May 14, 2024, 4:43 a.m. | Leon Gerard, Michael Scherbela, Halvard Sutterud, Matthew Foulkes, Philipp Grohs

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07599v1 Announce Type: cross
Abstract: Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schr\"odinger equation. Despite its favorable scaling with the number of electrons, $\mathcal{O}(n_\text{el}^{4})$, the practical value of DL-VMC is limited by the high cost of optimizing the neural network weights for every system studied. To mitigate this problem, recent research has proposed optimizing a single neural network across multiple systems, reducing the cost per system. Here …

abstract arxiv cost cs.lg electron equation network neural network physics.comp-ph practical scaling solutions text type value

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