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arXiv · 1710.03213

Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks

Peiyuan Teng
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Abstract

Inspired by the recent work of Carleo and Troyer[1], we apply machine learning methods to quantum mechanics in this article. The radial basis function network in a discrete basis is used as the variational wavefunction for the ground state of a quantum system. Variational Monte Carlo(VMC) calculations are carried out for some simple Hamiltonians. The results are in good agreements with theoretical values. The smallest eigenvalue of a Hermitian matrix can also be acquired using VMC calculations. Our results demonstrate that machine learning techniques are capable of solving quantum mechanical problems.

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Peiyuan Teng. 2018-09-11. Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks. https://doi.org/10.1103/physreve.98.033305

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