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

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

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 …

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

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

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

Computing accurate yet efficient approximations to the solutions of the electronic
Schrödinger equation has been a paramount challenge of computational chemistry for …

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 …

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