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| 1 | A general-purpose machine learning framework for predicting properties of inorganic materials显示文摘A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability. | Logan Ward Ankit Agrawal Alok Choudhary Christopher Wolverton | 2016 | npj Computational Materials2016，，1： | 83 |
| 2 | The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies显示文摘The Open Quantum Materials Database(OQMD)is a high-throughput database currently consisting of nearly 300,000 density functional theory(DFT)total energy calculations of compounds from the Inorganic Crystal Structure Database(ICSD)and decorations of commonly occurring crystal structures.To maximise the impact of these data,the entire database is being made available,without restrictions,at www.oqmd.org/download.In this paper,we outline the structure and contents of the database,and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds.This represents the largest comparison between DFT and experimental formation energies to date.The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom.In order to estimate how much error to attribute to the DFT calculations,we also examine deviation between different experimental measurements themselves where multiple sources are available,and find a surprisingly large mean absolute error of 0.082 eV/atom.Hence,we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties.Finally,we evaluate the stability of compounds in the OQMD(including compounds obtained from the ICSD as well as hypothetical structures),which allows us to predict the existence of~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery,based on historical data available within the ICSD. | Scott Kirklin James E Saal Bryce Meredig Alex Thompson Jeff W Doak Muratahan Aykol Stephan Rühl Chris Wolverton | 2015 | npj Computational Materials2015，，1： | 62 |
| 3 | Recent advances and applications of machine learning in solidstate materials science显示文摘One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.At present,we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.We provide a comprehensive overview and analysis of the most recent research in this topic.As a starting point,we introduce machine learning principles,algorithms,descriptors,and databases in materials science.We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure.Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning.We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications.Two major questions are always the interpretability of and the physical understanding gained from machine learning models.We consider therefore the different facets of interpretability and their importance in materials science.Finally,we propose solutions and future research paths for various challenges in computational materials science. | Jonathan Schmidt Mário R.G.Marques Silvana Botti Miguel A.L.Marques | 2019 | npj Computational Materials2019，，1： | 49 |
| 4 | Machine learning in materials informatics:recent applications and prospects显示文摘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. | Rampi Ramprasad Rohit Batra Ghanshyam Pilania Arun Mannodi-Kanakkithodi Chiho Kim | 2017 | npj Computational Materials2017，，1： | 40 |
| 5 | Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design显示文摘One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical.We review how methods from the information sciences enable us to accelerate the search and discovery of new materials.In particular,active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations.The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data.We discuss several utility functions and demonstrate their use in materials science applications,impacting both experimental and computational research.We summarize by indicating generalizations to multiple properties and multifidelity data,and identify challenges,future directions and opportunities in the emerging field of materials informatics. | Turab Lookman Prasanna V.Balachandran Dezhen Xue Ruihao Yuan | 2019 | npj Computational Materials2019，，1： | 28 |
| 6 | A strategy to apply machine learning to small datasets in materials science显示文摘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. | Ying Zhang Chen Ling | 2018 | npj Computational Materials2018，，1： | 25 |
| 7 | On the tuning of electrical and thermal transport in thermoelectrics: an integrated theory–experiment perspective显示文摘During the last two decades,we have witnessed great progress in research on thermoelectrics.There are two primary focuses.One is the fundamental understanding of electrical and thermal transport,enabled by the interplay of theory and experiment;the other is the substantial enhancement of the performance of various thermoelectric materials,through synergistic optimisation of those intercorrelated transport parameters.Here we review some of the successful strategies for tuning electrical and thermal transport.For electrical transport,we start from the classical but still very active strategy of tuning band degeneracy(or band convergence),then discuss the engineering of carrier scattering,and finally address the concept of conduction channels and conductive networks that emerge in complex thermoelectric materials.For thermal transport,we summarise the approaches for studying thermal transport based on phonon–phonon interactions valid for conventional solids,as well as some quantitative efforts for nanostructures.We also discuss the thermal transport in complex materials with chemical-bond hierarchy,in which a portion of the atoms(or subunits)are weakly bonded to the rest of the structure,leading to an intrinsic manifestation of part-crystalline part-liquid state at elevated temperatures.In this review,we provide a summary of achievements made in recent studies of thermoelectric transport properties,and demonstrate how they have led to improvements in thermoelectric performance by the integration of modern theory and experiment,and point out some challenges and possible directions. | Jiong Yang Lili Xi Wujie Qiu Lihua Wu Xun Shi Lidong Chen Jihui Yang Wenqing Zhang Ctirad Uher David J Singh | 2016 | npj Computational Materials2016，，1： | 24 |
| 8 | The ReaxFF reactive force-field: development, applications and future directions显示文摘The reactive force-field(ReaxFF)interatomic potential is a powerful computational tool for exploring,developing and optimizing material properties.Methods based on the principles of quantum mechanics(QM),while offering valuable theoretical guidance at the electronic level,are often too computationally intense for simulations that consider the full dynamic evolution of a system.Alternatively,empirical interatomic potentials that are based on classical principles require significantly fewer computational resources,which enables simulations to better describe dynamic processes over longer timeframes and on larger scales.Such methods,however,typically require a predefined connectivity between atoms,precluding simulations that involve reactive events.The ReaxFF method was developed to help bridge this gap.Approaching the gap from the classical side,ReaxFF casts the empirical interatomic potential within a bond-order formalism,thus implicitly describing chemical bonding without expensive QM calculations.This article provides an overview of the development,application,and future directions of the ReaxFF method. | Thomas P Senftle Sungwook Hong Md Mahbubul Islam Sudhir B Kylasa Yuanxia Zheng Yun Kyung Shin Chad Junkermeier Roman Engel-Herbert Michael J Janik Hasan Metin Aktulga Toon Verstraelen Ananth Grama Adri CT van Duin | 2016 | npj Computational Materials2016，，1： | 22 |
