiger, Albert Sabadell-Rendón, Nathan Daelman, Núria López
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Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-17 Kan Hatakeyama-Sato, Momoka Umeki, Hiroki Adachi, Naoaki Kuwata, Gen Hasegawa, Kenichi Oyaizu
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Ultrafast laser-driven topological spin textures on a 2D magnet npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-17 Mara Strungaru, Mathias Augustin, Elton J. G. Santos
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Symmetric or asymmetric glide resistance to twinning disconnection? npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-16 Mingyu Gong, Houyu Ma, Kunming Yang, Yue Liu, Jian-Feng Nie, Jian Wang
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Excitation and detection of coherent sub-terahertz magnons in ferromagnetic and antiferromagnetic heterostructures npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-11 Shihao Zhuang, Jia-Mian Hu
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Compressing local atomic neighbourhood descriptors npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-11 James P. Darby, James R. Kermode, Gábor Csányi
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Missed ferroelectricity in methylammonium lead iodide npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-10 Wen-Yi Tong, Jin-Zhu Zhao, Philippe Ghosez
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Generative design of stable semiconductor materials using deep learning and density functional theory npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-04 Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera, Jianjun Hu
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Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloys npj Comput. Mater. (IF 12.256) Pub Date : 2022-08-02 Shikha Saini, Joakim Halldin Stenlid, Frank Abild-Pedersen
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Hybrid magnetorheological elastomers enable versatile soft actuators npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-28 Miguel Angel Moreno-Mateos, Mokarram Hossain, Paul Steinmann, Daniel Garcia-Gonzalez
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Effect of exchange-correlation functionals on the estimation of migration barriers in battery materials npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-22 Reshma Devi, Baltej Singh, Pieremanuele Canepa, Gopalakrishnan Sai Gautam
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Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-22 Marc Duquesnoy, Teo Lombardo, Fernando Caro, Florent Haudiquez, Alain C. Ngandjong, Jiahui Xu, Hassan Oularbi, Alejandro A. Franco
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LaBr2 bilayer multiferroic moiré superlattice with robust magnetoelectric coupling and magnetic bimerons npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-22 Wei Sun, Wenxuan Wang, Hang Li, Xiaoning Li, Zheyin Yu, Ying Bai, Guangbiao Zhang, Zhenxiang Cheng
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Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-22 Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G. Anand, Reinhard J. Maurer, Christoph Ortner, James R. Kermode
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A classical equation that accounts for observations of non-Arrhenius and cryogenic grain boundary migration npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-21 Eric R. Homer, Oliver K. Johnson, Darcey Britton, James E. Patterson, Eric T. Sevy, Gregory B. Thompson
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Computational screening of materials with extreme gap deformation potentials npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-20 Pedro Borlido, Jonathan Schmidt, Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques
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Endless Dirac nodal lines in kagome-metal Ni3In2S2 npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-19 Tiantian Zhang, T. Yilmaz, E. Vescovo, H. X. Li, R. G. Moore, H. N. Lee, H. Miao, S. Murakami, M. A. McGuire
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Third-order topological insulators with wallpaper fermions in Tl4PbTe3 and Tl4SnTe3 npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-18 Ning Mao, Hao Wang, Ying Dai, Baibiao Huang, Chengwang Niu
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Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-18 D. Beniwal, P. Singh, S. Gupta, M. J. Kramer, D. D. Johnson, P. K. Ray
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On the role of the microstructure in the deformation of porous solids npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-18 Sansit Patnaik, Mehdi Jokar, Wei Ding, Fabio Semperlotti
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Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-15 Tim Hsu, Tuan Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, S. Roger Qiu, Xiao Chen, Brandon C. Wood
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Superconductivity in antiperovskites npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-13 Noah Hoffmann, Tiago F. T. Cerqueira, Jonathan Schmidt, Miguel A. L. Marques
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Classifying handedness in chiral nanomaterials using label error robust deep learning npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-12 C. K. Groschner, Alexander J. Pattison, Assaf Ben-Moshe, A. Paul Alivisatos, Wolfgang Theis, M. C. Scott
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Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-11 Minseok Jeong, Joonyoung F. Joung, Jinhyo Hwang, Minhi Han, Chang Woo Koh, Dong Hoon Choi, Sungnam Park
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Identification of high-dielectric constant compounds from statistical design npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-07 Abhijith Gopakumar, Koushik Pal, Chris Wolverton
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Photoinduced small electron polarons generation and recombination in hematite npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-07 Cheng Cheng, Yonghao Zhu, Zhaohui Zhou, Run Long, Wei-Hai Fang
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Effect of spin-orbit coupling on the high harmonics from the topological Dirac semimetal Na3Bi npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-06 Nicolas Tancogne-Dejean, Florian G. Eich, Angel Rubio
