Nonadiabatic Derivative Couplings Calculated Using Information of Potential Energy Surfaces without Wavefunctions: Ab Initio and Machine Learning Implementations

In this work, we implemented an approximate algorithm for calculating nonadiabatic coupling matrix elements (NACMEs) of a polyatomic system with ab initio methods and machine learning (ML) models. Utilizing this algorithm, one can calculate NACMEs using only the information of potential energy surfa...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2023-05, Vol.28 (10), p.4222
Main Authors: Chen, Wen-Kai, Wang, Sheng-Rui, Liu, Xiang-Yang, Fang, Wei-Hai, Cui, Ganglong
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Couplings
Electronic structure
Energy
excited states
Force and energy
Hessian matrices
Learning algorithms
Machine learning
Methods
Molecular dynamics
nonadiabatic couplings
Norms
Potential energy
Simulation
Simulation methods
Wave functions
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Summary:In this work, we implemented an approximate algorithm for calculating nonadiabatic coupling matrix elements (NACMEs) of a polyatomic system with ab initio methods and machine learning (ML) models. Utilizing this algorithm, one can calculate NACMEs using only the information of potential energy surfaces (PESs), i.e., energies, and gradients as well as Hessian matrix elements. We used a realistic system, namely CH NH, to compare NACMEs calculated by this approximate PES-based algorithm and the accurate wavefunction-based algorithm. Our results show that this approximate PES-based algorithm can give very accurate results comparable to the wavefunction-based algorithm except at energetically degenerate points, i.e., conical intersections. We also tested a machine learning (ML)-trained model with this approximate PES-based algorithm, which also supplied similarly accurate NACMEs but more efficiently. The advantage of this PES-based algorithm is its significant potential to combine with electronic structure methods that do not implement wavefunction-based algorithms, low-scaling energy-based fragment methods, etc., and in particular efficient ML models, to compute NACMEs. The present work could encourage further research on nonadiabatic processes of large systems simulated by ab initio nonadiabatic dynamics simulation methods in which NACMEs are always required.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules28104222