Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians

Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians

Abstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hami...

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Bibliographic Details
Main Authors: Changwei Zhang, Yang Zhong, Zhi-Guo Tao, Xinming Qin, Honghui Shang, Zhenggang Lan, Oleg V. Prezhdo, Xin-Gao Gong, Weibin Chu, Hongjun Xiang
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-57328-1
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https://doi.org/10.1038/s41467-025-57328-1

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