Matminer

Matminer is a sophisticated Python-based toolkit designed to bridge the gap between materials science data and machine learning. As of 2026, it serves as the foundational architecture for Materials Informatics (MI), providing high-level abstractions for converting raw structural and compositional data into machine-learnable numerical descriptors, a process known as featurization. Its technical architecture is built atop the Pymatgen ecosystem, enabling seamless manipulation of crystallographic information files (CIF) and structural objects. Matminer’s core strength lies in its extensive library of over 70 featurizers, ranging from simple stoichiometric descriptors to complex graph-based and Voronoi-tessellation-derived structural features. In the 2026 market, Matminer is positioned as the essential pre-processing layer for autonomous laboratories and generative AI models in material discovery. By standardizing the interface for data retrieval from major databases like the Materials Project and Citrination, it enables reproducible research and rapid prototyping of predictive models for properties such as thermal conductivity, band gaps, and catalytic activity. It is widely adopted by both academic institutions and R&D departments in the battery, semiconductor, and aerospace industries to accelerate the 'Materials Genome' initiative.