| 9 | Machine learning modeling of superconducting critical temperature显示文摘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(T_(c))of the 12,000+known superconductors available via the SuperCon database.Materials are first divided into two classes based on their T_(c) 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 T_(c) 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. | Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione Stefano Curtarolo Ichiro Takeuchi | 2018 | npj Computational Materials2018，，1： | 21 |
| 10 | Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries显示文摘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.Multiscale 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. | Aiping Wang Sanket Kadam Hong Li Siqi Shi Yue Qi | 2018 | npj Computational Materials2018，，1： | 20 |
| 11 | Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials显示文摘Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space.Ab initio calculations have emerged as a powerful approach that complements experiment.However,for multicomponent alloys existing approaches suffer from the chemical complexity involved.In this work we propose a method for studying HEAs computationally.Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations.The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA.The approach is employed to study phase stability,phase transitions,and chemical short-range order.The importance of including local relaxation effects is revealed:they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature.Finally,a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered. | Tatiana Kostiuchenko Fritz Körmann Jörg Neugebauer Alexander Shapeev | 2019 | npj Computational Materials2019，，1： | 17 |
| 12 | On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events显示文摘Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model.This severely limits the practical application of these models in systems with dynamics governed by important rare events,such as chemical reactions and diffusion.We present an adaptive Bayesian inference method for automating the training of interpretable,low-dimensional,and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework,the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model.The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency,while requiring a minimal amount of ab initio training data.We provide a fully opensource implementation of our method,as well as a procedure to map trained models to computationally efficient tabulated force fields. | Jonathan Vandermause Steven B.Torrisi Simon Batzner Yu Xie Lixin Sun Alexie M.Kolpak Boris Kozinsky | 2020 | npj Computational Materials2020，，1： | 17 |
| 13 | Machine learning guided appraisal and exploration of phase design for high entropy alloys显示文摘High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs. | Ziqing Zhou Yeju Zhou Quanfeng He Zhaoyi Ding Fucheng Li Yong Yang | 2019 | npj Computational Materials2019，，1： | 16 |
| 14 | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks显示文摘X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,respectively.We propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of misclassification.Finally,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less. | Felipe Oviedo Zekun Ren Shijing Sun Charles Settens Zhe Liu Noor Titan Putri Hartono Savitha Ramasamy Brian L.DeCost Siyu I.P.Tian Giuseppe Romano Aaron Gilad Kusne Tonio Buonassisi | 2019 | npj Computational Materials2019，，1： | 15 |
| 15 | Nanotwinned and hierarchical nanotwinned metals:a review of experimental,computational and theoretical efforts显示文摘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. | Ligang Sun Xiaoqiao He Jian Lu | 2018 | npj Computational Materials2018，，1： | 14 |
| 16 | Virtual screening of inorganic materials synthesis parameters with deep learning显示文摘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,highdimensional 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 SrTiO_(3) synthesis parameter sets,propose driving factors for brookite TiO_(2) formation,and identify correlations between alkali-ion intercalation and MnO_(2) polymorph selection. | Edward Kim Kevin Huang Stefanie Jegelka Elsa Olivetti | 2017 | npj Computational Materials2017，，1： | 14 |
| 17 | Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm显示文摘The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics. | Stephen Wu Yukiko Kondo Masa-aki Kakimoto Bin Yang Hironao Yamada Isao Kuwajima Guillaume Lambard Kenta Hongo Yibin Xu Junichiro Shiomi Christoph Schick Junko Morikawa Ryo Yoshida | 2019 | npj Computational Materials2019，，1： | 14 |
| 18 | A property-oriented design strategy for high performance copper alloys via machine learning显示文摘Traditional strategies for designing new materials with targeted property including methods such as trial and error,and experiences of domain experts,are time and cost consuming.In the present study,we propose a machine learning design system involving three features of machine learning modeling,compositional design and property prediction,which can accelerate the discovery of new materials.We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0%international annealed copper standard.There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions.Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning. | Changsheng Wang Huadong Fu Lei Jiang Dezhen Xue Jianxin Xie | 2019 | npj Computational Materials2019，，1： | 12 |
| 19 | Effective mass and Fermi surface complexity factor from ab initio band structure calculations显示文摘The effective mass is a convenient descriptor of the electronic band structure used to characterize the density of states and electron transport based on a free electron model.While effective mass is an excellent first-order descriptor in real systems,the exact value can have several definitions,each of which describe a different aspect of electron transport.Here we use Boltzmann transport calculations applied to ab initio band structures to extract a density-of-states effective mass from the Seebeck Coefficient and an inertial mass from the electrical conductivity to characterize the band structure irrespective of the exact scattering mechanism.We identify a Fermi Surface Complexity Factor:N_(v)^(*)K^(*) from the ratio of these two masses,which in simple cases depends on the number of Fermi surface pockets eN_(v)^(*) T and their anisotropy K^(*),both of which are beneficial to high thermoelectric performance as exemplified by the high values found in PbTe.The Fermi Surface Complexity factor can be used in high-throughput search of promising thermoelectric materials. | Zachary M.Gibbs Francesco Ricci Guodong Li Hong Zhu Kristin Persson Gerbrand Ceder Geoffroy Hautier Anubhav Jain G.Jeffrey Snyder | 2017 | npj Computational Materials2017，，1： | 11 |
| 20 | Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)显示文摘Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level. | Artem Maksov Ondrej Dyck Kai Wang Kai Xiao David B.Geohegan Bobby G.Sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov | 2019 | npj Computational Materials2019，，1： | 11 |
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