Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational...

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.