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A non-ideal solution theory for the mechanics and electrochemistry of charged membranes npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-05 Alain Boldini, Maurizio Porfiri
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Photovoltaphores: pharmacophore models for identifying metal-free dyes for dye-sensitized solar cells npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-04 Hadar Binyamin, Hanoch Senderowitz
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Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-04 Joshua Ojih, Mohammed Al-Fahdi, Alejandro David Rodriguez, Kamal Choudhary, Ming Hu
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Inclusion of infrared dielectric screening in the GW method from polaron energies to charge mobilities npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-01 Paolo Umari
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Non-synchronous bulk photovoltaic effect in two-dimensional interlayer-sliding ferroelectrics npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-01 Rui-Chun Xiao, Yang Gao, Hua Jiang, Wei Gan, Changjin Zhang, Hui Li
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Viscosity in water from first-principles and deep-neural-network simulations npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-01 Cesare Malosso, Linfeng Zhang, Roberto Car, Stefano Baroni, Davide Tisi
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XGBoost model for electrocaloric temperature change prediction in ceramics npj Comput. Mater. (IF 12.256) Pub Date : 2022-07-01 Jie Gong, Sharon Chu, Rohan K. Mehta, Alan J. H. McGaughey
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Bidirectional mechanical switching window in ferroelectric thin films predicted by first-principle-based simulations npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-29 Jianyi Liu, Weijin Chen, Mengjun Wu, Fei Sun, Xiang Huang, Yue Zheng
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Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-28 Saba Kharabadze, Aidan Thorn, Ekaterina A. Koulakova, Aleksey N. Kolmogorov
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Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-28 Xun Gong, Nicholas Renegar, Retsef Levi, Michael S. Strano
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Approaches for handling high-dimensional cluster expansions of ionic systems npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-27 Julia H. Yang, Tina Chen, Luis Barroso-Luque, Zinab Jadidi, Gerbrand Ceder
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Cellular automaton simulation and experimental validation of eutectic transformation during solidification of Al-Si alloys npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-27 Cheng Gu, Michael P. Moodispaw, Alan A. Luo
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High-throughput computation and structure prototype analysis for two-dimensional ferromagnetic materials npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-23 Zhen-Xiong Shen, Chuanxun Su, Lixin He
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Resonant tunneling in disordered borophene nanoribbons with line defects npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-23 Pei-Jia Hu, Si-Xian Wang, Xiao-Feng Chen, Zeng-Ren Liang, Tie-Feng Fang, Ai-Min Guo, Hui Xu, Qing-Feng Sun
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Dynamical phase-field model of coupled electronic and structural processes npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-22 Tiannan Yang, Long-Qing Chen
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Prediction of protected band edge states and dielectric tunable quasiparticle and excitonic properties of monolayer MoSi2N4 npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-15 Yabei Wu, Zhao Tang, Weiyi Xia, Weiwei Gao, Fanhao Jia, Yubo Zhang, Wenguang Zhu, Wenqing Zhang, Peihong Zhang
The electronic structure of two-dimensional (2D) materials are inherently prone to environmental perturbations, which may pose significant challenges to their applications in electronic or optoelectronic devices. A 2D material couples with its environment through two mechanisms: local chemical coupling and nonlocal dielectric screening effects. The local chemical coupling is often difficult to predict
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Anisotropic Dzyaloshinskii-Moriya interaction protected by D2d crystal symmetry in two-dimensional ternary compounds npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-08 Yonglong Ga, Qirui Cui, Yingmei Zhu, Dongxing Yu, Liming Wang, Jinghua Liang, Hongxin Yang
Magnetic skyrmions, topologically protected chiral spin swirling quasiparticles, have attracted great attention in fundamental physics and applications. Recently, the discovery of two-dimensional (2D) van der Waals (vdW) magnets have aroused great interest due to their appealing physical properties. Moreover, both experimental and theoretical works have revealed that isotropic Dzyaloshinskii–Moriya
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Machine learning in concrete science: applications, challenges, and best practices npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-06 Zhanzhao Li, Jinyoung Yoon, Rui Zhang, Farshad Rajabipour, Wil V. Srubar III, Ismaila Dabo, Aleksandra Radlińska
Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated
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Machine learning for exploring small polaron configurational space npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-06 Viktor C. Birschitzky, Florian Ellinger, Ulrike Diebold, Michele Reticcioli, Cesare Franchini
Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining small polarons’ spatial distributions is essential to understand materials properties and functionalities. However, the required exploration of the configurational space is computationally demanding when using first principles methods
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Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-06 Lichao Fang, Lin Cheng, Jennifer A. Glerum, Jennifer Bennett, Jian Cao, Gregory J. Wagner
In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent
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Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-03 Giovanni Trezza, Luca Bergamasco, Matteo Fasano, Eliodoro Chiavazzo
