Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses
Crossref DOI link: https://doi.org/10.1038/s41598-022-15981-2
Published Online: 2022-07-11
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Ghorbani, Alireza
Askari, Amirhossein
Malekan, Mehdi
Nili-Ahmadabadi, Mahmoud
Text and Data Mining valid from 2022-07-11
Version of Record valid from 2022-07-11
Article History
Received: 29 April 2022
Accepted: 1 July 2022
First Online: 11 July 2022
Competing interests
: The authors declare no competing interests.