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.

电子转移耦合（electron transfer coupling）是决定电子转移速率的关键因素。该耦合强度对分子几何结构的细节，尤其是分子间构型，极为敏感。因此，采用完全第一性原理方法研究电荷输运行为，会在量子化学（QC）计算中消耗大量计算资源。为解决这一问题，我们开发了一种机器学习（ML）方法来评估电子耦合强度。我们通过核岭回归结合库仑矩阵表征，构建了针对乙烯体系的典型机器学习模型。由于机器学习模型的性能高度依赖其构建策略，我们系统研究了机器学习模型的泛化性、特征选择以及目标标签的选取。基于40000个样本训练得到的最优机器学习模型，实现了3.5毫电子伏特（meV）的平均绝对误差，且在相态预测任务中准确率超过98%。我们成功捕捉到了电子耦合强度随距离与取向的依赖关系。通过规避量子化学计算，该机器学习模型可将计算开销缩减至原有水平的1/10至1/104。借助机器学习方法，可靠的电荷输运模型与内在机制可得到进一步发展。