论文标题
二维电子气的$ m^\ ast $：神经规范转换研究
$m^\ast$ of two-dimensional electron gas: a neural canonical transformation study
论文作者
论文摘要
相互作用电子的准粒子有效质量$ m^\ ast $是费米液体理论中的基本数量。但是，经过数十年的研究，有效统一电子气体的确切价值仍然难以捉摸。新开发的神经规范转化方法[Xie等，J。Mach。学习。 1，（2022）]提供了一种原则性的方法来通过直接在低温下计算热熵来提取有效的电子气体。该方法使用两个生成神经网络模拟了变异多电子密度矩阵：一种用于动量职业的自回归模型和电子坐标的正常流量。我们的计算表明，二维自旋偏振电子气体中有效质量的抑制作用，这比在低密度强耦合区域中比以前的报道更为明显。该预测要求在二维电子气体实验中进行验证。
The quasiparticle effective mass $m^\ast$ 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.
