| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
Machine Learning Feature Selection for Predicting Corrosion Rates In High-Entropy Alloys |
| Author(s) |
Mohammad Fuad Nur Taufique, Ankit Roy, Ganesh Balasubramanian, Gaoyuan Ouyang, Duane D. Johnson, Ram Devanathan |
| On-Site Speaker (Planned) |
Mohammad Fuad Nur Taufique |
| Abstract Scope |
More than $270 billion is spent on combatting corrosion annually in the USA alone. This work uses machine learning to meet the urgent need for the development of highly corrosion resistant alloys. The focus is on a new class of alloys called high-entropy alloys (HEAs) as a potential solution. Some HEAs have exhibited excellent mechanical properties even at high temperatures but the caveat associated is that their search space is almost up to half a trillion combinations. To counter this challenge, we employ machine learning tools to develop a model that predicts the corrosion resistance of any given HEA, based upon the existing corrosion data available on HEAs. Such a model reveals important features that determine the corrosion resistance of a given alloy and serves as a swift tool for screening a vast number of HEAs and selecting the best corrosion resistant HEAs. |
| Proceedings Inclusion? |
Definite: Other |