Dr. Ehsan Khatami
San Jose State University
ABSTRACT: The physics of strongly correlated phases of matter is often described in terms of straightforward electronic patterns. This has so far been the basis for studying correlations in the Fermi-Hubbard model realized with ultracold atoms. In this talk, I will show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm. We find that long and short range spin correlations spontaneously emerge in filters of a convolutional neural network trained to recognize snapshots of single species of fermions. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi liquid behavior. The technique can be employed to characterize correlations unique to other phases with no obvious order parameter or signatures in projective measurements.
BIOGRAPHY: Dr. Khatami did his undergraduate and Masters studies in physics in Iran. He joined the PhD program at the University of Cincinnati in 2004 where he worked on dynamical mean-field solutions for strongly-correlated fermionic systems to study superconductivity. He graduated in 2009 and after several research positions at Louisiana State University, Georgetown University, UC Santa Cruz and UC Davis, joined San Jose State University in 2014 where he is now an Associate Professor in physics. His numerical simulations of quantum many-body systems have been a part of several collaborations with AMO experimentalists. More recently, he has been exploring the use of machine learning tools for science discovery. Dr. Khatami was a KITP Scholar, and in 2018 was awarded San Jose State University’s Early Career Investigator Award along with early tenure and promotion.