Machine Learning for Predicting Electron Transfer Coupling

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.