Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866914243025043456 |
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| author | Man, Hai Wang, Chaobo Li, Jia-Rui Tian, Yuping Chen, Shu-Gang |
| author_facet | Man, Hai Wang, Chaobo Li, Jia-Rui Tian, Yuping Chen, Shu-Gang |
| contents | Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05577 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning Man, Hai Wang, Chaobo Li, Jia-Rui Tian, Yuping Chen, Shu-Gang Statistical Mechanics Machine Learning Computational Physics Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery. |
| title | Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning |
| topic | Statistical Mechanics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2601.05577 |