Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …
processing problems and now have an increasingly important role in scientific discovery. A …
The properties of hydrogen and helium under extreme conditions
Hydrogen and helium are the most abundant elements in the Universe. They are also, in
principle, the most simple. Nonetheless, they display remarkable properties under extreme …
principle, the most simple. Nonetheless, they display remarkable properties under extreme …
Ab initio solution of the many-electron Schrödinger equation with deep neural networks
Given access to accurate solutions of the many-electron Schrödinger equation, nearly all
chemistry could be derived from first principles. Exact wave functions of interesting chemical …
chemistry could be derived from first principles. Exact wave functions of interesting chemical …
[書籍][B] Quantum Monte Carlo approaches for correlated systems
Over the past several decades, computational approaches to studying strongly-interacting
systems have become increasingly varied and sophisticated. This book provides a …
systems have become increasingly varied and sophisticated. This book provides a …
Continuum variational and diffusion quantum Monte Carlo calculations
RJ Needs, MD Towler, ND Drummond… - Journal of Physics …, 2009 - iopscience.iop.org
This topical review describes the methodology of continuum variational and diffusion
quantum Monte Carlo calculations. These stochastic methods are based on many-body …
quantum Monte Carlo calculations. These stochastic methods are based on many-body …
Quantum Monte Carlo and related approaches
As the name implies, Monte Carlo (MC) methods employ random numbers to solve
problems. The range of problems that may be treated by MC is substantial; these include …
problems. The range of problems that may be treated by MC is substantial; these include …
Neural-network quantum states for ultra-cold Fermi gases
Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the
transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic …
transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic …
Neural wave functions for superfluids
Understanding superfluidity remains a major goal of condensed matter physics. Here, we
tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) …
tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) …
A new mean-field method suitable for strongly correlated electrons: Computationally facile antisymmetric products of nonorthogonal geminals
We propose an approach to the electronic structure problem based on noninteracting
electron pairs that has similar computational cost to conventional methods based on …
electron pairs that has similar computational cost to conventional methods based on …
Inhomogeneous backflow transformations in quantum Monte Carlo calculations
An inhomogeneous backflow transformation for many-particle wave functions is presented
and applied to electrons in atoms, molecules, and solids. We report variational and diffusion …
and applied to electrons in atoms, molecules, and solids. We report variational and diffusion …