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Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses
Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a pro...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273633/ https://www.ncbi.nlm.nih.gov/pubmed/35817887 http://dx.doi.org/10.1038/s41598-022-15981-2 |