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arXiv 2301.13435cond-mat.mtrl-sci
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非晶有机半导体中的激子扩散:利用机器学习降低模拟开销

Exciton diffusion in amorphous organic semiconductors: reducing simulation overheads with machine learning

Chayanit Wechwithayakhlung, Geoffrey R. Weal, Yu Kaneko, Paul A. Hume, Justin M. Hodgkiss, Daniel M. Packwood

AI总结:

本文提出一种快速训练的机器学习架构以预测分子间激子耦合参数,并在非晶并五苯中实现接近密度泛函理论精度的激子扩散模拟。

摘要:

对非晶有机材料中的激子和电荷跳跃进行模拟涉及大量物理参数。在模拟开始之前,每个参数都必须通过成本高昂的从头算计算获得,这给研究激子扩散带来了显著的计算开销,尤其是在大型且复杂的材料数据集中。尽管此前已经探索过利用机器学习快速预测这些参数的想法,但典型的机器学习模型需要较长的训练时间,最终也会增加模拟开销。本文提出了一种新的机器学习架构,用于构建分子间激子耦合参数的预测模型。该架构的设计使其总训练时间相较于普通的 Gaussian process regression(高斯过程回归)或 kernel ridge regression(核岭回归)模型有所减少。基于这一架构,我们构建了一个预测模型,并用它来估算进入非晶并五苯中激子跳跃模拟的耦合参数。我们表明,与完全使用由 density functional theory(密度泛函理论)计算得到的耦合参数所进行的模拟相比,这种跳跃模拟能够对激子扩散张量元素及其他性质作出极好的预测。这一结果,连同我们架构所带来的较短训练时间,因此展示了如何利用机器学习来降低与非晶有机材料中激子和电荷扩散模拟相关的高昂计算开销。

英文摘要:

Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, therefore shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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