The Hubbard model is the simplest model of interacting fermions on a lattice and is of similar
importance to correlated electron physics as the Ising model is to statistical mechanics or the …

The simulation of chemistry is among the most promising applications of quantum
computing. However, most prior work exploring algorithms for block encoding, time evolving …

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 …

Accurate and predictive computations of the quantum-mechanical behavior of many
interacting electrons in realistic atomic environments are critical for the theoretical design of …

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

Fundamental knowledge gaps are endemic in our understanding of how emergent
properties of soft materials are linked to the quantum mechanical (QM) world. The limitations …

Abstract Representation learning for the electronic structure problem is a major challenge of
machine learning in computational condensed matter and materials physics. Within …

We derive a widely applicable first-principles approach for determining two-body, static
effective interactions for low-energy Hamiltonians with quantitative accuracy. The algebraic …

Despite being relevant to better understand the properties of honeycomblike systems, as
graphene-based compounds, the electron-phonon interaction is commonly disregarded in …

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 …