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Kohn-Sham density functional theory has been the most popular method in electronic structure calculations. To fulfill the increasing accuracy requirements, new approximate functionals are needed to address key issues in existing approximations. It is well known that nonlocal components are crucial. Current nonlocal functionals mostly require orbital dependence such as in Hartree-Fock exchange and many-body perturbation correlation energy, which, however, leads to higher computational costs. Deviating from this pathway, we describe functional nonlocality in a new approach. By partitioning the total density to atom-centered local densities, a many-body expansion is proposed. This many-body expansion can be truncated at one-body contributions, if a base functional is used and an energy correction is approximated. The contribution from each atom-centered local density is a single finite-range nonlocal functional that is universal for all atoms. We then use machine learning to develop this universal atom-centered functional. Parameters in this functional is determined by fitting to data that are produced by high-level theories. Extensive tests on several different test sets, which include reaction energies, reaction barrier heights and non-covalent interaction energies, show that the new functional, with only the density as the basic variable, can produce results comparable to the best-performing double-hybrid functionals with a lower computational cost. This opens a new pathway to nonlocal functional development and applications.
This dataset contains: modified MGCDB84, GMTKN55 and MOR41 data files; calculated BLYP wave function files; modified dftd3 program and DFT-D3(BJ) internal data it generated; python scripts used to generate power spectra and the power spectra it calculated; training, validation and test data; python scripts used to train and evaluate the PyTorch model and the resulting models; and the python script generated the carbon atom energy plot.... [Read More]
Total Size
2 files (7.13 GB)
Data Citation
Chen, Z., & Yang, W. (2023). Data and scripts from: Development of a machine-learning finite-range nonlocal density functional. Duke Research Data Repository. https://doi.org/10.7924/r4fj2p230
