Crystal structure prediction by combining graph network and optimization algorithm
Predicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach.
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-29241-4 |
| _version_ | 1818353609668034560 |
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| author | Guanjian Cheng Xin-Gao Gong Wan-Jian Yin |
| author_facet | Guanjian Cheng Xin-Gao Gong Wan-Jian Yin |
| author_sort | Guanjian Cheng |
| collection | DOAJ |
| description | Predicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach. |
| first_indexed | 2024-12-13T19:12:15Z |
| format | Article |
| id | doaj.art-4247ffea691940238fbecccce3765eed |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-13T19:12:15Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-4247ffea691940238fbecccce3765eed2022-12-21T23:34:22ZengNature PortfolioNature Communications2041-17232022-03-011311810.1038/s41467-022-29241-4Crystal structure prediction by combining graph network and optimization algorithmGuanjian Cheng0Xin-Gao Gong1Wan-Jian Yin2College of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow UniversityShanghai Qi Zhi InstituteCollege of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow UniversityPredicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach.https://doi.org/10.1038/s41467-022-29241-4 |
| spellingShingle | Guanjian Cheng Xin-Gao Gong Wan-Jian Yin Crystal structure prediction by combining graph network and optimization algorithm Nature Communications |
| title | Crystal structure prediction by combining graph network and optimization algorithm |
| title_full | Crystal structure prediction by combining graph network and optimization algorithm |
| title_fullStr | Crystal structure prediction by combining graph network and optimization algorithm |
| title_full_unstemmed | Crystal structure prediction by combining graph network and optimization algorithm |
| title_short | Crystal structure prediction by combining graph network and optimization algorithm |
| title_sort | crystal structure prediction by combining graph network and optimization algorithm |
| url | https://doi.org/10.1038/s41467-022-29241-4 |
| work_keys_str_mv | AT guanjiancheng crystalstructurepredictionbycombininggraphnetworkandoptimizationalgorithm AT xingaogong crystalstructurepredictionbycombininggraphnetworkandoptimizationalgorithm AT wanjianyin crystalstructurepredictionbycombininggraphnetworkandoptimizationalgorithm |