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Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
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The Materials Data Facility: Data Services to Advance Materials Science Research
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Quantum Chemistry Structures and Properties of 134 Kilo Molecules
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Zhizhong Li and Derek Hoiem. 2017 · 2017
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Learning Multiple Visual Domains with Residual Adapters. In Advances in Neural Information Processing Systems , Vol. 30. Curran Associates, Inc
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. 2017 · 2017
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SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller. 2017 · 2017
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A Data Ecosystem to Support Machine Learning in Materials Science
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
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On the Opportunities and Risks of Foundation Models
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Ben Blaiszik, Logan Ward, Marcus Schwarting, Jonathon Gaff, Ryan Chard, Daniel Pike, Kyle Chard, and Ian Foster. 2019 · 2019
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Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. 2019 · 2019
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Decoupled Weight Decay Regularization
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Generative Adversarial Networks (GAN) Based Efficient Sampling of Chemical Composition Space for Inverse Design of Inorganic Materials
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, and Jianjun Hu. 2020 · 2020
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The Materials Simulation Toolkit for Machine Learning (MAST-ML): An Automated Open Source Toolkit to Accelerate Data-Driven Materials Research
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Coupled-Cluster Techniques for Computational Chemistry: The CFOUR Program Package
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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
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High-Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
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