瓶颈
灵活性(工程)
主动学习(机器学习)
有机太阳能电池
计算机科学
外推法
机器学习
化学空间
人工智能
材料科学
药物发现
数学
生物
生物信息学
聚合物
复合材料
嵌入式系统
数学分析
统计
作者
Prateek Malhotra,Juan C. Verduzco,Shyamal Biswas,Ganesh D. Sharma
标识
DOI:10.1021/acsami.2c18540
摘要
The structural flexibility of organic semiconductors offers vast a search space, and many potential candidates (donor and acceptor) for organic solar cells (OSCs) are yet to be discovered. Machine learning is extensively used for material discovery but performs poorly on extrapolation tasks with small training data sets. Active learning techniques can guide experimentalists to extrapolate and find the most promising D:A combination in a significantly small number of experiments. This study uses an active learning technique with a predictive random forest model to iteratively find the most optimal D:A combinations in the search space using various acquisition functions. Active learning results with five different acquisition functions (MM, MEI, MLI, MU, and UCB) are compared. Results reveal that acquisition functions that combine exploitation and exploration (MEI, MLI, and UCB) perform far better than purely exploiting (MM) and purely exploring (MU) acquisition functions. Interestingly, the proposed model can overcome the bottleneck of extrapolating small training data sets and find most promising D:A combinations in relatively fewer experiments.
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