129 citations · 762 across the 16 of their papers we have counts for
5 papers · 1 filter
A Self-Attention Ansatz for Ab-initio Quantum Chemistry
Ingrid von Glehn, James S. Spencer, David Pfau
We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the man…
Learned Force Fields Are Ready For Ground State Catalyst Discovery
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose +6
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces fro…
ipie: A Python-based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs
Fionn D. Malone, Ankit Mahajan, James S. Spencer +1
We report the development of a python-based auxiliary-field quantum Monte Carlo (AFQMC) program, ipie, with preliminary timing benchmarks and new AFQMC results on the isomerization…
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…