AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911398258278400 |
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| author | Ridwan, Osman Goni Pitié, Sylvain Raj, Monish Soundar Dai, Dong Frapper, Gilles Xue, Hongfei Zhu, Qiang |
| author_facet | Ridwan, Osman Goni Pitié, Sylvain Raj, Monish Soundar Dai, Dong Frapper, Gilles Xue, Hongfei Zhu, Qiang |
| contents | In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment Ridwan, Osman Goni Pitié, Sylvain Raj, Monish Soundar Dai, Dong Frapper, Gilles Xue, Hongfei Zhu, Qiang Materials Science Artificial Intelligence Computational Physics In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials. |
| title | AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment |
| topic | Materials Science Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2506.08224 |