Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are of...Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are often complex and expensive in terms of money and time.Machine learning(ML)can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy.Y6 has become the landmark high-performance OSC acceptor material.We collected the power conversion efficiency(PCE)of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors.Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination(R^(2))and Pearson correlation coefficient(r)as the metrics.Among them,decision tree-based algorithms showed excellent predictive capability,especially the Gradient Boosting Regression Tree(GBRT)models based on small molecular donors and polymer donors exhibited that the values of R2are 0.84 and 0.69 for the testing set,respectively.Our work provides a strategy to predict PCEs rapidly,and discovers the influence of the descriptors,thereby being expected to screen high-performance donor material molecules.展开更多
基金financially supported by the National Natural Science Foundation of China(21776067)the Hunan Provincial Distinguished Young Scholars Foundation of China(2020JJ2014)+1 种基金the Hunan Provincial Natural Science Foundation of China(2022JJ30239)the Key Project of Hunan Provincial Education Department,China,No.22A0328。
文摘Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are often complex and expensive in terms of money and time.Machine learning(ML)can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy.Y6 has become the landmark high-performance OSC acceptor material.We collected the power conversion efficiency(PCE)of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors.Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination(R^(2))and Pearson correlation coefficient(r)as the metrics.Among them,decision tree-based algorithms showed excellent predictive capability,especially the Gradient Boosting Regression Tree(GBRT)models based on small molecular donors and polymer donors exhibited that the values of R2are 0.84 and 0.69 for the testing set,respectively.Our work provides a strategy to predict PCEs rapidly,and discovers the influence of the descriptors,thereby being expected to screen high-performance donor material molecules.
