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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00768 |
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| _version_ | 1866916984413749248 |
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| author | Hyun, Lee Yoon, Sohee Park, Jinwoo Chae, Sue In Park, Seongeon Ahn, Jooyeon Jung, Yebin Chung, Youjung Chang, Hogeun Park, Sujin Kang, Myeonginn Kim, Jina Kim, Ho-Gyeong Jeong, Myeonghun |
| author_facet | Hyun, Lee Yoon, Sohee Park, Jinwoo Chae, Sue In Park, Seongeon Ahn, Jooyeon Jung, Yebin Chung, Youjung Chang, Hogeun Park, Sujin Kang, Myeonginn Kim, Jina Kim, Ho-Gyeong Jeong, Myeonghun |
| contents | AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a reasoning problem with large reasoning models (LRMs). To instill reasoning capability into language models, we curate reasoning traces from a teacher model to train a student model. However, most training pipelines select reasoning traces using binary correctness or learned preference signals that poorly reflect physical admissibility. We introduce Physics-aware Rejection Sampling (PaRS), a training-time trace selection scheme that favors traces consistent with fundamental physics and numerically close to targets, with lightweight halting to control compute. We instantiate our framework with a large student model fine-tuned on traces synthesized by a larger teacher model, and evaluate under matched token budgets against various rejection sampling baselines. Our method improves accuracy and calibration, reduces physics-violation rates, and lowers sampling cost relative to baselines. These results indicate that modest, domain-aware constraints combined with trace-level selection provide a practical path toward reliable, efficient LRMs for process-aware property prediction and closed-loop materials design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling Hyun, Lee Yoon, Sohee Park, Jinwoo Chae, Sue In Park, Seongeon Ahn, Jooyeon Jung, Yebin Chung, Youjung Chang, Hogeun Park, Sujin Kang, Myeonginn Kim, Jina Kim, Ho-Gyeong Jeong, Myeonghun Artificial Intelligence Materials Science Computation and Language AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a reasoning problem with large reasoning models (LRMs). To instill reasoning capability into language models, we curate reasoning traces from a teacher model to train a student model. However, most training pipelines select reasoning traces using binary correctness or learned preference signals that poorly reflect physical admissibility. We introduce Physics-aware Rejection Sampling (PaRS), a training-time trace selection scheme that favors traces consistent with fundamental physics and numerically close to targets, with lightweight halting to control compute. We instantiate our framework with a large student model fine-tuned on traces synthesized by a larger teacher model, and evaluate under matched token budgets against various rejection sampling baselines. Our method improves accuracy and calibration, reduces physics-violation rates, and lowers sampling cost relative to baselines. These results indicate that modest, domain-aware constraints combined with trace-level selection provide a practical path toward reliable, efficient LRMs for process-aware property prediction and closed-loop materials design. |
| title | Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling |
| topic | Artificial Intelligence Materials Science Computation and Language |
| url | https://arxiv.org/abs/2509.00768 |