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Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression
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DOI：10.1063/5.0110886.png)
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Abstract
En 中文
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 H-10 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. Published under an exclusive license by AIP Publishing.
Keywords:
DENSITY-FUNCTIONAL THEORY
AB-INITIO
ENERGIES
T-1
Journal
IF:
3.1
Papers:
7.2W
Citations:
23.2W