mlsanityData to 7 Oct 2026

Better, Faster Fermionic Neural Networks

arXiv 2011.07125 ↗First posted 13 Nov 2020physics.comp-ph cs.LG physics.chem-phcited by 28

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Abstract (arXiv)

The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems. Here we present several improvements to the FermiNet that allow us to set new records for speed and accuracy on challenging systems. We find that increasing the size of the network is sufficient to reach chemical accuracy on atoms as large as argon. Through a combination of implementing FermiNet in JAX and simplifying several parts of the network, we are able to reduce the number of GPU hours needed to train the FermiNet on large systems by an order of magnitude. This enables us to run the FermiNet on the challenging transition of bicyclobutane to butadiene and compare against the PauliNet on the automerization of cyclobutadiene, and we achieve results near the state of the art for both.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2020.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 5; 2024: 4; 2025: 3; 2026: 4.

28 papers.

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  1. 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 45
  2. 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
  3. 2021Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functionscited by 36
  4. 2021Provably Strict Generalisation Benefit for Invariance in Kernel Methodscited by 29
  5. 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 28
  6. 2023Generalizing Neural Wave Functionscited by 26
  7. 2022Gold-standard solutions to the Schr"odinger equation using deep learning: How much physics do we need?cited by 25
  8. 2021Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networkscited by 22
  9. 2022Electronic excited states in deep variational Monte Carlocited by 21
  10. 2022Sampling-free Inference for Ab-Initio Potential Energy Surface Networkscited by 19
  11. 2021Ab-initio study of interacting fermions at finite temperature with neural canonical transformationcited by 16
  12. 2025An ab initio foundation model of wavefunctions that accurately describes chemical bond breakingcited by 11
16 more
  1. 2023Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctionscited by 8
  2. 2024Neural Pfaffians: Solving Many Many-Electron Schr"odinger Equationscited by 8
  3. 2025Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problemscited by 8
  4. 2021Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlocited by 6
  5. 2023Towards a Foundation Model for Neural Network Wavefunctionscited by 6
  6. 2023Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlocited by 6
  7. 2022Positive-definite parametrization of mixed quantum states with deep neural networkscited by 4
  8. 2024Highly Accurate Real-space Electron Densities with Neural Networkscited by 4
  9. 2023Convergence of variational Monte Carlo simulation and scale-invariant pre-trainingcited by 2
  10. 2022O(N2) Universal Antisymmetry in Fermionic Neural Networkscited by 1
  11. 2026Excited Pfaffians: Generalized Neural Wave Functions Across Structure and Statecited by 1
  12. 2026Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulationscited by 1
  13. 2024Open-Source Fermionic Neural Networks with Ionic Charge Initializationcited by 0
  14. 2024A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equationcited by 0
  15. 2026Uncovering Exotic Paired States in the 2D Spin-Imbalanced Fermi Gas with Neural Wave Functionscited by 0
  16. 2026Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimizationcited by 0