Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Correlated electron materials display a rich variety of notable properties ranging from
unconventional superconductivity to metal-insulator transitions. These properties are of …

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum
many-body systems. One of its bottlenecks is the lack of a general and efficient update …

Typical Wannier-function downfolding starts with a mean-field or density functional set of
bands to construct the Wannier functions. Here, we carry out a controlled approach, using …

Traditional computational methods for studying quantum many-body systems are “forward
methods,” which take quantum models, ie, Hamiltonians, as input and produce ground …

Data-driven techniques are increasingly used to replace electronic-structure calculations of
matter. In this context, a relevant question is whether machine learning (ML) should be …

It has become increasingly feasible to use quantum Monte Carlo (QMC) methods to study
correlated fermion systems for realistic Hamiltonians. We give a summary of these …

In single sheets of graphene, vacancy-induced states have been shown to host an effective
spin-1/2 hole that can be Kondo screened at low temperatures. Here, we show how these …

We describe a new open-source Python-based package for high accuracy correlated
electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC …

Due to advances in computer hardware and new algorithms, it is now possible to perform
highly accurate many-body simulations of realistic materials with all their intrinsic …