m* of two-dimensional electron gas: A neural canonical transformation study

被引:10
作者
Xie, Hao [1 ,2 ]
Zhang, Linfeng [3 ,4 ]
Wang, Lei [1 ,5 ]
机构
[1] Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] DP Technol, Beijing 100080, Peoples R China
[4] AI Sci Inst, Beijing 100080, Peoples R China
[5] Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China
关键词
MONTE-CARLO; GROUND-STATE; POINTS; MASS;
D O I
10.21468/SciPostPhys.14.6.154
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The quasiparticle effective mass m* of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform electron gas is still elusive after decades of research. The newly developed neural canonical transformation approach [Xie et al., J. Mach. Learn. 1, (2022)] offers a principled way to extract the effective mass of electron gas by directly calculating the thermal entropy at low temperature. The approach models a variational many-electron density matrix using two generative neural networks: an autoregressive model for momentum occupation and a normalizing flow for electron coordinates. Our calculation reveals a suppression of effective mass in the two-dimensional spin-polarized electron gas, which is more pronounced than previous reports in the low-density strong-coupling region. This prediction calls for verification in two-dimensional electron gas experiments.
引用
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页数:22
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