Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians
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DOI: 10.1038/s41467-025-57328-1
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- Simon Axelrod & Eugene Shakhnovich & Rafael Gómez-Bombarelli, 2022. "Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential," Nature Communications, Nature, vol. 13(1), pages 1-11, 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.
- 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.
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