Generative Design and Experimental Validation of Non-Fullerene Acceptors for Photovoltaics

Tan Jin Da, Balamurugan Ramalingam, Kedar Hippalgaonkar

Abstract:

The utilization of non-fullerene acceptors (NFA) in organic
photovoltaic (OPV) devices offers advantages over fullerene-based acceptors,
including lower costs and improved light absorption. Despite advances in small
molecule generative design, experimental validation frameworks are often lacking.
This study introduces a comprehensive pipeline for generating, virtual screening,
and synthesizing potential NFAs for high-efficiency OPVs, integrating generative
and predictive ML models with expert knowledge. Iterative refinement ensured the
synthetic feasibility of the generated molecules, using the diketopyrrolopyrrole
(DPP) core motif to manually generate NFA candidates meeting stringent
synthetic criteria. These candidates were virtually screened using a predictive ML
model based on power conversion efficiency (PCE) calculations from the modified
Scharber model (PCEMS). We successfully synthesized seven NFA candidates, each
requiring three or fewer steps. Experimental HOMO and LUMO measurements
yielded calculated PCEMS values from 6.7% to 11.8%. This study demonstrates an effective pipeline for discovering OPV NFA
candidates by integrating generative and predictive ML models.