Nonlinear optical (NLO) materials are essential for photonic technologies, yet discovering optimal NLO molecules requires balancing multiple competing objectives across vast chemical spaces. Previous work showed that Multi-Objective MAP-Elites (MOME) with grid-based archives discovers diverse, high-quality molecules for electro-optic applications. However, uniform grid partitioning wastes archive capacity on chemically infeasible regions while undersampling high-density areas. We apply MOME with Centroidal Voronoi Tessellation (CVT) archives whose cells are defined by learned embeddings from ChemBERTa-2 Multi-Task Regression reduced via UMAP, capturing chemical similarity beyond simple structural features. We investigate a four-objective NLO molecular design problem: maximizing the β/γ hyperpolarizability ratio, constraining HOMO-LUMO gap and linear polarizability to target ranges, and minimizing energy per atom. Our results demonstrate that embedding-based measures in CVT archives yield significantly higher median global hypervolume and multi-objective quality diversity scores, while filling nearly all native archive niches.
@article{arxiv.2604.05622,
title = {CVT Archives and Chemical Embedding Measures for Multi-Objective Quality Diversity in Molecular Design},
author = {Dominic Mashak and Jacob Schrum},
journal= {arXiv preprint arXiv:2604.05622},
year = {2026}
}