Non-equilibrium criticality and efficient exploration of glassy landscapes with memory dynamics
Non-equilibrium criticality and efficient exploration of glassy landscapes with memory dynamics
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DOI：
10.1016/j.physa.2021.126727
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发表时间：
2021-02
期刊：
影响因子：
--
通讯作者：
Y. Pei;M. Di Ventra
中科院分区：
文献类型：
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作者：
Y. Pei;M. Di Ventra
由于受挫和亚稳态的存在，自旋玻璃的分析和数值研究都是出了名的困难。它们高度非凸的景观需要集体更新才能有效地探索。目前，大多数最先进的算法依赖于随机自旋簇来执行非局部更新，但这种“簇算法”缺乏一般的效率。本文介绍了一种基于经典记忆动力学的非平衡方法来模拟自旋玻璃。通过模拟各种类型的三维自旋玻璃（Edwards-Anderson、部分受挫模型和完全受挫模型），我们发现记忆在时间演化过程中以自组织的方式动态地促进了临界自旋团簇。这有助于有效地探索自旋玻璃的低温相。
Spin glasses are notoriously difficult to study both analytically and numerically due to the presence of frustration and metastability. Their highly non-convex landscapes require collective updates to explore efficiently. Currently, most state-of-the-art algorithms rely on stochastic spin clusters to perform non-local updates, but such “cluster algorithms” lack general efficiency. Here, we introduce a non-equilibrium approach for simulating spin glasses based on classical dynamics with memory. By simulating various classes of 3 d spin glasses (Edwards–Anderson, partially-frustrated, and fully-frustrated models), we find that memory dynamically promotes critical spin clusters during time evolution, in a self-organizing manner. This facilitates an efficient exploration of the low-temperature phases of spin glasses.