Data-Driven Versus Physics-Informed Neural Networks for Nonadiabatic Semiclassical Mapping Dynamics (Communications in Computational Chemistry, 2025)
Semiclassical mapping dynamics offer a computationally tractable approach for simulating nonadiabatic processes incomplex molecular systems. This work presents a comparative study of purely data-driven (DD) neural networks and physics-informed neural networks (PINNs) for learning the Markovian propagation of trajectories within the Meyer-Miller-Stock-Thoss (MMST) mapping Hamiltonian framework. Using the spin-boson model as a benchmark, we assess the performanceof both approaches in reproducing the nonadiabatic dynamical details for classical mapping model (CMM) and symmetricalquasiclassical (SQC) dynamics. Our results demonstrate that PINNs, which explicitly incorporate the physical equationsof motion, significantly outperform DD models, especially when trained with limited datasets. PINNs accurately capturethe population dynamics and preserve the physical correlations in phase space, regardless of the underlying neural networkarchitecture (fully connected network or gated recurrent unit) or the specific MMST Hamiltonian formulation. In contrast, DDmodels exhibit substantial inaccuracies and unphysical behaviors. This clearly shows the key benefit of embedding physicallaws into machine learning frameworks to achieve data-efficient, accurate, and reliable simulations of nonadiabatic phenomena.
Keywords: machine learning; nonadiabatic dynamics; physics-informed neural network; data-driven approach; mapping Hamiltonian