MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912165185716224 |
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| author | Qi, Jingyuan Jia, Zian Liu, Minqian Zhan, Wangzhi Zhang, Junkai Wen, Xiaofei Gan, Jingru Chen, Jianpeng Liu, Qin Ma, Mingyu Derek Li, Bangzheng Wang, Haohui Kulkarni, Adithya Chen, Muhao Zhou, Dawei Li, Ling Wang, Wei Huang, Lifu |
| author_facet | Qi, Jingyuan Jia, Zian Liu, Minqian Zhan, Wangzhi Zhang, Junkai Wen, Xiaofei Gan, Jingru Chen, Jianpeng Liu, Qin Ma, Mingyu Derek Li, Bangzheng Wang, Haohui Kulkarni, Adithya Chen, Muhao Zhou, Dawei Li, Ling Wang, Wei Huang, Lifu |
| contents | The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design Qi, Jingyuan Jia, Zian Liu, Minqian Zhan, Wangzhi Zhang, Junkai Wen, Xiaofei Gan, Jingru Chen, Jianpeng Liu, Qin Ma, Mingyu Derek Li, Bangzheng Wang, Haohui Kulkarni, Adithya Chen, Muhao Zhou, Dawei Li, Ling Wang, Wei Huang, Lifu Artificial Intelligence Human-Computer Interaction The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field. |
| title | MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.16270 |