Universal machine learning for the response of atomistic systems to external fields
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DOI: 10.1038/s41467-023-42148-y
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References listed on IDEAS
- 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.
- 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.
- 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.
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- Giuseppe Cassone & Fausto Martelli, 2024. "Electrofreezing of liquid water at ambient conditions," Nature Communications, Nature, vol. 15(1), pages 1-10, 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.
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