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| 1 | The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design显示文摘The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment. | Kamal Choudhary Kevin F.Garrity Andrew C.E.Reid Brian DeCost Adam J.Biacchi Angela R.Hight Walker Zachary Trautt Jason Hattrick-Simpers A.Gilad Kusne Andrea Centrone Albert Davydov Jie Jiang Ruth Pachter Gowoon Cheon Evan Reed Ankit Agrawal Xiaofeng Qian Vinit Sharma Houlong Zhuang Sergei V.Kalinin Bobby G.Sumpter Ghanshyam Pilania Pinar Acar Subhasish Mandal Kristjan Haule David Vanderbilt Karin Rabe Francesca Tavazza | 2020 | npj Computational Materials2020，，1： | 10 |
| 2 | Recent advances and applications of deep learning methods in materials science显示文摘Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science. | Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton | 2022 | npj Computational Materials2022，，1： | 9 |
| 3 | Atomistic Line Graph Neural Network for improved materials property predictions显示文摘Graph neural networks(GNN)have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.While most existing GNN models for atomistic predictions are based on atomic distance information,they do not explicitly incorporate bond angles,which are critical for distinguishing many atomic structures.Furthermore,many material properties are known to be sensitive to slight changes in bond angles.We present an Atomistic Line Graph Neural Network(ALIGNN),a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles.We demonstrate that angle information can be explicitly and efficiently included,leading to improved performance on multiple atomistic prediction tasks.We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT,Materials project,and QM9 databases.ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85%in accuracy with better or comparable model training speed. | Kamal Choudhary Brian DeCost | 2021 | npj Computational Materials2021，，1： | 6 |
| 4 | Author Correction:Atomistic Line Graph Neural Network for improved materials property predictions显示文摘The original version of this Article contained errors in values of ALIGNN data in Table 5.As a result,the following changes have been made to the original version of this Article:In Table 5,the data for“OrbNetens5”column were removed and values for“ALIGNN”column were updated.The correct version of Table 5 appears below. | Kamal Choudhary Brian DeCost | 2022 | npj Computational Materials2022，，1： | 4 |
| 5 | A critical examination of robustness and generalizability of machine learning prediction of materials properties显示文摘Recent advances in machine learning(ML)have led to substantial performance improvement in material database benchmarks,but an excellent benchmark score may not imply good generalization performance.Here we show that ML models trained on Materials Project 2018 can have severely degraded performance on new compounds in Materials Project 2021 due to the distribution shift.We discuss how to foresee the issue with a few simple tools.Firstly,the uniform manifold approximation and projection(UMAP)can be used to investigate the relation between the training and test data within the feature space.Secondly,the disagreement between multiple ML models on the test data can illuminate out-of-distribution samples.We demonstrate that the UMAP-guided and query by committee acquisition strategies can greatly improve prediction accuracy by adding only 1%of the test data.We believe this work provides valuable insights for building databases and models that enable better robustness and generalizability. | Kangming Li Brian DeCost Kamal Choudhary Michael Greenwood Jason Hattrick-Simpers | 2023 | npj Computational Materials2023，，1： | 0 |
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