materials-featurization

Materials Featurization

Description

This skill covers the conversion of atomic structures, compositions, and trajectories into numerical feature vectors for physical-science ML: composition featurizers (Magpie-style elemental statistics, oxidation-state features), structure featurizers (SOAP, Coulomb matrix, Voronoi, radial distribution functions, bond orientational order), site-level featurizers with structure-level aggregation, and descriptor selection for crystals, molecules, surfaces, defects, amorphous structures, and MD trajectories. Covers matminer, pymatgen, ASE, DScribe, and RDKit. Invoke this skill when building a feature matrix for regression or classification on materials datasets, when selecting between hand-crafted descriptors and learned graph representations, or when auditing a feature pipeline for leakage, NaN patterns, or physical inconsistencies.

Domain Context

Materials featurization is the translation of atomic structures and compositions into vectors of numbers that a statistical model can process. Unlike image or text data where pixels and tokens have a natural numerical representation, materials structures have no canonical encoding: a crystal can be described by its unit cell (arbitrary choice of conventional vs. primitive cell), its composition (continuous or categorical species fractions), its local environments (per-site descriptors aggregated to structure level), or its global topology (graph connectivity). Each encoding captures different physical information and has different sensitivity to symmetry operations, cell conventions, and disorder.

Hand-crafted descriptors vs. learned representations. Composition-based descriptors (Magpie, CGCNN input) require only element symbols and fractions — cheap to compute, insensitive to structural details, and appropriate for screening or when structures are unavailable. Structure-based descriptors (SOAP, Voronoi, Coulomb matrix) use full atomic positions but are O(N²) or worse for global descriptors. Learned graph neural network representations (CGCNN, MEGNet, MatFormer) jointly learn the featurization and the regression, but require training data, are less interpretable, and cannot be used as feature vectors for arbitrary downstream models without fine-tuning. The choice between them depends on data quantity, interpretability requirements, computational budget, and whether the target property is dominated by local chemistry (SOAP is then sufficient) or long-range interactions (learned models with long-range attention are preferable).