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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 | 介电测井新技术与应用显示文摘介电测井仪通过地层电磁波测量能分析淡水环境储层,识别可流动油气。介电测井数据资料对分析稠油储层特别有用。一种新型仪器通过长时间的应用,正将新的生机带入介电测井技术。这一切都得益于最近开发的、用于评价碳酸盐岩结构和泥质对砂岩影响的频散技术。 | Romulo Carmona Eric Decoster Jim Hemingway Mehdi Hizem Laurent Mosse Tarek Rizk Dale Julander Jeffrey Little Tom McDonald Jonathan Mude | 2013 | 国外测井技术2013，，5： | 3 |
| 6 | Challenges of type Ⅱ diabetes and role of health care social work:a neglected area of practice 显示文摘 | Vaughn A Decoster | 2001 | Health & Social Work2001，26，1： | 1 |
| 7 | Antimicrobial suscepti- bility of group B streptococci collected in two Belgian hospitals 显示文摘 | Decoster L Frans J Blanckaert H | 2005 | Acta Clin Belg2005，60，4： | 1 |
| 8 | Rapid prototyping: the future of trauma surgery?显示文摘 | Brown GA Firoozbakhsh K DeCoster TA | 2003 | J Bone Joint Surg Am2003，85，： | 1 |
| 9 | Experimental and probabilistic analysis of distal femoral periprosthetic fracture: a comparison of locking plate and intramedullary nail fixation. Part B: probabilistic investigation显示文摘 | Christina Salas Deana Mercer Thomas A. DeCoster Mahmoud M. Reda Taha | 2011 | Computer Methods in Biomechanics and Biomedical Engineering2011，，2： | 1 |
| 10 | Challenges of type 2 diabetes and role of healthcare social work: a neglected area of practice显示文摘 | Vaughn A Decoster | 2001 | Health & Social Work2001，26，1： | 1 |
| 11 | Mechanics of retrograde nail versus plate fixation for supracondylar femur fractures显示文摘 | Firoozbakhsh K Behzadi K DeCoster TA | | 0，，02： | 1 |
| 12 | Plain radio- graphic interpretation in trimalleolar ankle fractures poorly assess- es posterior fragment size显示文摘 | Ferries JS DeCoster TA Firoozbakhsh KK | 1994 | J Orthop Trauma1994，8，4： | 1 |
| 13 | Multiple B-cell epitopes in a recombinant GRA2 secreted antigen of Toxoplasma gondii显示文摘 | Murray A Mercier C Decoster A | 1993 | Appl Parasitol1993，34，4： | 1 |
| 14 | Role of calci- um in sigma-mediated neuroprotection in rat primary cortical neurons 显示文摘 | Klette KL DeCoster MA Moreton JE Tortella FC | 1995 | Brain Res1995，704，1： | 1 |
| 15 | Bisphospho- nates:Prevention of bone metastases in lung cancer 显示文摘 | Decoster L de Marinis F Syrigos K | 2012 | Recent Results Cancer Res2012，192，： | 1 |
| 16 | Neuroprotectionby PEDF against glutamate toxicity in developing primaryhippocampal neurons显示文摘 | DeCoster MA Schabelman E Tombran-Tink J | 1999 | J Neurosci Res1999，56，6： | 1 |
| 17 | Machine learning for science:State of the art and futureprospects显示文摘 | Mjolsness E Decoste D | | 0，，14： | 1 |
| 18 | External Fixation of Tibial Plafond Fractures: Is Routine Plating of the Fibula Necessary?显示文摘 | Todd M. Williams J. Lawrence Marsh James V. Nepola Thomas A. DeCoster Shepard R. Hurwitz Susan B. Bonar | 1998 | Journal of Orthopaedic Trauma1998，，1： | 1 |
| 19 | Bisphosphonates : prevention of bone metastases in lung cancer 显示文摘 | Decoster L de Marinis F Syrigos K | 2012 | Recent Results Cancer Res2012，192，： | 1 |
| 20 | Generation and biological characterization of menbrane-bound, uncleavable murine tumor necrosis factor显示文摘 | Decoster E Vanhaesebroeck B | 1995 | J Biol Chem1995，270，18： | 1 |
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