机器学习与多尺度模拟:迈向有机半导体材料的快速筛选

M. Rinderle, A. Gagliardi
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引用次数: 2

摘要

有机半导体器件承诺在低温下具有低成本的可加工性,但通常非晶材料的载流子迁移率低。对高迁移率有机半导体材料的搜索已经蓬勃发展,数据科学和机器学习方法可以筛选大量可能的有机材料。提出了一种基于机器学习传递积分的多尺度模拟模型来计算有机薄膜中的载流子迁移率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning & multiscale simulations: toward fast screening of organic semiconductor materials
Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.
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