1. Schr\'{o}dingerNet: A Universal Neural Network Solver for The Schr\'{o}dinger Equation
- Author
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Zhang, Yaolong, Jiang, Bin, and Guo, Hua
- Subjects
Physics - Chemical Physics ,Physics - Computational Physics - Abstract
Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schr\"{o}dingerNet equation (SE) by integrating various neural network (NN)-based wavefunction ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born-Oppenheimer approximation (BOA) and require a separate and computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, Schr\"{o}dingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system. This approach is based on a rotationally invariant total wavefunction ansatz that includes both nuclear and electronic coordinates. This strategy allows for an efficient and accurate generation of a continuous potential energy surface at any geometry within the well-sampled nuclear configuration space, and also incorporates non-BO corrections, through a single training process. Comparison with benchmarks of atomic and molecular systems demonstrates its accuracy and efficiency, Comment: 4 figure
- Published
- 2024