Discovering new phases via computing second-order stationary states of Landau-Brazovskii model
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915999640453120 |
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| author | Bao, Chenglong Deng, Kai Jiang, Kai Zhang, Juan |
| author_facet | Bao, Chenglong Deng, Kai Jiang, Kai Zhang, Juan |
| contents | In this work, we report a stable ordered structure -- the cubic FDDD phase -- that has not previously been identified in the Landau-Brazovskii (LB) model, a fundamental and important model for studying crystals and their phase transitions. The key to this discovery is the proposed implicit-explicit trust region method for computing second-order stationary points in the high-dimensional nonconvex energy landscape of the LB model. Different from existing first-order gradient-based algorithms, which only guarantee convergence to first-order stationary points and may therefore stagnate at saddle points, the proposed method is theoretically guaranteed to converge to second-order stationary points corresponding to local minima. Numerical experiments verify the theoretical properties of the algorithm and demonstrate its robustness in locating stable phases from different initial conditions. Based on the discovered FDDD phase, we further construct an updated phase diagram of the LB model and identify its stable region. These results show that targeting second-order stationary points provides an effective computational paradigm for exploring complex free-energy landscapes and uncovering new stable phases. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_03933 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discovering new phases via computing second-order stationary states of Landau-Brazovskii model Bao, Chenglong Deng, Kai Jiang, Kai Zhang, Juan Numerical Analysis Optimization and Control 65N22, 65K10, 90C26 In this work, we report a stable ordered structure -- the cubic FDDD phase -- that has not previously been identified in the Landau-Brazovskii (LB) model, a fundamental and important model for studying crystals and their phase transitions. The key to this discovery is the proposed implicit-explicit trust region method for computing second-order stationary points in the high-dimensional nonconvex energy landscape of the LB model. Different from existing first-order gradient-based algorithms, which only guarantee convergence to first-order stationary points and may therefore stagnate at saddle points, the proposed method is theoretically guaranteed to converge to second-order stationary points corresponding to local minima. Numerical experiments verify the theoretical properties of the algorithm and demonstrate its robustness in locating stable phases from different initial conditions. Based on the discovered FDDD phase, we further construct an updated phase diagram of the LB model and identify its stable region. These results show that targeting second-order stationary points provides an effective computational paradigm for exploring complex free-energy landscapes and uncovering new stable phases. |
| title | Discovering new phases via computing second-order stationary states of Landau-Brazovskii model |
| topic | Numerical Analysis Optimization and Control 65N22, 65K10, 90C26 |
| url | https://arxiv.org/abs/2603.03933 |