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Quick Start

step_count: 5· runtime: —

Five-step workflow covering the full Matbench benchmark methodology: from linear baselines through gradient boosting (the key tabular benchmark) to random forest stability classification. GNN-based engines (CGCNN, MEGNet, MACE-MP, CHGNet) are cataloged but require external PyTorch infrastructure to run.

// pipeline

5 steps

ridge_baseline_band_gap

engine ridge_regression

gradient_boosting_formation_energy

engine gradient_boosting

band_gap_regression

engine ols_regression

stability_classification

engine logistic_regression

rf_stability

engine random_forest

// from pax

F field Machine learning for computational materials discovery

ML for Materials Discovery

// engines

engine.gradient_boostingengine.ridge_regressionengine.ols_regressionengine.logistic_regressionengine.random_forest

// note

step bodies extracted from the .pax archive at build time. download the parent pax for the full yaml.

[ download ml-materials-discovery.pax.tar.gz ]

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