A high throughput molecular screening for organic electronics via machine learning: present status and perspective

Abstract

Abstract Organic electronics such as organic field-effect transistors (OFET), organic light-emitting diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three decades, largely due to the development of new conjugated materials. Their designs have evolved through incremental modification and stepwise inspiration by researchers; however, a complete survey of the large molecular space is experimentally intractable. Machine learning (ML), based on the rapidly growing field of artificial intelligence technology, offers high throughput material exploration that is more efficient than high-cost quantum chemical calculations. This review describes the present status and perspective of ML-based development (materials informatics) of organic electronics. Although the complexity of OFET, OLED, and OPV makes revealing their structure-property relationships difficult, a cooperative approach incorporating virtual ML, human consideration, and fast experimental screening may help to navigate growth and development in the organic electronics field.