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 …
Deep-neural-network solution of the electronic Schrödinger equation
The electronic Schrödinger equation can only be solved analytically for the hydrogen atom,
and the numerically exact full configuration-interaction method is exponentially expensive in …
and the numerically exact full configuration-interaction method is exponentially expensive in …
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 …
Fermionic neural-network states for ab-initio electronic structure
Neural-network quantum states have been successfully used to study a variety of lattice and
continuous-space problems. Despite a great deal of general methodological developments …
continuous-space problems. Despite a great deal of general methodological developments …
Discovering quantum phase transitions with fermionic neural networks
Deep neural networks have been very successful as highly accurate wave function Ansätze
for variational Monte Carlo calculations of molecular ground states. We present an extension …
for variational Monte Carlo calculations of molecular ground states. We present an extension …
Machine learning the quantum mechanical wave function
M Dey, D Ghosh - The Journal of Physical Chemistry A, 2023 - ACS Publications
Strongly correlated systems have been challenging to computational chemists for a long
time. To solve these systems, multireference methods have been developed over the years …
time. To solve these systems, multireference methods have been developed over the years …
Neural network ansatz for periodic wave functions and the homogeneous electron gas
We design a neural network Ansatz for variationally finding the ground-state wave function
of the homogeneous electron gas, a fundamental model in the physics of extended systems …
of the homogeneous electron gas, a fundamental model in the physics of extended systems …
Message-passing neural quantum states for the homogeneous electron gas
We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …
Backflow transformations via neural networks for quantum many-body wave functions
Obtaining an accurate ground state wave function is one of the great challenges in the
quantum many-body problem. In this Letter, we propose a new class of wave functions …
quantum many-body problem. In this Letter, we propose a new class of wave functions …
Neural-network quantum states for periodic systems in continuous space
We introduce a family of neural quantum states for the simulation of strongly interacting
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …