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Abstract In this paper, we report an implementation of the quantum trajectory‐guided adaptive Gaussian (QTAG) method in a modular open‐source Libra package for quantum dynamics calculations. The QTAG method is based on a representation of wavefunctions in terms of a quantum trajectory‐guided adaptable Gaussians basis and is generalized for time‐propagation on multiple coupled surfaces to be applicable to model nonadiabatic dynamics. The potential matrix elements are evaluated within either the local harmonic or bra‐ket‐average (linear) approximations to the potential energy surfaces, the latter being a more practical option. Performance of the QTAG method is demonstrated and discussed for the Holstein and Tully models, which are the standard benchmarks for method development in the area of nonadiabatic dynamics.
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This content will become publicly available on April 8, 2026
Learning Multiple Potential Energy Surfaces by Automated Discovery of a Compatible Representation
Creating analytic representations of multiple potential energy surfaces for modeling electronically nonadiabatic processes is a major challenge being addressed in various ways by the chemical dynamics community. In this work, we introduce a new method that can achieve convenient learning of multiple potential energy surfaces (PESs) and their gradients (negatives of the forces) for a polyatomic system. This new method, called compatibilization by deep neural network (CDNN), is demonstrated to be accurate and, even more importantly, to be automatic. The only required input is a database with geometries and potential energies. The method produces a matrix, called the compatible potential energy matrix (CPEM), that may be interpreted as the electronic Hamiltonian in an implicit nonadiabatic basis, and the analytic adiabatic potential energy surfaces and their gradients are obtained by diagonalization and automatic differentiation. We show that the CPEM, which is neither adiabatic nor necessarily diabatic, can be discovered automatically during the learning procedure by the special design of a CDNN architecture. We believe that the CDNN method will be very useful in practice for learning coupled PESs for polyatomic systems because it is accurate and fully automatic.
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- Award ID(s):
- 2300321
- PAR ID:
- 10581716
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- Journal of Chemical Theory and Computation
- Volume:
- 21
- Issue:
- 7
- ISSN:
- 1549-9618
- Page Range / eLocation ID:
- 3342 to 3352
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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