High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models
Consolidating published research on aluminum alloys into insights about microstructure–property relationships can simplify and reduce the costs involved in alloy design. One critical design consideration for many heat-treatable alloys deriving superior properties from precipitation are phases as key...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer International Publishing
2024
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| Online Access: | https://hdl.handle.net/1721.1/153929 |
| _version_ | 1811095256251236352 |
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| author | Montanelli, Luca Venugopal, Vineeth Olivetti, Elsa A. Latypov, Marat I. |
| author_facet | Montanelli, Luca Venugopal, Vineeth Olivetti, Elsa A. Latypov, Marat I. |
| author_sort | Montanelli, Luca |
| collection | MIT |
| description | Consolidating published research on aluminum alloys into insights about microstructure–property relationships can simplify and reduce the costs involved in alloy design. One critical design consideration for many heat-treatable alloys deriving superior properties from precipitation are phases as key microstructure constituents because they can have a decisive impact on the engineering properties of alloys. Here, we present a computational framework for high-throughput extraction of phases and their impact on properties from scientific papers. Our framework includes transformer-based and large language models to identify sentences with phase-property information in papers, recognize phase and property entities, and extract phase-property relationships and their “sentiment.” We demonstrate the application of our framework on aluminum alloys, for which we build a database of 7,675 phase–property relationships extracted from a corpus of almost 5000 full-text papers. We comment on the extracted relationships based on common metallurgical knowledge. |
| first_indexed | 2024-09-23T16:14:06Z |
| format | Article |
| id | mit-1721.1/153929 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-24T16:23:50Z |
| publishDate | 2024 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1539292024-09-23T04:09:07Z High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models Montanelli, Luca Venugopal, Vineeth Olivetti, Elsa A. Latypov, Marat I. Industrial and Manufacturing Engineering General Materials Science Consolidating published research on aluminum alloys into insights about microstructure–property relationships can simplify and reduce the costs involved in alloy design. One critical design consideration for many heat-treatable alloys deriving superior properties from precipitation are phases as key microstructure constituents because they can have a decisive impact on the engineering properties of alloys. Here, we present a computational framework for high-throughput extraction of phases and their impact on properties from scientific papers. Our framework includes transformer-based and large language models to identify sentences with phase-property information in papers, recognize phase and property entities, and extract phase-property relationships and their “sentiment.” We demonstrate the application of our framework on aluminum alloys, for which we build a database of 7,675 phase–property relationships extracted from a corpus of almost 5000 full-text papers. We comment on the extracted relationships based on common metallurgical knowledge. 2024-03-25T15:25:54Z 2024-03-25T15:25:54Z 2024-03-19 2024-03-24T04:18:05Z Article http://purl.org/eprint/type/JournalArticle 2193-9764 2193-9772 https://hdl.handle.net/1721.1/153929 Montanelli, L., Venugopal, V., Olivetti, E.A. et al. High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models. Integr Mater Manuf Innov (2024). https://doi.org/10.1007/s40192-024-00344-8 PUBLISHER_CC en 10.1007/s40192-024-00344-8 Integrating Materials and Manufacturing Innovation Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Industrial and Manufacturing Engineering General Materials Science Montanelli, Luca Venugopal, Vineeth Olivetti, Elsa A. Latypov, Marat I. High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title | High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title_full | High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title_fullStr | High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title_full_unstemmed | High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title_short | High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models |
| title_sort | high throughput extraction of phase property relationships from literature using natural language processing and large language models |
| topic | Industrial and Manufacturing Engineering General Materials Science |
| url | https://hdl.handle.net/1721.1/153929 |
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