MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917749223063552 |
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| author | Chen, Yan Wang, Xueru Deng, Xiaobin Liu, Yilun Chen, Xi Zhang, Yunwei Wang, Lei Xiao, Hang |
| author_facet | Chen, Yan Wang, Xueru Deng, Xiaobin Liu, Yilun Chen, Xi Zhang, Yunwei Wang, Lei Xiao, Hang |
| contents | Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science. Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficiency, architectural constraints and restricted open-source availability. The representation of crystal structures using the SLICES (Simplified Line-Input Crystal-Encoding System) notation as a string of characters enables the use of state-of-the-art natural language processing models, such as Transformers, for crystal design. Drawing inspiration from the success of GPT models in generating coherent text, we trained a generative Transformer on the next-token prediction task to generate solid-state materials with targeted properties. We demonstrate MatterGPT's capability to generate de novo crystal structures with targeted single properties, including both lattice-insensitive (formation energy) and lattice-sensitive (band gap) properties. Furthermore, we extend MatterGPT to simultaneously target multiple properties, addressing the complex challenge of multi-objective inverse design of crystals. Our approach showcases high validity, uniqueness, and novelty in generated structures, as well as the ability to generate materials with properties beyond the training data distribution. This work represents a significant step forward in computational materials discovery, offering a powerful and open tool for designing materials with tailored properties for various applications in energy, electronics, and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials Chen, Yan Wang, Xueru Deng, Xiaobin Liu, Yilun Chen, Xi Zhang, Yunwei Wang, Lei Xiao, Hang Materials Science Computational Physics Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science. Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficiency, architectural constraints and restricted open-source availability. The representation of crystal structures using the SLICES (Simplified Line-Input Crystal-Encoding System) notation as a string of characters enables the use of state-of-the-art natural language processing models, such as Transformers, for crystal design. Drawing inspiration from the success of GPT models in generating coherent text, we trained a generative Transformer on the next-token prediction task to generate solid-state materials with targeted properties. We demonstrate MatterGPT's capability to generate de novo crystal structures with targeted single properties, including both lattice-insensitive (formation energy) and lattice-sensitive (band gap) properties. Furthermore, we extend MatterGPT to simultaneously target multiple properties, addressing the complex challenge of multi-objective inverse design of crystals. Our approach showcases high validity, uniqueness, and novelty in generated structures, as well as the ability to generate materials with properties beyond the training data distribution. This work represents a significant step forward in computational materials discovery, offering a powerful and open tool for designing materials with tailored properties for various applications in energy, electronics, and beyond. |
| title | MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2408.07608 |