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...

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Detalles Bibliográficos
Autores principales: Ghorbani, Alireza, Askari, Amirhossein, Malekan, Mehdi, Nili-Ahmadabadi, Mahmoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
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

Internet

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

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