Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics

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.

Automated summary

The improvements in organic photovoltaics (OPVs) have been accelerated by the introduction of data-driven methods using machine learning algorithms and known materials/experimental parameters knowledge. This has helped in building quantitative structure-property relationship models and speeding up molecular design and parameter optimization in OPV studies. This review summarizes recent promising progresses in experimental OPV datasets and introduces the general workflow of ML-OPV projects, along with their applications for predicting OPV performance and experimental optimizations. Additionally, future work directions in this rapidly developing field are discussed.