ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.
Relationship between structure, dynamics, and mechanical properties in metallic glass-forming alloys,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cond-mat.mtrl-sci 2years
2026 2representative citing papers
citing papers explorer
-
Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.
- Extraction of a structural short-range order descriptor from nanobeam electron diffraction patterns using a transfer learning approach