随机森林
有机太阳能电池
计算机科学
机器学习
吞吐量
人工智能
虚拟筛选
光伏系统
集合(抽象数据类型)
过程(计算)
支持向量机
大数据
功率(物理)
高通量筛选
选择(遗传算法)
算法
数据挖掘
药物发现
工程类
化学
电气工程
物理
无线
程序设计语言
操作系统
电信
量子力学
生物化学
作者
Yao Wu,Jin Guo,Rui Sun,Jie Min
标识
DOI:10.1038/s41524-020-00388-2
摘要
Abstract Integrating artificial intelligence (AI) and computer science together with current approaches in material synthesis and optimization will act as an effective approach for speeding up the discovery of high-performance photoactive materials in organic solar cells (OSCs). Yet, like model selection in statistics, the choice of appropriate machine learning (ML) algorithms plays a vital role in the process of new material discovery in databases. In this study, we constructed five common algorithms, and introduced 565 donor/acceptor (D/A) combinations as training data sets to evaluate the practicalities of these ML algorithms and their application potential when guiding material design and D/A pairs screening. Thus, the best predictive capabilities are provided by using the random forest (RF) and boosted regression trees (BRT) approaches beyond other ML algorithms in the data set. Furthermore, >32 million D/A pairs were screened and calculated by RF and BRT models, respectively. Among them, six photovoltaic D/A pairs are selected and synthesized to compare their predicted and experimental power conversion efficiencies. The outcome of ML and experiment verification demonstrates that the RF approach can be effectively applied to high-throughput virtual screening for opening new perspectives to design of materials and D/A pairs, thereby accelerating the development of OSCs.
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