Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Okabe, Ryotaro Cheng, Mouyang Chotrattanapituk, Abhijatmedhi Hung, Nguyen Tuan Fu, Xiang Han, Bowen Wang, Yao Xie, Weiwei Cava, Robert J. Jaakkola, Tommi S. Cheng, Yongqiang Li, Mingda |
| author_facet | Okabe, Ryotaro Cheng, Mouyang Chotrattanapituk, Abhijatmedhi Hung, Nguyen Tuan Fu, Xiang Han, Bowen Wang, Yao Xie, Weiwei Cava, Robert J. Jaakkola, Tommi S. Cheng, Yongqiang Li, Mingda |
| contents | Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates Okabe, Ryotaro Cheng, Mouyang Chotrattanapituk, Abhijatmedhi Hung, Nguyen Tuan Fu, Xiang Han, Bowen Wang, Yao Xie, Weiwei Cava, Robert J. Jaakkola, Tommi S. Cheng, Yongqiang Li, Mingda Materials Science Machine Learning Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates. |
| title | Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2407.04557 |