Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

Abstract

Over past two decades, organic photovoltaics (OPVs) with unique advantages of low cost and flexibility meet significant development opportunities and the official world record for the power conversion efficiency (PCE) of organic solar cells (OSCs) has reached to 17.3%. Traditionally, efficiency breakthrough need the constant input of intensive labor and time. The artificial intelligence, as a rising interdisciplinary, brings certainly a revolution in research methods. In this review, we introduce a state‐of‐art theoretical methodology of the synergy of high‐throughput screening and machine learning (ML) in accelerating the discovery of high‐efficient OSC materials. We present key details, rules and experience in database construction, selection of molecular features, fast‐screening calculations, models training and their predication capabilities. Meanwhile, three typical ML frameworks are concluded to reveal the structure‐property‐efficiency relationship, suggesting that this theoretical methodology can train powerful models with just molecular configurations and theoretical calculations for molecular design and efficiency improvements.