Positive-unlabeled (PU) synthesizability scorers for inorganic compositions. Trained on 109,283 Materials Project entries (49,283 labeled positives, 60,000 unlabeled). Features: ORB-v3 embeddings reduced by PCA, Magpie composition descriptors, and an optional ORB-energy stability scalar. The estimator is a Mordelet–Vert PU bagging classifier with an optional abstention / out-of-distribution layer (AbstainingPUClassifier).

This private repository holds the Optuna-tuned checkpoints from eight studies: {Magpie, ORB, ORB+Magpie, ORB+Magpie+stability} × {RandomForest, XGBoost}.

Selected model: apu_optuna/orb_mag__xgboost/model.joblib (held-out MP test split: AUPRC 0.961, AUROC 0.967, ECE 0.024).

Code, results, figures, and the full write-up are in https://github.com/sheikhahnaf/matinvent-BO under hcap_bo/ (see syn_finding.md).

Unpickling requires the apu_synthesizability package (matinvent-BO/hcap_bo/src) and its dependencies (scikit-learn, xgboost, joblib, numpy). Each study is reproducible from hcap_bo/slurm/apu_optuna.slurm (seed 42).