在有机半导体中使用基于神经网络的Frenkel哈密尔顿和梯度的激发动态的非adiabatic模拟
Farhad Ghalami1,2, Philipp M Dohmen1, Mila Krämer1
1Institute of Physical Chemistry (IPC), Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.
Journal of chemical theory and computation
|July 8, 2024
概括
这项研究引入了一种使用神经网络模拟有机半导体中激子传播的多尺度方法. 该方法准确地预测了关键参数,使得可靠的刺激子扩散计算能够改善材料设计.
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.
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 有机半导体 (OSC) 对电子设备至关重要.
- 了解激电动力学是提高OSC效率的关键.
- 需要准确的模拟方法来预测刺激子的行为.
研究的目的:
- 开发一种多尺度的方法来模拟OSC中的Frenkel单点激子传播.
- 利用神经网络模型进行准确的哈密尔顿式和梯度预测.
- 提供对代表性OSC中激素扩散机制的见解.
主要方法:
- 一种结合神经网络和量子化学的多尺度模拟方法.
- 用DFTB训练Frenkel型哈密尔顿数及其梯度的神经网络模型.
- 采用最少开关的表面跳跃算法用于非adiabatic动态.
主要成果:
- 使用神经网络模型准确预测网站能量和激发性合.
- 该方法成功地应用于炭烯,五烯, perylenediimide 和 diindenoperylene.
- 模拟的兴奋子扩散常数与实验和理论数据有很好的一致性.
结论:
- 开发的多尺度方法提供了一个可靠的工具来模拟OSC中的激电动力学.
- 这种方法为控制激素扩散的因素提供了有价值的见解.
- 这项工作有助于合理设计先进的有机电子材料.
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