Diffusion Generative Models for Designing Efficient Singlet Fission Dimers.

Diffusion generative models, a class of machine learning techniques, have shown remarkable promise in materials science and chemistry by enabling the precise generation of complex molecular structures. In this article, we propose a novel application of diffusion generative models for stabilizing reactive molecular structures identified through quantum mechanical screening. Specifically, we focus on the design challenge presented by singlet fission (SF), a phenomenon crucial for advancing solar cell efficiency beyond theoretical limits.