Machine learning electronic structure methods based on the one-electron reduced density matrix

Xuecheng Shao (), Lukas Paetow, Mark E. Tuckerman () and Michele Pavanello ()
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Xuecheng Shao: Rutgers University
Lukas Paetow: Rutgers University
Mark E. Tuckerman: New York University
Michele Pavanello: Rutgers University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms.

Date: 2023
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DOI: 10.1038/s41467-023-41953-9

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