Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys
| Autor: | Gaoyuan Ouyang, Ganesh Balasubramanian, M. F. N. Taufique, Ram Devanathan, Jun Cui, Hrishabh Khakurel, Ankit Roy, Duane D. Johnson |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Theory and computation
Multidisciplinary Property (programming) Computer science business.industry Science Modulus Feature selection Young's modulus Machine learning computer.software_genre Structural materials Article symbols.namesake Atomic radius Scalability symbols Medicine Gradient boosting Artificial intelligence Valence electron business computer |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
| ISSN: | 2045-2322 |
| Popis: | We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications. |
| Databáze: | OpenAIRE |
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