262 citations · 2.5k across the 36 of their papers we have counts for
36 papers
Transferable Neural Wavefunctions for Solids
Leon Gerard, Michael Scherbela, Halvard Sutterud +2
Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schrödinger equation.…
Neural network variational Monte Carlo for positronic chemistry
G. Cassella, W. M. C. Foulkes, D. Pfau +1
Quantum chemical calculations of the ground-state properties of positron-molecule complexes are challenging. The main difficulty lies in employing an appropriate basis set for repr…
The Einstein-de Haas Effect in an Cluster
T. Wells, W. M. C. Foulkes, S. L. Dudarev +1
Classical models of spin-lattice coupling are at present unable to accurately reproduce results for numerous properties of ferromagnetic materials, such as heat transport coefficie…
Neural Wave Functions for Superfluids
Wan Tong Lou, Halvard Sutterud, Gino Cassella +4
Understanding superfluidity remains a major goal of condensed matter physics. Here we tackle this challenge utilizing the recently developed Fermionic neural network (FermiNet) wav…
Ab-initio quantum chemistry with neural-network wavefunctions
Jan Hermann, James Spencer, Kenny Choo +5
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also…
Discovering Quantum Phase Transitions with Fermionic Neural Networks
G. Cassella, H. Sutterud, S. Azadi +4
Deep neural networks have been extremely successful as highly accurate wave function ansätze for variational Monte Carlo calculations of molecular ground states. We present an exte…