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The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design.
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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
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Ionic correlations and failure of nernst-einstein relation in solid-state electrolytes
Aris Marcolongo and Nicola Marzari · 2017
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Machine learning a general-purpose interatomic potential for silicon
Albert P Bartók, James Kermode, Noam Bernstein, and Gábor Csányi · 2018
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Active learning in gaussian process interpolation of potential energy surfaces
Elena Uteva, Richard S Graham, Richard D Wilkinson, and Richard J Wheatley · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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Transfer learning using attentions across atomic systems with graph neural networks (taag)
Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C Lawrence Zitnick, and Zachary Ulissi · 2022
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Dpa-1: Pretraining of attention-based deep potential model for molecular simulation
Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang, Linfeng Zhang, and Han Wang · 2022
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Spherical channels for modeling atomic interactions
Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood · 2022
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky · 2022
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wacsf—weighted atom-centered symmetry functions as descriptors in machine learning potentials
Michael Gastegger, Ludwig Schwiedrzik, Marius Bittermann, Florian Berzsenyi, and Philipp Marquetand · 2018
Cited alongside, same era.
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, et al · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
Network representation learning: A survey
Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Cited alongside, same era.
Soft phonon modes and diffuse scattering in pb (in1/2nb1/2) o3-pb (mg1/3nb2/3) o3-pbtio3 relaxor ferroelectrics
Qian Li, Sergey Danilkin, Guochu Deng, Zhengrong Li, Ray L Withers, Zhuo Xu, and Yun Liu · 2018
Cited alongside, same era.
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Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi · 2022
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2022
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2022
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Unified 2d and 3d pre-training of molecular representations
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu · 2022
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Pre-training via denoisingfor molecular property prediction
S. Zaidi, M. Schaarschmidt, J. Martens, H. Kim, Y. W. Teh, A. Sanchez-Gonzalez, P. Battaglia, R. Pascanu, and J. Godwin · 2022
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3d equivariant molecular graph pretraining
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2022
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Dp compress: A model compression scheme for generating efficient deep potential models
Denghui Lu, Wanrun Jiang, Yixiao Chen, Linfeng Zhang, Weile Jia, Han Wang, and Mohan Chen · 2022
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Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C Lawrence Zitnick, and Abhishek Das · 2022
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Transition1x-a dataset for building generalizable reactive machine learning potentials
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, and Ole Winther · 2022
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Classical and machine learning interatomic potentials for bcc vanadium
Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J Srolovitz, Tongqi Wen, and Zhaoxuan Wu · 2022
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A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment
Xiaoyang Wang, Yinan Wang, Linfeng Zhang, Fuzhi Dai, and Han Wang · 2022
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A deep potential model with long-range electrostatic interactions
Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z Panagiotopoulos, Roberto Car, et al · 2022
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How do graph networks generalize to large and diverse molecular systems?
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C Lawrence Zitnick, and Abhishek Das · 2022
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Qd π \pi : A quantum deep potential interaction model for drug discovery
Jinzhe Zeng, Yujun Tao, Timothy J Giese, and Darrin M York · 2023
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Unified graph neural network force-field for the periodic table: solid state applications
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, and Francesca Tavazza · 2023
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Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder · 2023
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Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
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Gpip: Geometry-enhanced pre-training on interatomic potentials
Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, and Wanli Ouyang · 2023
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May the force be with you: Unified force-centric pre-training for 3d molecular conformations
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Leveraging multitask learning to improve the transferability of machine learned force fields
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Denoise pre-training on non-equilibriummolecules for accurate and transferable neuralpotentials
Y. Wang, C. Xu, Z. Li, and A. B. Farimani · 2023
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From molecules to materials: Pre-training large generalizable models for atomic property prediction
Nima Shoghi, Adeesh Kolluru, John R Kitchin, Zachary W Ulissi, C Lawrence Zitnick, and Brandon M Wood · 2023
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Deepmd-kit v2: A software package for deep potential models
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Highly accurate quantum chemical propertyprediction with uni-mol+
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Fractional denoising for 3d molecular pre-training
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Synthetic pre-training for neural-network interatomic potentials
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