CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery

TL;DR

CycleChemist is a comprehensive machine learning framework that integrates predictive models and generative design to accelerate the discovery of high-efficiency organic photovoltaic materials.

Contribution

It introduces a dual ML framework combining predictive modeling and generative design, along with a large curated dataset and multiple specialized models for OPV discovery.

Findings

Largest curated OPV dataset with 2000 donor-acceptor pairs

Accurate prediction of OPV behavior and PCE using developed models

Generation of synthetically accessible OPV candidates with reinforcement learning

Abstract

Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance donor and acceptor pairs with strong power conversion efficiencies (PCEs). Existing design strategies typically focus on either the donor or the acceptor alone, rather than using a unified approach capable of modeling both components. In this work, we introduce a dual machine learning framework for OPV discovery that combines predictive modeling with generative molecular design. We present the Organic Photovoltaic Donor Acceptor Dataset (OPV2D), the largest curated dataset of its kind, containing 2000 experimentally characterized donor acceptor pairs. Using this dataset, we develop the Organic Photovoltaic Classifier (OPVC) to predict whether a material exhibits OPV behavior, and a hierarchical graph neural network that incorporates multi task learning and donor acceptor interaction modeling. This framework includes the Molecular Orbital Energy Estimator (MOE2) for predicting HOMO and LUMO energy levels, and the Photovoltaic Performance Predictor (P3) for estimating PCE. In addition, we introduce the Material Generative Pretrained Transformer (MatGPT) to produce synthetically accessible organic semiconductors, guided by a reinforcement learning strategy with three objective policy optimization. By linking molecular representation learning with performance prediction, our framework advances data driven discovery of high performance OPV materials.