We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO2 and H2O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations
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AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-03 Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara
The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained
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Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential npj Comput. Mater. (IF 12.256) Pub Date : 2022-06-01 Shuang Han, Giovanni Barcaro, Alessandro Fortunelli, Steen Lysgaard, Tejs Vegge, Heine Anton Hansen
The structural stability of nanoalloys is a challenging research subject due to the complexity of size, shape, composition, and chemical ordering. The genetic algorithm is a popular global optimization method that can efficiently search for the ground-state nanoalloy structure. However, the algorithm suffers from three significant limitations: the efficiency and accuracy of the energy evaluator and
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Multifunctional two-dimensional van der Waals Janus magnet Cr-based dichalcogenide halides npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-26 Yusheng Hou, Feng Xue, Liang Qiu, Zhe Wang, Ruqian Wu
Two-dimensional van der Waals Janus materials and their heterostructures offer fertile platforms for designing fascinating functionalities. Here, by means of systematic first-principles studies on van der Waals Janus monolayer Cr-based dichalcogenide halides CrYX (Y = S, Se, Te; X = Cl, Br, I), we find that CrSX (X = Cl, Br, I) are the very desirable high TC ferromagnetic semiconductors with an out-of-plane
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In-silico synthesis of lowest-pressure high-Tc ternary superhydrides npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-25 Roman Lucrezi, Simone Di Cataldo, Wolfgang von der Linden, Lilia Boeri, Christoph Heil
We report the theoretical prediction of two high-performing hydride superconductors BaSiH8 and SrSiH8. They are thermodynamically stable above pressures of 130 and 174 GPa, respectively, and metastable below that. Employing anharmonic phonon calculations, we determine the minimum pressures of dynamical stability to be around 3 GPa for BaSiH8 and 27 GPa for SrSiH8, and using the fully anisotropic Migdal-Eliashberg
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Installing a molecular truss beam stabilizes MOF structures npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-24 Hong Ki Kim, Jong-Yeong Jung, Gyumin Kang, Mu-Hyun Baik, Eun-Young Choi
Enhancing the stability and durability of metal-organic frameworks (MOFs) is vital for practical applications because many promising MOF materials suffer from phase transitions and/or structural decompositions with humidity being a particularly damaging condition. In mechanical engineering, the frame of buildings and furniture can be stabilized significantly by installing a truss beam. Employing the
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Coexisting charge density wave and ferromagnetic instabilities in monolayer InSe npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-24 Evgeny A. Stepanov, Viktor Harkov, Malte Rösner, Alexander I. Lichtenstein, Mikhail I. Katsnelson, Alexander N. Rudenko
Recently fabricated InSe monolayers exhibit remarkable characteristics that indicate the potential of this material to host a number of many-body phenomena. In this work, we systematically describe collective electronic effects in hole-doped InSe monolayers using advanced many-body techniques. To this end, we derive a realistic electronic-structure model from first principles that takes into account
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Calibration after bootstrap for accurate uncertainty quantification in regression models npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-20 Glenn Palmer, Siqi Du, Alexander Politowicz, Joshua Paul Emory, Xiyu Yang, Anupraas Gautam, Grishma Gupta, Zhelong Li, Ryan Jacobs, Dane Morgan
Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate
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Machine learning sparse tight-binding parameters for defects npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-20 Christoph Schattauer, Milica Todorović, Kunal Ghosh, Patrick Rinke, Florian Libisch
We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy
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Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-18 Chuqiao Shi, Michael C. Cao, Sarah M. Rehn, Sang-Hoon Bae, Jeehwan Kim, Matthew R. Jones, David A. Muller, Yimo Han
Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area. Here, we demonstrate a rapid and semi-automated unsupervised machine
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Robust and tunable Weyl phases by coherent infrared phonons in ZrTe5 npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-17 Niraj Aryal, Xilian Jin, Qiang Li, Mengkun Liu, A. M. Tsvelik, Weiguo Yin
Ultrafast control of structural and electronic properties of various quantum materials has recently sparked great interest. In particular, photoinduced switching between distinct topological phases has been considered a promising route to realize quantum computers. Here we use first-principles and effective Hamiltonian methods to show that in ZrTe5, lattice distortions corresponding to all three types
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High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-17 Andrew S. Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G. Truhlar, Kristin A. Persson, Justin M. Notestein, Randall Q. Snurr
With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient
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Abnormal nonlinear optical responses on the surface of topological materials npj Comput. Mater. (IF 12.256) Pub Date : 2022-05-16 Haowei Xu, Hua Wang, Ju Li
The nonlinear optical (NLO) responses of topological materials are under active research. Most previous works studied the surface and bulk NLO responses separately. Here we develop a generic Green’s function framework to investigate the surface and bulk NLO responses together. We reveal that the topological surface can behave disparately from the bulk under light illumination. Remarkably, the photocurrents