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

Cheng, Lixue and Sun, Jiace and Deustua, J. Emiliano and Bhethanabotla, Vignesh C. and Miller, Thomas F., III (2022) Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression. Journal of Chemical Physics, 157 (15). Art. No. 154105. ISSN 0021-9606. doi:10.1063/5.0110886.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Cheng, Lixue0000-0002-7329-0585
Sun, Jiace0000-0002-0566-2084
Deustua, J. Emiliano0000-0001-8193-2229
Bhethanabotla, Vignesh C.0000-0001-9401-6967
Miller, Thomas F., III0000-0002-1882-5380
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).
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-12-2-0023
Department of Energy (DOE)DE-SC0019390
Caltech DeLogi FundUNSPECIFIED
Camille and Henry Dreyfus FoundationML-20-196
Department of Energy (DOE)DE-AC02-05CH11231
NSF Graduate Research FellowshipDGE-1745301
Issue or Number:15
Record Number:CaltechAUTHORS:20221110-414654600.1
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117818
Deposited By: Research Services Depository
Deposited On:22 Nov 2022 23:34
Last Modified:22 Nov 2022 23:34

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