End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction
Abstract Powder X‐ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse‐grained level. The more difficult and important task of fine‐grained cryst...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202410722 |
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| author | Qingsi Lai Fanjie Xu Lin Yao Zhifeng Gao Siyuan Liu Hongshuai Wang Shuqi Lu Di He Liwei Wang Linfeng Zhang Cheng Wang Guolin Ke |
| author_facet | Qingsi Lai Fanjie Xu Lin Yao Zhifeng Gao Siyuan Liu Hongshuai Wang Shuqi Lu Di He Liwei Wang Linfeng Zhang Cheng Wang Guolin Ke |
| author_sort | Qingsi Lai |
| collection | DOAJ |
| description | Abstract Powder X‐ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse‐grained level. The more difficult and important task of fine‐grained crystal structure prediction from PXRD remains unaddressed. This study introduces XtalNet, the first equivariant deep generative model for end‐to‐end crystal structure prediction from PXRD. Unlike previous crystal structure prediction methods that rely solely on composition, XtalNet leverages PXRD as an additional condition, eliminating ambiguity and enabling the generation of complex organic structures with up to 400 atoms in the unit cell. XtalNet comprises two modules: a Contrastive PXRD‐Crystal Pretraining (CPCP) module that aligns PXRD space with crystal structure space, and a Conditional Crystal Structure Generation (CCSG) module that generates candidate crystal structures conditioned on PXRD patterns. Evaluation on two MOF datasets (hMOF‐100 and hMOF‐400) demonstrates XtalNet's effectiveness. XtalNet achieves a top‐10 Match Rate of 90.2% and 79% for hMOF‐100 and hMOF‐400 in conditional crystal structure prediction task, respectively. XtalNet enables the direct prediction of crystal structures from experimental measurements, eliminating the need for manual intervention and external databases. This opens up new possibilities for automated crystal structure determination and the accelerated discovery of novel materials. |
| format | Article |
| id | doaj-art-13e5cc9a143048d3bce69b9904d8870d |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-13e5cc9a143048d3bce69b9904d8870d2025-08-20T03:09:54ZengWileyAdvanced Science2198-38442025-02-01128n/an/a10.1002/advs.202410722End‐to‐End Crystal Structure Prediction from Powder X‐Ray DiffractionQingsi Lai0Fanjie Xu1Lin Yao2Zhifeng Gao3Siyuan Liu4Hongshuai Wang5Shuqi Lu6Di He7Liwei Wang8Linfeng Zhang9Cheng Wang10Guolin Ke11DP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaDP Technology Beijing 100080 ChinaSchool of Intelligence Science and Technology Peking University Beijing 100871 ChinaCenter for Data Science Peking University Beijing 100871 ChinaDP Technology Beijing 100080 ChinaCollege of Chemistry and Chemical Engineering Xiamen University Xiamen 361005 ChinaDP Technology Beijing 100080 ChinaAbstract Powder X‐ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse‐grained level. The more difficult and important task of fine‐grained crystal structure prediction from PXRD remains unaddressed. This study introduces XtalNet, the first equivariant deep generative model for end‐to‐end crystal structure prediction from PXRD. Unlike previous crystal structure prediction methods that rely solely on composition, XtalNet leverages PXRD as an additional condition, eliminating ambiguity and enabling the generation of complex organic structures with up to 400 atoms in the unit cell. XtalNet comprises two modules: a Contrastive PXRD‐Crystal Pretraining (CPCP) module that aligns PXRD space with crystal structure space, and a Conditional Crystal Structure Generation (CCSG) module that generates candidate crystal structures conditioned on PXRD patterns. Evaluation on two MOF datasets (hMOF‐100 and hMOF‐400) demonstrates XtalNet's effectiveness. XtalNet achieves a top‐10 Match Rate of 90.2% and 79% for hMOF‐100 and hMOF‐400 in conditional crystal structure prediction task, respectively. XtalNet enables the direct prediction of crystal structures from experimental measurements, eliminating the need for manual intervention and external databases. This opens up new possibilities for automated crystal structure determination and the accelerated discovery of novel materials.https://doi.org/10.1002/advs.202410722crystal structure predictiondeep learningequivariant deep generative modelmetal–organic frameworks (MOFs)powder X‐ray diffraction |
| spellingShingle | Qingsi Lai Fanjie Xu Lin Yao Zhifeng Gao Siyuan Liu Hongshuai Wang Shuqi Lu Di He Liwei Wang Linfeng Zhang Cheng Wang Guolin Ke End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction Advanced Science crystal structure prediction deep learning equivariant deep generative model metal–organic frameworks (MOFs) powder X‐ray diffraction |
| title | End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction |
| title_full | End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction |
| title_fullStr | End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction |
| title_full_unstemmed | End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction |
| title_short | End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction |
| title_sort | end to end crystal structure prediction from powder x ray diffraction |
| topic | crystal structure prediction deep learning equivariant deep generative model metal–organic frameworks (MOFs) powder X‐ray diffraction |
| url | https://doi.org/10.1002/advs.202410722 |
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