دورية أكاديمية
Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression.
العنوان: | Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression. |
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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 |
تواريخ الأحداث: | Date Created: 20221022 Latest Revision: 20221024 |
رمز التحديث: | 20231215 |
DOI: | 10.1063/5.0110886 |
PMID: | 36272799 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1089-7690 |
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DOI: | 10.1063/5.0110886 |