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Published October 21, 2022 | public
Journal Article

Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression

Abstract

We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H₁₀ chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules.

Additional Information

We thank Tamara Husch for helpful discussions at the early stages of this project. T.F.M. acknowledges support from the U.S. Army Research Laboratory (Grant No. W911NF-12-2-0023), the U.S. Department of Energy (Grant No. DE-SC0019390), the Caltech DeLogi Fund, and the Camille and Henry Dreyfus Foundation (Grant No. Award ML-20-196). Computational resources were provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the DOE Office of Science under contract Grant No. DE-AC02-05CH11231. National Science Foundation Graduate Research Fellowship Program (Grant No. DGE-1745301).

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023