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

التفاصيل البيبلوغرافية
العنوان:	Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression.
المؤلفون:	Cheng L; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Sun J; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Deustua JE; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Bhethanabotla VC; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Miller TF 3rd; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA.
المصدر:	The Journal of chemical physics [J Chem Phys] 2022 Oct 21; Vol. 157 (15), pp. 154105.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: American Institute of Physics Country of Publication: United States NLM ID: 0375360 Publication Model: Print Cited Medium: Internet ISSN: 1089-7690 (Electronic) Linking ISSN: 00219606 NLM ISO Abbreviation: J Chem Phys Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة:	Publication: New York, NY : American Institute of Physics Original Publication: Lancaster, Pa., American Institute of Physics.
مستخلص:	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.
تواريخ الأحداث:	Date Created: 20221022 Latest Revision: 20221024
رمز التحديث:	20231215
DOI:	10.1063/5.0110886
PMID:	36272799
قاعدة البيانات:	MEDLINE

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الوصف
تدمد:	1089-7690
DOI:	10.1063/5.0110886