有机太阳能电池
接受者
材料科学
集合(抽象数据类型)
领域(数学分析)
光伏系统
计算机科学
组分(热力学)
工作(物理)
人工智能
生物系统
热力学
数学
物理
聚合物
程序设计语言
复合材料
数学分析
生物
凝聚态物理
生态学
作者
Daniele Padula,Alessandro Troisi
标识
DOI:10.1002/aenm.201902463
摘要
Abstract In this work an instance of the general problem occurring when optimizing multicomponent materials is treated: can components be optimized separately or the optimization should occur simultaneously? This problem is investigated from a computational perspective in the domain of donor–acceptor pairs for organic photovoltaics, since most experimental research reports optimization of each component separately. A collection of organic donors and acceptors recently analyzed is used to train nonlinear machine learning models of different families to predict the power conversion efficiency of donor–acceptor pairs, considering computed electronic and structural parameters of both components. The trained models are then used to predict photovoltaic performance for donor–acceptor combinations for which experimental data are not available in the data set. Data structure, and the usefulness of the trained models are critically assessed by predicting some donor–acceptor pairs that recently appeared in the literature, and the best combinations are proposed as worth investigating experimentally.
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