Physics-Informed Machine Learning Framework for Cross-Family Classification and Inverse Identification of Superconducting Materials from Simulated Nanoindentation Responses — Reproducible Pipeline

Cafer Mert Yeşilkanat, Uğur Kölemen

This deposit is the complete, fully deterministic computational pipeline that reproduces every quantitative result, table, and figure of the associated article by C. M. Yeşilkanat and U. Kölemen. No physical indentation experiments were performed. A physics-informed Monte Carlo forward model — built on the Oliver–Pharr (1992) contact mechanics, the Nix–Gao (1998) indentation-size-effect law, and the Bolshakov–Pharr (1998) unloading/pile-up framework — generates 2,520 physically self-consistent Berkovich load–displacement (P–h) curves across six superconductor families (YBCO, BSCCO, MgB2, BaFe2As2, Nb3Sn, Nb/NbTi). Hardness and reduced-modulus sampling windows are anchored to peer-reviewed indentation measurements. The pipeline then (i) extracts dimensional-analysis curve-shape descriptors, (ii) benchmarks six-family classification (random forest, SVM, histogram gradient boosting; macro-F1 rising from 0.707 on scalar descriptors to 0.894 on curve-shape descriptors), (iii) provides SHAP and UMAP explainability, (iv) evaluates leave-one-family-out generalization with Isolation-Forest anomaly scoring, (v) recovers hardness, reduced modulus, and the indentation-size-effect length h* from a single curve (R2 = 0.986, 0.991, 0.937), (vi) runs an ablation isolating the physics-versus-machine-learning contribution, and (vii) validates the forward model against published BSCCO microindentation scalars. Everything is deterministic under the global random seed 20260719: a single command (python run_all.py) regenerates every number, table, and figure. Requirements: Python >= 3.10 (tested on 3.12); ~5–8 minutes on a standard laptop; no GPU. Released under the MIT license. Contents: code/ (5 Python modules), data/ (descriptor table + all reported scalars as JSON), figures/ (Figures 1–9 as PNG and editable-text SVG), tables/ (all manuscript tables as CSV), and full documentation (README, LICENSE, CITATION.cff, AUTHORS, data provenance/ethics, requirements, changelog).