bajpaianurag/Dmax-certified-inverse-design: Python Codes and Dataset
This release provides an integrated Python workflow for predicting glass-forming ability and designing bulk metallic glass compositions under bounded compositional drift. The workflow combines machine learning, conformal calibration, composition-neighborhood sampling, and Bayesian optimization in a single script. The target property is the critical casting diameter, Dmax. Candidate compositions are ranked using a calibrated, drift-aware lower-bound estimate that accounts for changes in model predictions under mass-conserving composition perturbations. The outputs are calibrated, model-based design estimates whose interpretation depends on the
Resource information
- Format
- zip
- Original repository
- 23566419
- DOI
- 10.5281/zenodo.23566419
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- mit-license
- Last record update
- Nov 5, 2026
About this resource
This release provides an integrated Python workflow for predicting glass-forming ability and designing bulk metallic glass compositions under bounded compositional drift. The workflow combines machine learning, conformal calibration, composition-neighborhood sampling, and Bayesian optimization in a single script. The target property is the critical casting diameter, Dmax. Candidate compositions are ranked using a calibrated, drift-aware lower-bound estimate that accounts for changes in model predictions under mass-conserving composition perturbations. The outputs are calibrated, model-based design estimates whose interpretation depends on the calibration assumptions and the sampled composition neighborhoods. Casting outcomes at specified diameters are recorded as experimental phase observations rather than automatically treated as exact critical casting diameters.