Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

TL;DR

This paper presents a graph neural network-based high-throughput screening framework that efficiently predicts singlet fission properties, enabling rapid discovery of promising photovoltaic molecules with significantly reduced computational costs.

Contribution

The study introduces a novel GNN model trained on existing data to accurately predict excited-state properties, vastly accelerating the identification of efficient singlet fission molecules.

Findings

Achieved 0.1 eV MAE in excitation energy predictions.

Screened over 20 million molecules to find 180 potential SF candidates.

Reduced quantum-chemical validation costs by four orders of magnitude.

Abstract

Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of SF-active molecules. Trained on a previously reported FORMED database, the GNN achieves state-of-the-art accuracy in predicting SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with Time-Dependent Density Functional Theory validation by four orders of magnitude and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The approach presented herein exemplifies an effective strategy for accelerating the discovery of functional molecules with optoelectronic applications.