Machine learning assisted design of reactor steels with high long-term strength and toughness
The search for new materials with unique properties is one of the most critical challenges in modern materials science. Steels are among the most important, as they are used in many areas of life, including aerospace, oil and gas, nuclear energy, and various other high-tech industries. Typically, ne...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525004344 |
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| Summary: | The search for new materials with unique properties is one of the most critical challenges in modern materials science. Steels are among the most important, as they are used in many areas of life, including aerospace, oil and gas, nuclear energy, and various other high-tech industries. Typically, new steels were created experimentally by trial and error. However, the growth of steels databases and the development of machine learning algorithm can greatly speed up the search for new steels and lead to the previously unknown materials with target properties. In this paper, we used machine learning algorithms to predict the properties of austenitic steels such as yield strength, long-term strength and toughness, which are extremely significant for nuclear power plants. In order to find steels with optimal properties, we collected the database of steels and their properties, trained machine learning models and developed optimization algorithm. Using it, we found 5 new steels, which mechanical properties were better than those used in the industry. Composition and mechanical properties of newly found steels were verified in experiment. We found a good agreement between the experimental values of mechanical properties and the values predicted by the machine learning model. |
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| ISSN: | 0264-1275 |