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Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

preprint
submitted on 01.04.2020 and posted on 02.04.2020 by Benjamin Peyton, Connor Briggs, Ruhee D'Cunha, Johannes T. Margraf, Thomas Crawford

The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of train- ing data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system.

Funding

DOE CCS AL-18-380-057

History

Email Address of Submitting Author

crawdad@vt.edu

Institution

Virginia Tech

Country

United States

ORCID For Submitting Author

0000-0002-7961-7016

Declaration of Conflict of Interest

None

Version Notes

Submitted for publication

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in The Journal of Physical Chemistry A

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