Towards exact molecular dynamics simulations with machine-learned force fields
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DOI: 10.1038/s41467-018-06169-2
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Cited by:
- 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.
- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, 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.
- Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- 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.
- Yuanming Bai & Leslie Vogt-Maranto & Mark E. Tuckerman & William J. Glover, 2022. "Machine learning the Hohenberg-Kohn map for molecular excited states," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- 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.
- Simon Batzner & Albert Musaelian & Lixin Sun & Mario Geiger & Jonathan P. Mailoa & Mordechai Kornbluth & Nicola Molinari & Tess E. Smidt & Boris Kozinsky, 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- 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.
- 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.
- Eric C.-Y. Yuan & Anup Kumar & Xingyi Guan & Eric D. Hermes & Andrew S. Rosen & Judit Zádor & Teresa Head-Gordon & Samuel M. Blau, 2024. "Analytical ab initio hessian from a deep learning potential for transition state optimization," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- 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.
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