Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models
Abstract Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structur...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61778-y |
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| _version_ | 1849235115309268992 |
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| author | Zhilong Song Shuaihua Lu Minggang Ju Qionghua Zhou Jinlan Wang |
| author_facet | Zhilong Song Shuaihua Lu Minggang Ju Qionghua Zhou Jinlan Wang |
| author_sort | Zhilong Song |
| collection | DOAJ |
| description | Abstract Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herein, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which utilizes three specialized LLMs to predict the synthesizability of arbitrary 3D crystal structures, possible synthetic methods, and suitable precursors, respectively. We construct a comprehensive dataset including synthesizable/non-synthesizable crystal structures and develop an efficient text representation for crystal structures to fine-tune LLMs. Our Synthesizability LLM achieves state-of-the-art accuracy (98.6%), significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability. Its outstanding generalization ability is further demonstrated in experimental structures with complexity considerably exceeding that of the training data. Furthermore, both the Method and Precursor LLMs exceed 90% accuracy in classifying possible synthetic methods and identifying solid-state synthetic precursors for common binary and ternary compounds, respectively. Leveraging CSLLM, tens of thousands of synthesizable theoretical structures are successfully identified, with their 23 key properties predicted using accurate graph neural network models. |
| format | Article |
| id | doaj-art-baf27e5cfd4f490ca1a9257b87bc6a67 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-baf27e5cfd4f490ca1a9257b87bc6a672025-08-20T04:02:55ZengNature PortfolioNature Communications2041-17232025-07-0116111110.1038/s41467-025-61778-yAccurate prediction of synthesizability and precursors of 3D crystal structures via large language modelsZhilong Song0Shuaihua Lu1Minggang Ju2Qionghua Zhou3Jinlan Wang4Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast UniversityAbstract Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herein, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which utilizes three specialized LLMs to predict the synthesizability of arbitrary 3D crystal structures, possible synthetic methods, and suitable precursors, respectively. We construct a comprehensive dataset including synthesizable/non-synthesizable crystal structures and develop an efficient text representation for crystal structures to fine-tune LLMs. Our Synthesizability LLM achieves state-of-the-art accuracy (98.6%), significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability. Its outstanding generalization ability is further demonstrated in experimental structures with complexity considerably exceeding that of the training data. Furthermore, both the Method and Precursor LLMs exceed 90% accuracy in classifying possible synthetic methods and identifying solid-state synthetic precursors for common binary and ternary compounds, respectively. Leveraging CSLLM, tens of thousands of synthesizable theoretical structures are successfully identified, with their 23 key properties predicted using accurate graph neural network models.https://doi.org/10.1038/s41467-025-61778-y |
| spellingShingle | Zhilong Song Shuaihua Lu Minggang Ju Qionghua Zhou Jinlan Wang Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models Nature Communications |
| title | Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models |
| title_full | Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models |
| title_fullStr | Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models |
| title_full_unstemmed | Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models |
| title_short | Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models |
| title_sort | accurate prediction of synthesizability and precursors of 3d crystal structures via large language models |
| url | https://doi.org/10.1038/s41467-025-61778-y |
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