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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...

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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:
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
Descripción
Sumario: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 promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (D(max)) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R(2) = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of D(max) for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs.