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 …

Accurate knowledge of the properties of hydrogen at high compression is crucial for
astrophysics (eg, planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) …

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 …

Understanding the real-time evolution of many-electron quantum systems is essential for
studying dynamical properties in condensed matter, quantum chemistry, and complex …

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 …

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 …

Understanding superfluidity remains a major goal of condensed matter physics. Here, we
tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) …