An extension of FermiNet to discover quantum phase transitions - Phys.org

Architectures based on artificial neural networks (ANNs) have proved to be very helpful in research settings, as they can quickly analyze vast amounts of data and make accurate predictions.

In 2020, Google's British AI subsidiary DeepMind used a new ANN architecture dubbed the Fermionic neural network (FermiNet) to solve the Schrodinger equation for electrons in molecules, a central problem in the field of chemistry.

"Molecules are nice, but physicists are more concerned with solving the Schrodinger equation for solid matter," Gino Cassella, one of the researchers who carried out the study, told Phys.org.

"The field of 'condensed matter physics' centers around calculating the behavior of electrons in solid materials, from the wood of your desk to the silicon inside the transistors which power your phone.

We wanted to solve the Schrodinger equation with the FermiNet on either side of the Wigner transition and see how accurate the solutions we obtained are compared to current state-of-the-art methods."

The recent study by this team of researchers highlights the huge potential of FermiNet as a probe for materials that undergo exotic and poorly understood phase transitions.