Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials

Designing
efficient organic photovoltaic (OPV) materials purposefully
is still challenging and time-consuming. It is of paramount importance
in material development to identify basic functional units that play
the key roles in material performance and subsequently establish the
substructure–property relationship. Herein, we describe an
automatic design framework based on an in-house designed La FREMD
Fingerprint and machine learning (ML) algorithms for highly efficient
OPV donor molecules. The key building blocks are identified, and a
library consisting of 18 960 new molecules is generated within
this framework. Through investigating the chemical structures of materials
with different performance, a guidance on designing efficient OPV
materials is proposed. Furthermore, the most promising candidates
exhibit a predicted power conversion efficiency (PCE) value of over
15% when combined with acceptor Y6. Density functional theory (DFT)
studies show these candidate materials possess exceptional potential
for efficient charge carrier transport. The proposed framework demonstrates
the ability to design new materials based on the substructure–property
relationship built by ML, which provides an alternative methodology
for applying ML in new material discovery.