دورية أكاديمية
Analytical gradients for molecular-orbital-based machine learning.
| العنوان: | Analytical gradients for molecular-orbital-based machine learning. |
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| المؤلفون: | Lee SJR; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Husch T; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Ding F; Entos, Inc., Los Angeles, California 90027, 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] 2021 Mar 28; Vol. 154 (12), pp. 124120. |
| 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: | Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 dataset while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures at a computational cost for the gradient evaluation that is comparable to a density-corrected density functional theory calculation. |
| تواريخ الأحداث: | Date Created: 20210403 Date Completed: 20210405 Latest Revision: 20210405 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1063/5.0040782 |
| PMID: | 33810669 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Scitation. | تدمد: | 1089-7690 |
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| DOI: | 10.1063/5.0040782 |