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- W2969286367 abstract "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 10-104 times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed." @default.
- W2969286367 created "2019-08-29" @default.
- W2969286367 creator A5055247770 @default.
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- W2969286367 creator A5091914090 @default.
- W2969286367 date "2019-08-20" @default.
- W2969286367 modified "2023-10-04" @default.
- W2969286367 title "Machine Learning for Predicting Electron Transfer Coupling" @default.
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- W2969286367 doi "https://doi.org/10.1021/acs.jpca.9b04256" @default.
- W2969286367 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31429287" @default.
- W2969286367 hasPublicationYear "2019" @default.
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