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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15761989 |
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| author | Sharma, Himanshu Jain, Relaince Rao, K. Raja |
| author_facet | Sharma, Himanshu Jain, Relaince Rao, K. Raja |
| contents | <p> High-entropy alloys (HEAs) offer remarkable properties in the areas of mechanics, temperature and chemistry. While <br>HEAs have a wide range of possible material compositions, both exploring and identifying them proves difficult for traditional <br>processes. ML methods have proven to be highly effective in speeding up materials research and optimization by exploring the <br>links between different compositions, structures and the related properties. It reviews the interaction between machine learning <br>(ML) and HEA research, pointing out successful applications along the materials creation process. It reviews several ML <br>methods used in developing HEA systems, such as supervised learning for forecasting properties, unsupervised learning for <br>detecting patterns, reinforcement learning for optimizing results and active learning for saving time during experimentation. The <br>review looks at the present obstacles, difficulties and potential paths forward for research involving ML in HEA. Using ML <br>along with physics and experiments greatly speeds up the search for new HEAs useful for many purposes. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15761989 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AREVIEWOFMACHINE-LEARNINGSYNERGYINHIGH-ENTROPYALLOYS Sharma, Himanshu Jain, Relaince Rao, K. Raja High-entropy alloys; Machine learning Materials discovery Property prediction; Composition structure-property relationships Materials informatics <p> High-entropy alloys (HEAs) offer remarkable properties in the areas of mechanics, temperature and chemistry. While <br>HEAs have a wide range of possible material compositions, both exploring and identifying them proves difficult for traditional <br>processes. ML methods have proven to be highly effective in speeding up materials research and optimization by exploring the <br>links between different compositions, structures and the related properties. It reviews the interaction between machine learning <br>(ML) and HEA research, pointing out successful applications along the materials creation process. It reviews several ML <br>methods used in developing HEA systems, such as supervised learning for forecasting properties, unsupervised learning for <br>detecting patterns, reinforcement learning for optimizing results and active learning for saving time during experimentation. The <br>review looks at the present obstacles, difficulties and potential paths forward for research involving ML in HEA. Using ML <br>along with physics and experiments greatly speeds up the search for new HEAs useful for many purposes. </p> |
| title | AREVIEWOFMACHINE-LEARNINGSYNERGYINHIGH-ENTROPYALLOYS |
| topic | High-entropy alloys; Machine learning Materials discovery Property prediction; Composition structure-property relationships Materials informatics |
| url | https://doi.org/10.5281/zenodo.15761989 |