Machine Learning for Predicting Electron Transfer Coupling
| Autor: | Chao-Ping Hsu, Gil C. Claudio, Ricky B. Nellas, Mac Kevin E. Braza, Chun I Wang |
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| Rok vydání: | 2019 |
| Předmět: |
010304 chemical physics
Coupling strength Chemistry Intermolecular force food and beverages 010402 general chemistry 01 natural sciences Molecular physics 0104 chemical sciences Coupling (electronics) Electron transfer Molecular geometry 0103 physical sciences Physical and Theoretical Chemistry Computer Science::Databases |
| Zdroj: | The Journal of Physical Chemistry A. 123:7792-7802 |
| ISSN: | 1520-5215 1089-5639 |
| DOI: | 10.1021/acs.jpca.9b04256 |
| Popis: | Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10-10 |
| Databáze: | OpenAIRE |
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