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- W4308756559 abstract "Physics-informed neural networks have been widely applied to learn general parametric solutions of differential equations. Here, we propose a neural network to discover parametric eigenvalue and eigenfunction surfaces of quantum systems. We apply our method to solve the hydrogen molecular ion. This is an ab-initio deep learning method that solves the Schrodinger equation with the Coulomb potential yielding realistic wavefunctions that include a cusp at the ion positions. The neural solutions are continuous and differentiable functions of the interatomic distance and their derivatives are analytically calculated by applying automatic differentiation. Such a parametric and analytical form of the solutions is useful for further calculations such as the determination of force fields." @default.
- W4308756559 created "2022-11-15" @default.
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- W4308756559 date "2022-11-08" @default.
- W4308756559 modified "2023-09-28" @default.
- W4308756559 title "First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces" @default.
- W4308756559 doi "https://doi.org/10.48550/arxiv.2211.04607" @default.
- W4308756559 hasPublicationYear "2022" @default.
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