莫特过渡和体积定律与神经量子状态的纠
Chloé Gauvin-Ndiaye1,2, Joseph Tindall2, Javier Robledo Moreno2,3,4
1Université de Sherbrooke, Département de physique, Regroupement québécois sur les matériaux de pointe & Institut quantique, 2500 Boulevard Université, Sherbrooke, Québec J1K2R1, Canada.
Physical review letters
|March 7, 2025
概括
神经网络隐藏的费米子决定性状态 (HFDS) 揭示了无序电子系统中的莫特过渡. 这种先进的方法为有限的系统大小提供比传统方法更准确的波函数洞察力.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 量子力学就是量子力学.
- 计算物理学的计算物理.
背景情况:
- 莫特过渡意味着电子系统的相变,从金属到绝缘体,由移位和排斥驱动.
- 动态平均场理论 (DMFT) 提供了批量属性的精确解决方案,但仅限于热力学极限.
- 精确模拟有限大小的系统对于理解复杂的电子行为至关重要.
研究的目的:
- 引入和验证神经网络隐藏的费米子决定性状态 (HFDS),用于研究Mott过渡.
- 克服现有方法的局限性,比如精确对角化和有限系统的矩阵产物状态 (MPS).
- 为了获得新的见解,波函数的行为在金属绝缘体过渡附近.
主要方法:
- 实现神经网络隐藏的费米子决定性状态 (HFDS) 来建模无序,完全连接的哈伯德模型.
- 与动态平均场理论 (DMFT) 和矩阵产物状态 (MPS) 相比,HFDS准确性的比较.
- 计算关键的物理可观测值,包括能量,双重占用,准粒子重量和能量差距.
主要成果:
- 与MPS相比,HFDS在金属模式和Mott过渡附近提供了更准确的结果.
- 该方法成功地访问了超出精确的对角化极限的有限系统大小的波函数属性.
- 获得了对波函数幅度的新见解,阐明了过渡机制.
结论:
- HFDS代表了一个强大的新工具,用于模拟强度相关的电子系统.
- 这种方法克服了阻碍MPS等其他方法的纠限制.
- 这项研究开辟了利用神经量子状态在凝聚物质中探索量子现象的新途径.
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