Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach

A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learnin...

Full description

Autores:

Tzu-Chia, Chen
Rajiman, Rajiman
Elveny, Marischa
Grimaldo Guerrero, John William
Lawal, Adedoyin Isola
Acwin Dwijendra, Ngakan Ketut
aravindhan, surendar
Danshina, Svetlana
ZHU, Yu

Tipo de recurso:

Article of journal

Fecha de publicación:

2021

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

spa

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/8642

Acceso en línea:

https://hdl.handle.net/11323/8642
https://doi.org/10.1007/s13369-021-05966-0
https://repositorio.cuc.edu.co/

Palabra clave:

Bulk metallic glass
Glass-forming ability
Machine learning
Materials design

Rights

embargoedAccess

License

CC0 1.0 Universal