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Title: Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings
We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. The efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH2NH2+.  more » « less
Award ID(s):
2154829
PAR ID:
10582426
Author(s) / Creator(s):
;
Publisher / Repository:
American Institute of Physics
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
161
Issue:
13
ISSN:
0021-9606
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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