University of Cambridge > > Theory of Condensed Matter > Solving the Many-Electron Schrödinger Equation with Deep Neural Networks

Solving the Many-Electron Schrödinger Equation with Deep Neural Networks

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Jan Behrends.

Exact wavefunctions of interesting many-electron systems are NP-hard to compute in general, but approximations can be found using polynomially-scaling algorithms. The challenge is to find an approximate wavefunction that is simple enough to evaluate and yet has enough variational freedom to produce accurate results. Neural networks have shown impressive power as accurate practical function approximators and promise as compact wavefunctions for spin systems, but the Pauli principle complicates network representations of many-fermion wavefunctions. Here we introduce a fully antisymmetric deep learning architecture, Fermi Net, able to approximate the wavefunctions of atoms and small molecules to remarkable accuracy. For example, we predict the dissociation curves of the nitrogen molecule and the hydrogen chain, two challenging strongly-correlated systems, to significantly higher precision than the coupled cluster method, widely considered the best scalable method for quantum chemistry. This work opens the possibility of accurate direct optimisation of wavefunctions for previously intractable molecules and solids.

This talk is part of the Theory of Condensed Matter series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity