工作流程
计算机科学
直觉
有机太阳能电池
化学空间
实验数据
机器学习
人工智能
光伏
光伏系统
工程类
生物
统计
认识论
电气工程
数据库
药物发现
哲学
生物信息学
数学
作者
Z. G. Zhao,Yun Geng,Alessandro Troisi,Jing Ma
标识
DOI:10.1002/aisy.202100261
摘要
The improvements of organic photovoltaics (OPVs) are mainly implemented by the design of novel materials and optimizations of experimental conditions through extensive trial-and-error experiments based on chemical intuition, which may be tedious and inefficient for exploring a larger chemical space. In the recent five years, data-driven methods using machine learning (ML) algorithms and the knowledge of known materials/experimental parameters are introduced to OPV studies to help build a quantitative structure-property relationship model and accelerate the molecular design and parameter optimization. Here, these recent promising progresses based on experimental OPV datasets are summarized. This review introduces the general workflow (e.g., dataset collection, feature engineering, ML model generation, and evaluation) of ML-OPV projects and discusses the applications of this framework for predicting OPV performance and experimental optimizations in OPVs. Finally, an outlook of future work directions in this exciting and quickly developing field is presented.
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