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 Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N²AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N²AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstra

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11868637 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature