What is recorded here?
Optimized antisymmetric neural-network wavefunctions with variational Monte Carlo to calculate electronic energies for tested atoms and molecules.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
These are approximate computational results for specified systems, not a general exact solution of the many-electron Schrödinger equation.
Read the original work
- Ab initio solution of the many-electron Schrödinger equation with deep neural networks
- Ab initio solution of the many-electron Schrödinger equation with deep neural networks — deposited bibliography
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
David Pfau; James S. Spencer; Alexander G. D. G. Matthews; W. M. C. Foulkes
Full deposited paper byline or identified author of the scoped historical source. Association does not measure individual effort.
FermiNet · Google DeepMind · Fermionic neural network
Variational wavefunction optimization; human-led ansatz design and numerical evaluation. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
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Documented countries / regions: United Kingdom
DeepMind, London, and Department of Physics, Imperial College London; all authors’ original paper affiliations.
English catalogue narrative
FermiNet: learning quantum wavefunctions
Optimized antisymmetric neural-network wavefunctions with variational Monte Carlo to calculate electronic energies for tested atoms and molecules.
Scientific scope
These are approximate computational results for specified systems, not a general exact solution of the many-electron Schrödinger equation.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
David Pfau; James S. Spencer; Alexander G. D. G. Matthews; W. M. C. Foulkes
Full deposited paper byline or identified author of the scoped historical source. Association does not measure individual effort.
FermiNet / Google DeepMind / Fermionic neural network: Variational wavefunction optimization; human-led ansatz design and numerical evaluation (source reported).