Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties, supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work. We compare results for features derived from easy-...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Journal of Materiomics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235284782400203X |
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| author | Lane E. Schultz Benjamin Afflerbach Paul M. Voyles Dane Morgan |
| author_facet | Lane E. Schultz Benjamin Afflerbach Paul M. Voyles Dane Morgan |
| author_sort | Lane E. Schultz |
| collection | DOAJ |
| description | We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties, supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work. We compare results for features derived from easy-to-compute functions of elemental properties to more complex physically motivated properties using ab initio, machine-learning potentials, and empirical potential molecular dynamics methods. The established approach enables property acquisition across a diverse range of alloys. Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features. The elemental property based feature is an ideal entropy value based on alloy stoichiometry. The simulated features were acquired from estimates of energies above the convex hull, changes in heat capacity, and the fraction of icosahedra-like Voronoi polyhedra. Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an R2 of 0.78 and a mean average error of 0.76 in units of lg(K/s). We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions. The established methodology can be applied to other high-throughput studies of material properties of diverse compositions. |
| format | Article |
| id | doaj-art-c81bcb8ebd5f4f04974fca0220b0cab8 |
| institution | Kabale University |
| issn | 2352-8478 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materiomics |
| spelling | doaj-art-c81bcb8ebd5f4f04974fca0220b0cab82025-08-20T03:44:28ZengElsevierJournal of Materiomics2352-84782025-07-0111410096410.1016/j.jmat.2024.100964Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurizationLane E. Schultz0Benjamin Afflerbach1Paul M. Voyles2Dane Morgan3Corresponding author.; University of Wisconsin-Madison, 1500 Engineering Drive, Madison, WI, 53706, USAUniversity of Wisconsin-Madison, 1500 Engineering Drive, Madison, WI, 53706, USAUniversity of Wisconsin-Madison, 1500 Engineering Drive, Madison, WI, 53706, USAUniversity of Wisconsin-Madison, 1500 Engineering Drive, Madison, WI, 53706, USAWe have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties, supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work. We compare results for features derived from easy-to-compute functions of elemental properties to more complex physically motivated properties using ab initio, machine-learning potentials, and empirical potential molecular dynamics methods. The established approach enables property acquisition across a diverse range of alloys. Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features. The elemental property based feature is an ideal entropy value based on alloy stoichiometry. The simulated features were acquired from estimates of energies above the convex hull, changes in heat capacity, and the fraction of icosahedra-like Voronoi polyhedra. Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an R2 of 0.78 and a mean average error of 0.76 in units of lg(K/s). We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions. The established methodology can be applied to other high-throughput studies of material properties of diverse compositions.http://www.sciencedirect.com/science/article/pii/S235284782400203XGlassesMetalsAlloyPotentialMachine learningMolecular dynamics |
| spellingShingle | Lane E. Schultz Benjamin Afflerbach Paul M. Voyles Dane Morgan Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization Journal of Materiomics Glasses Metals Alloy Potential Machine learning Molecular dynamics |
| title | Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| title_full | Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| title_fullStr | Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| title_full_unstemmed | Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| title_short | Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| title_sort | machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization |
| topic | Glasses Metals Alloy Potential Machine learning Molecular dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S235284782400203X |
| work_keys_str_mv | AT laneeschultz machinelearningmetallicglasscriticalcoolingratesthroughelementalandmolecularsimulationbasedfeaturization AT benjaminafflerbach machinelearningmetallicglasscriticalcoolingratesthroughelementalandmolecularsimulationbasedfeaturization AT paulmvoyles machinelearningmetallicglasscriticalcoolingratesthroughelementalandmolecularsimulationbasedfeaturization AT danemorgan machinelearningmetallicglasscriticalcoolingratesthroughelementalandmolecularsimulationbasedfeaturization |