Recurrent convolutional neural networks for non-adiabatic dynamics of quantum-classical systems

• URL: http://arxiv.org/abs/2412.06631v1
• Date: Mon, 09 Dec 2024 16:23:25 GMT
• Title: Recurrent convolutional neural networks for non-adiabatic dynamics of quantum-classical systems
• Authors: Alex P. Ning, Lingyu Yang, Gia-Wei Chern,
• Abstract summary: We present a RNN model based on convolutional neural networks for modeling the nonlinear non-adiabatic dynamics of hybrid quantum-classical systems. validation studies show that the trained PARC model could reproduce the space-time evolution of a one-dimensional semi-classical Holstein model.
• Score: 1.2972104025246092
• License:
• Abstract: Recurrent neural networks (RNNs) have recently been extensively applied to model the time-evolution in fluid dynamics, weather predictions, and even chaotic systems thanks to their ability to capture temporal dependencies and sequential patterns in data. Here we present a RNN model based on convolutional neural networks for modeling the nonlinear non-adiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The physics-aware recurrent convolutional (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Validation studies show that the trained PARC model could reproduce the space-time evolution of a one-dimensional semi-classical Holstein model {with comparable accuracy to direct numerical simulations}. We also investigate the scaling of prediction errors with size of training dataset, prediction window, step-size, and model size.

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