Materials Expert-Artificial Intelligence for materials discovery

Advances in materials databases create an opportunity to uncover descriptors that predict emergent properties, yet most studies rely on high-throughput ab initio calculations that can diverge from experiment. Experimentalists instead depend on intuition honed by hands-on work. We present "Materials Expert-Artificial Intelligence" (ME-AI), a machine-learning framework that translates this intuition into quantitative descriptors extracted from curated, measurement-based data. Using a set of 879 square-net compounds described using 12 experimental features, we train a Dirichlet-based Gaussian-process model with a chemistry-aware kernel. ME-AI reproduces established expert rules for spotting topological semimetals (TSMs) and reveals hypervalency as a decisive chemical lever in these systems. Remarkably, a model trained only on square-net TSM data correctly classifies topological insulators in rocksalt structures, demonstrating transferability. Complementing electronic-structure theory, our framework scales with growing databases, embeds expert knowledge, offers interpretable criteria, and guides targeted synthesis, accelerating materials discovery and rapid experimental validation across diverse chemical families.