AI assists in design of corrosion-resistant alloys
Alloy design can potentially benefit from artificial intelligence (AI). Within the broader context of advanced materials design, researchers have explored the creation of various alloys, including ferrous, high-entropy materials, and nonferrous metallic compositions, employing data-driven methodolog...
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| Format: | Article |
| Language: | English |
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Springer International Publishing
2023
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| Online Access: | https://hdl.handle.net/1721.1/153204 |
| _version_ | 1811072195522199552 |
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| author | Venugopal, Vineeth |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Venugopal, Vineeth |
| author_sort | Venugopal, Vineeth |
| collection | MIT |
| description | Alloy design can potentially benefit from artificial intelligence (AI). Within the broader context of advanced materials design, researchers have explored the creation of various alloys, including ferrous, high-entropy materials, and nonferrous metallic compositions, employing data-driven methodologies. However, a significant hurdle in these endeavors has been the scarcity of extensive, machine-readable databases suitable for training AI models. To address this challenge, researchers have turned to text mining and autonomous data extraction from literature sources. However, a major challenge with this approach is that the essential materials properties and features are not necessarily known, and must be obtained from textual sources to enhance the predictive accuracy of these models. |
| first_indexed | 2024-09-23T09:02:12Z |
| format | Article |
| id | mit-1721.1/153204 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:02:12Z |
| publishDate | 2023 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1532042024-01-03T21:38:39Z AI assists in design of corrosion-resistant alloys Venugopal, Vineeth Massachusetts Institute of Technology. Department of Materials Science and Engineering Alloy design can potentially benefit from artificial intelligence (AI). Within the broader context of advanced materials design, researchers have explored the creation of various alloys, including ferrous, high-entropy materials, and nonferrous metallic compositions, employing data-driven methodologies. However, a significant hurdle in these endeavors has been the scarcity of extensive, machine-readable databases suitable for training AI models. To address this challenge, researchers have turned to text mining and autonomous data extraction from literature sources. However, a major challenge with this approach is that the essential materials properties and features are not necessarily known, and must be obtained from textual sources to enhance the predictive accuracy of these models. 2023-12-19T15:06:55Z 2023-12-19T15:06:55Z 2023-12-06 2023-12-19T04:39:12Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153204 Venugopal, Vineeth. 2023. "AI assists in design of corrosion-resistant alloys." MRS Bulletin, 48. en https://doi.org/10.1557/s43577-023-00633-1 MRS Bulletin Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The Author(s), under exclusive License to the Materials Research Society application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Venugopal, Vineeth AI assists in design of corrosion-resistant alloys |
| title | AI assists in design of corrosion-resistant alloys |
| title_full | AI assists in design of corrosion-resistant alloys |
| title_fullStr | AI assists in design of corrosion-resistant alloys |
| title_full_unstemmed | AI assists in design of corrosion-resistant alloys |
| title_short | AI assists in design of corrosion-resistant alloys |
| title_sort | ai assists in design of corrosion resistant alloys |
| url | https://hdl.handle.net/1721.1/153204 |
| work_keys_str_mv | AT venugopalvineeth aiassistsindesignofcorrosionresistantalloys |