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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

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Cited by:

  1. Wei Lu & Jixian Zhang & Weifeng Huang & Ziqiao Zhang & Xiangyu Jia & Zhenyu Wang & Leilei Shi & Chengtao Li & Peter G. Wolynes & Shuangjia Zheng, 2024. "DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  2. Jonathan P. Mailoa & Xin Li & Shengyu Zhang, 2024. "3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  3. Andreas Erlebach & Martin Šípka & Indranil Saha & Petr Nachtigall & Christopher J. Heard & Lukáš Grajciar, 2024. "A reactive neural network framework for water-loaded acidic zeolites," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  4. Li Zheng & Konstantinos Karapiperis & Siddhant Kumar & Dennis M. Kochmann, 2023. "Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  5. Albert Musaelian & Simon Batzner & Anders Johansson & Lixin Sun & Cameron J. Owen & Mordechai Kornbluth & Boris Kozinsky, 2023. "Learning local equivariant representations for large-scale atomistic dynamics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  6. J. Thorben Frank & Oliver T. Unke & Klaus-Robert Müller & Stefan Chmiela, 2024. "A Euclidean transformer for fast and stable machine learned force fields," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  7. Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  8. Rama Oktavian & Ruben Goeminne & Lawson T. Glasby & Ping Song & Racheal Huynh & Omid Taheri Qazvini & Omid Ghaffari-Nik & Nima Masoumifard & Joan L. Cordiner & Pierre Hovington & Veronique Speybroeck , 2024. "Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  9. Bowen Hou & Jinyuan Wu & Diana Y. Qiu, 2024. "Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  10. T. M. Linker & A. Krishnamoorthy & L. L. Daemen & A. J. Ramirez-Cuesta & K. Nomura & A. Nakano & Y. Q. Cheng & W. R. Hicks & A. I. Kolesnikov & P. D. Vashishta, 2024. "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  11. Adil Kabylda & Valentin Vassilev-Galindo & Stefan Chmiela & Igor Poltavsky & Alexandre Tkatchenko, 2023. "Efficient interatomic descriptors for accurate machine learning force fields of extended molecules," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  12. Alessio Fallani & Leonardo Medrano Sandonas & Alexandre Tkatchenko, 2024. "Inverse mapping of quantum properties to structures for chemical space of small organic molecules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  13. Chao Liang & Yilimiranmu Rouzhahong & Caiyuan Ye & Chong Li & Biao Wang & Huashan Li, 2023. "Material symmetry recognition and property prediction accomplished by crystal capsule representation," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  14. Bin Han & Kuang Yu, 2025. "Refining potential energy surface through dynamical properties via differentiable molecular simulation," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  15. Keke Song & Rui Zhao & Jiahui Liu & Yanzhou Wang & Eric Lindgren & Yong Wang & Shunda Chen & Ke Xu & Ting Liang & Penghua Ying & Nan Xu & Zhiqiang Zhao & Jiuyang Shi & Junjie Wang & Shuang Lyu & Zezhu, 2024. "General-purpose machine-learned potential for 16 elemental metals and their alloys," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  16. Yaolong Zhang & Bin Jiang, 2023. "Universal machine learning for the response of atomistic systems to external fields," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  17. Taoyong Cui & Chenyu Tang & Dongzhan Zhou & Yuqiang Li & Xingao Gong & Wanli Ouyang & Mao Su & Shufei Zhang, 2025. "Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
  18. Zechen Tang & He Li & Peize Lin & Xiaoxun Gong & Gan Jin & Lixin He & Hong Jiang & Xinguo Ren & Wenhui Duan & Yong Xu, 2024. "A deep equivariant neural network approach for efficient hybrid density functional calculations," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  19. Mingfeng Liu & Jiantao Wang & Junwei Hu & Peitao Liu & Haiyang Niu & Xuexi Yan & Jiangxu Li & Haile Yan & Bo Yang & Yan Sun & Chunlin Chen & Georg Kresse & Liang Zuo & Xing-Qiu Chen, 2024. "Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  20. Chen, Xin & Zhang, Lin & Huang, JiangBo & Jin, Lei & Song, YongShi & Zheng, XianHua & Zou, ZhiXiong, 2025. "A thermodynamics-consistent machine learning approach for ammonia-water thermal cycles," Energy, Elsevier, vol. 315(C).
  21. Lucien F. Krapp & Luciano A. Abriata & Fabio Cortés Rodriguez & Matteo Dal Peraro, 2023. "PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  22. Junjie Wang & Yong Wang & Haoting Zhang & Ziyang Yang & Zhixin Liang & Jiuyang Shi & Hui-Tian Wang & Dingyu Xing & Jian Sun, 2024. "E(n)-Equivariant cartesian tensor message passing interatomic potential," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  23. Charlotte Loh & Thomas Christensen & Rumen Dangovski & Samuel Kim & Marin Soljačić, 2022. "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  24. Xiaoxun Gong & He Li & Nianlong Zou & Runzhang Xu & Wenhui Duan & Yong Xu, 2023. "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  25. Yusong Wang & Tong Wang & Shaoning Li & Xinheng He & Mingyu Li & Zun Wang & Nanning Zheng & Bin Shao & Tie-Yan Liu, 2024. "Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  26. Ziduo Yang & Yi-Ming Zhao & Xian Wang & Xiaoqing Liu & Xiuying Zhang & Yifan Li & Qiujie Lv & Calvin Yu-Chian Chen & Lei Shen, 2024. "Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
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