Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization

Organic semiconductors require both high carrier mobility and structural diversity, but direct first-principles evaluation is costly and brute-force exploration of chemical space is infeasible. We propose a data-driven framework that combines a hierarchical variational autoencoder (HVAE), Gaussian mixture regression (GMR), and Bayesian optimization to design small molecules exhibiting low reorganization energy and high carrier mobility. An HVAE was trained to learn a latent representation of organic semiconductor-like molecules, and GMR linked latent variables to hole and electron reorganization energies. By (i) adding random noise around low-reorganization energy molecules in the latent space and (ii) sampling from a Gaussian mixture model fitted to their latent distribution, we generated structurally reasonable candidates under constraints on ring number and molecular size, including a molecule with a new minimum hole reorganization energy obtained via sulfur-to-nitrogen substitution. Subsequent Bayesian optimization using reorganization energy and a small set of structural descriptors identified molecules with high mobilities and confirmed reorganization energy as a key descriptor for both hole and electron mobility.