Ab-initio quantum chemistry with neural-network wavefunctions. (arXiv:2208.12590v1 [physics.chem-ph])

Machine learning and specifically deep-learning methods have outperformed
human capabilities in many pattern recognition and data processing problems, in
game playing, and now also play an increasingly important role in scientific
discovery. A key application of machine learning in the molecular sciences is
to learn potential energy surfaces or force fields from ab-initio solutions of
the electronic Schr\"odinger equation using datasets obtained with density
functional theory, coupled cluster, or other quantum chemistry methods. Here we
review a recent and complementary …

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