The on-top pair density  is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in space. Being the simplest quantity related to the two-particle density matrix, the on-top pair density is a powerful indicator of electron correlation effects, and as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree–Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the complete active space self-consistent field (CASSCF)-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules, bypassing completely the need for ab initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.
Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation
Note: This paper is part of the JCP Special Collection in Honor of Women in Chemical Physics and Physical Chemistry.
Alberto Fabrizio, Ksenia R. Briling, David D. Girardier, Clemence Corminboeuf; Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation. J. Chem. Phys. 28 November 2020; 153 (20): 204111. https://doi.org/10.1063/5.0033326
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