有机半导体
管道(软件)
动性
分子间力
半导体
载流子
电荷(物理)
材料科学
化学物理
光电子学
凝聚态物理
化学
物理
工程类
分子
有机化学
机械工程
量子力学
社会学
社会科学
作者
Vinayak Bhat,Baskar Ganapathysubramanian,Chad Risko
标识
DOI:10.1021/acs.jpclett.4c01309
摘要
Organic semiconductors (OSC) offer tremendous potential across a wide range of (opto)electronic applications. OSC development, however, is often limited by trial-and-error design, with computational modeling approaches deployed to evaluate and screen candidates through a suite of molecular and materials descriptors that generally require hours to days of computational time to accumulate. Such bottlenecks slow the pace and limit the exploration of the vast chemical space comprising OSC. When considering charge-carrier transport in OSC, a key parameter of interest is the intermolecular electronic coupling. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic crystalline materials from their three-dimensional (3D) molecular geometries. The ML predictions take only a few seconds of computing time compared to hours by density functional theory (DFT) methods. To demonstrate the utility of the ML predictions, we deploy the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobility anisotropy for more than 60,000 molecular crystal structures and compare the ML predictions to DFT benchmarks.
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