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Cited by 503

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Deep-neural-network solution of the electronic Schrödinger equation

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Hermann, Jan, Schätzle, Zeno, Noé, Frank·2020·Nature Chemistry
DOI 10.1038/s41557-020-0544-y
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Abstract

The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H 10 , and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient. High-accuracy quantum chemistry methods struggle with a combinatorial explosion of Slater determinants in larger molecular systems, but now a method has been developed that learns electronic wavefunctions with deep neural networks and reaches high accuracy with only a few determinants. The method is applicable to realistic chemical processes such as the automerization of cyclobutadiene.

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Cited by

Metadata

Authors
Hermann, J., Schätzle, Z., & Noé, F.
Venue
Nature Chemistry
Publisher
Springer Science and Business Media LLC
Published
2020-09-23
Volume
12
Issue
10
Pages
891-897
ISSN
  • 1755-4330 (ISSN-L)
  • 1755-4349
DOI
10.1038/s41557-020-0544-y
OpenAlex
W2973805762
Cited by
503
References
3
FWCI
25.69
Type
journal article
Language
en
Open access
green
Licence

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