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

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 chemis

SUBMITTER: Shao X 

PROVIDER: S-EPMC10560258 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature