Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …

We present an algorithm to estimate the excited states of a quantum system by variational
Monte Carlo, which has no free parameters and requires no orthogonalization of the states …

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising
cutting-edge technique of ab initio quantum chemistry. However, the high computational cost …

Recent neural network-based wave functions have achieved state-of-the-art accuracies in
modeling ab-initio ground-state potential energy surface. However, these networks can only …

We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …

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 …

Deep neural networks have become a highly accurate and powerful wavefunction ansatz in
combination with variational Monte Carlo methods for solving the electronic Schrödinger …

Abstract Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed
significant developments in the past decades and become one of the go-to methods when …

Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of
computational chemistry. Given its importance for the development of new chemical …

Obtaining accurate solutions to the Schrödinger equation is the key challenge in
computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) …