Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression
Year: 2022
Type: article
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.
Source: The Journal of Chemical Physics
DOI: 10.1063/5.0110886
Institution: California Institute of Technology
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Camille and Henry Dreyfus Foundation, California Institute of Technology, National Energy Research Scientific Computing Center, Office of Science, U.S. Army, Army Research Laboratory, National Science Foundation Graduate Research Fellowship Program
Awards: G1144327611, G122673668, G1286236842, G1677143136, G1863003419, AB INITIO MOLECULAR DYNAMICS BEYOND DENSITY FUNCTIONAL THEORY, G3083819904, G3353826364, G419881185, G42916606, G4501827968, G498139845, G500318789, G5076365615, AB INITIO MOLECULAR DYNAMICS BEYOND DENSITY FUNCTIONAL THEORY, G5259331294, G5480287990, G620260647, G6652958823, Graduate Research Fellowship Program (GRFP), G8586848730, G969889393
OpenAlex ID: W4307169907