Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search

TL;DR

This paper introduces a symmetry-aware Monte Carlo Tree Search method for generating diverse low-bandgap molecules with targeted optoelectronic properties, validated by computational calculations, advancing molecular design for optoelectronic applications.

Contribution

It presents a novel symmetry-constrained generative approach using patent-mined datasets and MCTS to design molecules with desired properties, addressing synthetic accessibility challenges.

Findings

Generated molecules exhibit red-shifted absorption as predicted by TD-DFT.

The method effectively incorporates symmetry constraints from real datasets.

Generated candidates show potential for optoelectronic applications.

Abstract

Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which have significantly improved the power conversion efficiency (PCE) of solar cells and enhanced spectral coverage in the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations.