PDGPT: A large language model for acquiring phase diagram information in magnesium alloys
Abstract Magnesium alloys, known for their lightweight advantages, are increasingly in demand across a range of applications, from aerospace to the automotive industry. With rising requirements for strength and corrosion resistance, the development of new magnesium alloy systems has become critical....
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.77 |
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| _version_ | 1841543274742939648 |
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| author | Zini Yan Hongyu Liang Jingya Wang Hongbin Zhang Alisson Kwiatkowski da Silva Shiyu Liang Ziyuan Rao Xiaoqin Zeng |
| author_facet | Zini Yan Hongyu Liang Jingya Wang Hongbin Zhang Alisson Kwiatkowski da Silva Shiyu Liang Ziyuan Rao Xiaoqin Zeng |
| author_sort | Zini Yan |
| collection | DOAJ |
| description | Abstract Magnesium alloys, known for their lightweight advantages, are increasingly in demand across a range of applications, from aerospace to the automotive industry. With rising requirements for strength and corrosion resistance, the development of new magnesium alloy systems has become critical. Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability, composition, and temperature ranges, enabling the optimization of alloy properties and processing conditions. However, accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time‐consuming, often requiring intricate calculations and iterative refinement based on thermodynamic models. To address this challenge, we introduce PDGPT, a ChatGPT‐based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy. Enhanced by prompt‐engineering, supervised fine‐tuning and retrieval‐augmented generation, PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data. By combining large language models with traditional phase diagram research tools, PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science. |
| format | Article |
| id | doaj-art-a773d498d6e34a4d869170a6f14c31e9 |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-a773d498d6e34a4d869170a6f14c31e92025-01-13T15:15:31ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-12-0124n/an/a10.1002/mgea.77PDGPT: A large language model for acquiring phase diagram information in magnesium alloysZini Yan0Hongyu Liang1Jingya Wang2Hongbin Zhang3Alisson Kwiatkowski da Silva4Shiyu Liang5Ziyuan Rao6Xiaoqin Zeng7National Engineering Research Center of Light Alloy Net Forming School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai ChinaJohn Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai ChinaNational Engineering Research Center of Light Alloy Net Forming School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai ChinaInstitut für Materialwissenschaft Technische Universität Darmstadt Darmstadt GermanyThermo‐Calc Software AB Stockholm SwedenJohn Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai ChinaNational Engineering Research Center of Light Alloy Net Forming School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai ChinaNational Engineering Research Center of Light Alloy Net Forming School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai ChinaAbstract Magnesium alloys, known for their lightweight advantages, are increasingly in demand across a range of applications, from aerospace to the automotive industry. With rising requirements for strength and corrosion resistance, the development of new magnesium alloy systems has become critical. Phase diagrams play a crucial role in guiding the magnesium alloy design by providing key insights into phase stability, composition, and temperature ranges, enabling the optimization of alloy properties and processing conditions. However, accessing and interpreting phase diagram data with thermodynamic calculation software can be complex and time‐consuming, often requiring intricate calculations and iterative refinement based on thermodynamic models. To address this challenge, we introduce PDGPT, a ChatGPT‐based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy. Enhanced by prompt‐engineering, supervised fine‐tuning and retrieval‐augmented generation, PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data. By combining large language models with traditional phase diagram research tools, PDGPT not only improves the accessibility of critical phase diagram information but also sets the stage for future advancements in applying large language models to materials science.https://doi.org/10.1002/mgea.77large language modelphase diagram predictionprompt‐engineeringretrieval‐augmented generationsupervised fine‐tuning |
| spellingShingle | Zini Yan Hongyu Liang Jingya Wang Hongbin Zhang Alisson Kwiatkowski da Silva Shiyu Liang Ziyuan Rao Xiaoqin Zeng PDGPT: A large language model for acquiring phase diagram information in magnesium alloys Materials Genome Engineering Advances large language model phase diagram prediction prompt‐engineering retrieval‐augmented generation supervised fine‐tuning |
| title | PDGPT: A large language model for acquiring phase diagram information in magnesium alloys |
| title_full | PDGPT: A large language model for acquiring phase diagram information in magnesium alloys |
| title_fullStr | PDGPT: A large language model for acquiring phase diagram information in magnesium alloys |
| title_full_unstemmed | PDGPT: A large language model for acquiring phase diagram information in magnesium alloys |
| title_short | PDGPT: A large language model for acquiring phase diagram information in magnesium alloys |
| title_sort | pdgpt a large language model for acquiring phase diagram information in magnesium alloys |
| topic | large language model phase diagram prediction prompt‐engineering retrieval‐augmented generation supervised fine‐tuning |
| url | https://doi.org/10.1002/mgea.77 |
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