期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 16, 期 7, 页码 4061-4070出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.0c00246
关键词
-
资金
- German Research Foundation (DFG) through the Research Training Group 2450 Tailored Scale-Bridging Approaches to Computational Nanoscience
- DFG [SFB 1249, INST 40/467-1 FUGG]
- Virtual Materials Design (VIRTMAT) project
- National Centre of Competence in Research (NCCR) Materials Revolution: Computational Design and Discovery of Novel Materials (MARVEL) of the Swiss National Science Foundation Wurttemberg through bwHPC
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 non-adiabatic 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据