Lin Hung-Hsuan, Wang Chun-I, Yang Chou-Hsun, Secario Muhammad Khari, Hsu Chao-Ping
Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan.
Molecular Science and Digital Innovation Center, Genetics Generation Advancement Corp, No. 28, Ln. 36, Xinhu First Rd., Neihu, Taipei 114, Taiwan.
J Phys Chem A. 2024 Jan 11;128(1):271-280. doi: 10.1021/acs.jpca.3c04524. Epub 2023 Dec 29.
Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy.
电子耦合在确定电荷转移速率和动力学方面很重要。耦合强度对分子间（例如取向或距离）和分子内自由度都很敏感。因此，构建一个准确的机器学习模型来预测分子对的电子耦合具有挑战性，特别是对于那些来自非晶相的分子对，其分子间构型比来自晶体的分子对要多样化得多。在这项工作中，我们设计了一种新的预测算法，该算法采用两个连续的核岭回归（KRR）模型。第一个模型根据每个片段的结构变化预测分子轨道（MOs），并通过使用第二个模型中包含的重叠积分进一步预测耦合。通过我们的两步程序，我们实现了乙烯二聚体的平均绝对误差为0.27毫电子伏特，萘对的平均绝对误差为1.99毫电子伏特，精度有了很大提高，误差分别降低了14倍和3倍。此外，第一个模型的分子轨道也可以作为从原子结构获得其他量子化学性质的起点。这种方法也与具有足够精度的分子轨道预测器兼容。
