Generative artificial intelligence and its applications in materials science: Current situation and future perspectives
Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to ge...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Journal of Materiomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352847823000771 |
| _version_ | 1797783567728640000 |
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| author | Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi |
| author_facet | Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi |
| author_sort | Yue Liu |
| collection | DOAJ |
| description | Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development. |
| first_indexed | 2024-03-13T00:27:43Z |
| format | Article |
| id | doaj.art-0de75964923b4f47a60e8d4b928ee91e |
| institution | Directory Open Access Journal |
| issn | 2352-8478 |
| language | English |
| last_indexed | 2024-03-13T00:27:43Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materiomics |
| spelling | doaj.art-0de75964923b4f47a60e8d4b928ee91e2023-07-11T04:06:29ZengElsevierJournal of Materiomics2352-84782023-07-0194798816Generative artificial intelligence and its applications in materials science: Current situation and future perspectivesYue Liu0Zhengwei Yang1Zhenyao Yu2Zitu Liu3Dahui Liu4Hailong Lin5Mingqing Li6Shuchang Ma7Maxim Avdeev8Siqi Shi9School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Shanghai Engineering Research Center of Intelligent Computing System, Shanghai, 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, ChinaState Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, ChinaState Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, ChinaAustralian Nuclear Science and Technology Organisation, Sydney, 2232, Australia; School of Chemistry, The University of Sydney, Sydney, 2006, AustraliaState Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China; Materials Genome Institute, Shanghai University, Shanghai, 200444, China; Corresponding author. State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China.Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.http://www.sciencedirect.com/science/article/pii/S2352847823000771Machine learningArtificial intelligenceGenerative artificial intelligenceMaterials scienceNovel materials discoveryDeep learning |
| spellingShingle | Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi Generative artificial intelligence and its applications in materials science: Current situation and future perspectives Journal of Materiomics Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning |
| title | Generative artificial intelligence and its applications in materials science: Current situation and future perspectives |
| title_full | Generative artificial intelligence and its applications in materials science: Current situation and future perspectives |
| title_fullStr | Generative artificial intelligence and its applications in materials science: Current situation and future perspectives |
| title_full_unstemmed | Generative artificial intelligence and its applications in materials science: Current situation and future perspectives |
| title_short | Generative artificial intelligence and its applications in materials science: Current situation and future perspectives |
| title_sort | generative artificial intelligence and its applications in materials science current situation and future perspectives |
| topic | Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352847823000771 |
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