Machine learning predictions of electronic couplings for charge transport calculations of P3HT
Machine learning predictions of electronic couplings for charge transport calculations of P3HT
复制标题
用于 P3HT 电荷传输计算的电子耦合的机器学习预测
DOI：
10.1002/aic.16760
复制
发表时间：
2019
期刊：
影响因子：
3.7
通讯作者：
Jankowski, Eric
中科院分区：
文献类型：
--
作者：
Miller, Evan D.;Jones, Matthew L.;Henry, Mike M.;Stanfill, Bryan;Jankowski, Eric
这项工作的目的是降低预测有机半导体中电荷迁移率的计算成本，这将有利于筛选廉价的太阳能发电候选者。我们通过训练机器来预测聚(3-己基噻吩基)单体之间的电子耦合，从而最大限度地减少昂贵的量子化学计算的数量。我们测试了五种机器学习技术，并将随机森林确定为解决这个问题的最准确、信息密集和易于实现的方法，获得了0.02[×1.6 × 10−19J]的平均绝对误差，R2=0.986，预测电子耦合的速度是量子化学计算的390倍，并在以前工作的5%内通知零场空穴迁移率。我们讨论了识别小的有效训练集的策略。总之，我们展示了一个例子问题，其中机器学习技术提供了有效的减少计算成本，同时帮助理解潜在的结构-性质关系在材料系统中具有广泛的适用性。
The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly‐(3‐hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information‐dense, and easy‐to‐implement approach for this problem, achieving mean‐absolute‐error of 0.02 [× 1.6 × 10−19J],R2= 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero‐field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability.