activity
20202025
most citedTBG as Topological Heavy Fermion: II. Analytical approximations of the model parameters

35 citations · 62 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.str-el2025

Particle-hole Symmetric Slave Boson Method for the Mixed Valence Problem

Liam L. H. Lau, Piers Coleman

We introduce an analytic slave boson method for treating the finite Anderson impurity model. Our approach introduces two bosons to track both valenc…

cond-mat.str-el2025★ 3 cited

Oscillate and Renormalize: Fast Phonons Reshape the Kondo Effect in Flat Band Systems

Liam L. H. Lau, Andreas Gleis, Daniel Kaplan +2

We examine the interplay between electron correlations and phonons in an Anderson-Holstein impurity model with an Einstein phonon. When the phonons are slow compared to charge fluc…

cond-mat.str-el2023★ 35 cited

TBG as Topological Heavy Fermion: II. Analytical approximations of the model parameters

Dumitru Călugăru, Maksim Borovkov, Liam L. H. Lau +3

The recently-introduced Topological Heavy Fermion (THF) model [Phys. Rev. Lett. 129, 047601] of twisted bilayer graphene (TBG) aims to reconcile the quantum-dot-like electronic str…

cond-mat.str-el2023★ 15 cited

Topological Mixed Valence Model for Twisted Bilayer Graphene

Liam L. H. Lau, Piers Coleman

Song and Bernevig (SB) have recently proposed a topological heavy fermion description of the physics of magic angle twisted bilayer graphene (MATBG), involving the hybridization of…

cond-mat.quant-gas2020★ 6 cited

Quantum walk of two anyons across a statistical boundary

Liam L. H. Lau, Shovan Dutta

We model a quantum walk of identical particles that can change their exchange statistics by hopping across a domain wall in a 1D lattice. Such a "statistical boundary" is transpare…

physics.comp-ph2020★ 3 cited

ODEN: A Framework to Solve Ordinary Differential Equations using Artificial Neural Networks

Liam L. H. Lau, Denis Werth

We explore in detail a method to solve ordinary differential equations using feedforward neural networks. We prove a specific loss function, which does not require knowledge of the…