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在有机半导体中模拟电荷传输使用基于神经网络的哈密尔顿数和力

Philipp M Dohmen^1,2, Mila Krämer¹, Patrick Reiser^2,3

¹Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany.

Journal of chemical theory and computation

|June 21, 2023

概括

神经网络显著加快了有机半导体中电荷传输的模拟. 这种方法可以将计算成本降低高达7个数量级,同时保持洞移动性预测的准确性.

科学领域:

计算化学是一种计算化学.
材料科学是一种材料科学.
有机电子学有机电子学

背景情况:

最少开关表面跳跃方法对于模拟有机半导体中的电荷传输至关重要.
精确模拟电荷传输对于设计先进的有机电子设备至关重要.

研究的目的:

为了评估基于神经网络 (NN) 的哈密尔顿的性能,用于孔运输的非adiabatic分子动力学 (NAMD) 模拟.
评估不同核放松方案对模拟准确性和计算成本的影响.
为了将NN模型的性能与既有量子力学 (QM) 方法 (如DFTB和DFT) 进行比较.

主要方法:

执行了NAMD模拟在烯和烯中运输孔的模拟.
采用了基于NN的哈密尔顿数和两个核放松方案:预计算的重组能量和来自NN的现场能量梯度.
在DFTB和DFT计算数据上训练NN模型.
基于洞移动性,反向参与率,质量和计算成本,评估了NN模型.

主要成果:

在DFTB或DFT数据上训练的NN模型与QM参考方法在隐式和显式放松方面取得了很好的一致性.
使用NN模型的模拟表明,与DFTB和DFT相比,计算成本降低了1到7个数量级.
在模拟和实验孔移动性之间发现了合理的协议.

结论:

基于NN的哈密尔顿模型在电荷运输模拟的准确性和效率上都得到了显著的改进.
在复杂的大分子系统中,NNN是模拟电荷和激子运输的有希望的工具.
这种方法可以加速发现和开发新的有机半导体材料.

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