论文标题
使用哈密顿元素和神经网络的力量的电荷传输模拟
Charge Transfer Simulations using Hamiltonian Elements and Forces from Neural Networks
论文作者
论文摘要
轨迹表面跳跃方法已被广泛用于有机半导体中电荷传输的模拟。在本研究中，我们采用基于机器学习（ML）的哈密顿量来模拟蒽和五苯甲烷中的电荷运输。基于神经网络（NN）的模型不仅能够预测位点能量和耦合，还可以预测场地能量的梯度以及力所需的偏置梯度。我们在DFTB质量数据上训练模型的蒽和五苯烯。通过在繁殖模拟中使用所获得的模型，我们可以根据质量和计算成本来评估它们在这些材料中重现孔迁移率的性能。结果表明，使用基于NN的Hamiltonian获得的电荷迁移率与使用基于DFTB的Hamiltonian计算的电荷迁移率达成了很好的协议。
The trajectory surface hopping method has been widely used in the simulation of charge transport in organic semiconductors. In the present study, we employ the machine learning (ML) based Hamiltonian to simulate the charge transport in anthracene and pentacene. The neural network (NN) based models are able to predict not just site energies and couplings but also the gradients of the site energy as well as off-diagonal gradients necessary for forces. We train the models on DFTB-quality data for both anthracene and pentacene. By using the obtained models in propagation simulations, we evaluate their performance in reproducing hole mobilities in these materials in terms of both quality and computational cost. The results show that the charge mobilities obtained using the NN-based Hamiltonian are in very good agreements with the charge mobilities computed using the DFTB-based Hamiltonian.
