Machine learning assisted design of high entropy alloys with desired property
By Cheng Wen, Y. Zhang, Changxin Wang, D. Xue, Yang Bai, S. Antonov, L. Dai, Turab Lookman, Yanjing Su. Acta Materialia, 2019
774 citations recorded by the source corpus.
Abstract We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties.
Subfields: Aerospace Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/cc0d51e0bdb30287a841fb6533cca2c3c24bf91a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Zhiming Li
- Fritz Körmann
- Dierk Raabe
- Xianglin Liu
- Zongrui Pei
- Jiaxin Zhang
- Blazej Grabowski
- R. Ritchie
In Mechanical Engineering
- Dierk Raabe
- Zhiming Li
- D. Ponge
- Fritz Körmann
- Zongrui Pei
- A. Kwiatkowski da Silva
- Xianglin Liu
- Jiaxin Zhang
Citations in the corpus
- Machine learning–enabled high-entropy alloy discovery (2022, methodology): “One conventional ML approach in alloy design is to build a surrogate model that predicts the properties of a given composition, which is then used for candidate inference (9, 10).”
- Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “This has lead to accelerated materials discovery, with notable success stories of superhard ceramics 68 , thermoelectrics 66 , interfaces with high thermal resistance 69 , high-strength high-entropy alloys 26 and metallic glasses 70 .”
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, methodology): “designed HEAs with high hardness assisted by ML models [192].”