A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
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DOI: 10.1038/s41467-020-20427-2
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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.
- 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.
- Ang Gao & Richard C. Remsing, 2022. "Self-consistent determination of long-range electrostatics in neural network potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Oliver T. Unke & Stefan Chmiela & Michael Gastegger & Kristof T. Schütt & Huziel E. Sauceda & Klaus-Robert Müller, 2021. "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
- Kit Joll & Philipp Schienbein & Kevin M. Rosso & Jochen Blumberger, 2024. "Machine learning the electric field response of condensed phase systems using perturbed neural network potentials," Nature Communications, Nature, vol. 15(1), pages 1-12, 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.
- Linus C. Erhard & Jochen Rohrer & Karsten Albe & Volker L. Deringer, 2024. "Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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