Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians

Quantum-mechanical simulations of
charge and exciton transfer in
molecular organic materials are a key method to increase our understanding
of organic semiconductors. Our goal is to build an efficient multiscale
model to predict charge-transfer mobilities and exciton diffusion
constants from nonadiabatic molecular dynamics simulations and Marcus-based
Monte Carlo approaches. In this work, we apply machine learning models
to simulate charge and exciton propagation in organic semiconductors.
We show that kernel ridge regression models can be trained to predict
electronic and excitonic couplings from semiempirical density functional
tight binding (DFTB) reference data with very good accuracy. In simulations,
the models could reproduce hole mobilities along the anthracene crystal
axes to within 8.5% of the DFTB reference and 34% of the experimental
results with only 1000 training data points. Using these models decreased
the cost of exciton transfer simulations by one order of magnitude.

量子力学模拟在分子有机材料中电荷和激子转移过程是加深我们对有机半导体理解的关键方法。本研究旨在构建一个高效的多尺度模型，以预测非绝热分子动力学模拟和基于 Marcus 的蒙特卡洛方法得到的电荷转移迁移率和激子扩散常数。在本研究中，我们应用机器学习模型模拟有机半导体中的电荷和激子传播。我们证明，核岭回归模型可以被训练以从半经验密度泛函紧束缚（DFTB）参考数据中预测电子和激子耦合，且精度极高。在模拟中，这些模型能够将蒽晶体轴上的空穴迁移率与 DFTB 参考数据吻合至 8.5%，与实验结果吻合至 34%，仅使用 1000 个训练数据点。利用这些模型降低了激子转移模拟的成本，降低了十倍。