Cargando…
Phase prediction and experimental realisation of a new high entropy alloy using machine learning
Nearly ~ 10(8) types of High entropy alloys (HEAs) can be developed from about 64 elements in the periodic table. A major challenge for materials scientists and metallurgists at this stage is to predict their crystal structure and, therefore, their mechanical properties to reduce experimental effort...
Autores principales: | Singh, Swati, Katiyar, Nirmal Kumar, Goel, Saurav, Joshi, Shrikrishna N. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036487/ https://www.ncbi.nlm.nih.gov/pubmed/36959265 http://dx.doi.org/10.1038/s41598-023-31461-7 |
Ejemplares similares
-
Machine learning-based prediction of phases in high-entropy alloys: A data article
por: Machaka, Ronald, et al.
Publicado: (2021) -
Phase Prediction of High-Entropy Alloys by Integrating Criterion and Machine Learning Recommendation Method
por: Hou, Shuai, et al.
Publicado: (2022) -
Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning
por: He, Zhongping, et al.
Publicado: (2022) -
Retracted: Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning
por: Neuroscience, Computational Intelligence and
Publicado: (2022) -
A machine learning framework for discovering high entropy alloys phase formation drivers
por: Syarif, Junaidi, et al.
Publicado: (2023)