Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
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DOI: 10.1038/s41467-019-10827-4
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Cited by:
- Zeyin Yan & Dacong Wei & Xin Li & Lung Wa Chung, 2024. "Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Peikun Zheng & Roman Zubatyuk & Wei Wu & Olexandr Isayev & Pavlo O. Dral, 2021. "Artificial intelligence-enhanced quantum chemical method with broad applicability," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
- 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.
- Shuai Jiang & Yi-Rong Liu & Teng Huang & Ya-Juan Feng & Chun-Yu Wang & Zhong-Quan Wang & Bin-Jing Ge & Quan-Sheng Liu & Wei-Ran Guang & Wei Huang, 2022. "Towards fully ab initio simulation of atmospheric aerosol nucleation," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
- 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.
- David Buterez & Jon Paul Janet & Steven J. Kiddle & Dino Oglic & Pietro Lió, 2024. "Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
- Stephan Thaler & Julija Zavadlav, 2021. "Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
- Pushkar G. Ghanekar & Siddharth Deshpande & Jeffrey Greeley, 2022. "Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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