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TT: Fachverband Tiefe Temperaturen

TT 44: Correlated Electrons: Method Development

TT 44.9: Vortrag

Mittwoch, 3. April 2019, 17:15–17:30, H7

Learning multiple order parameters with interpretable machines — •Jonas Greitemann, Ke Liu, and Lode Pollet — Arnold Sommerfeld Center for Theoretical Physics, Ludwig-Maximilians-Universität München

Machine learning shows promise for studying phase transitions many-body systems. However, most studies are tied to situations involving only one phase transition and one order parameter. Systems that accommodate multiple phases of coexisting and competing orders, which are common in condensed matter physics, remain largely unexplored from a machine learning perspective. We investigate the multiclassification of phases using Support Vector Machines (SVMs) and present a generic protocol for detecting hidden spin and orbital orders to learn multiple phases and their analytical order parameters. Our focus is on multipolar orders and their tensorial order parameters whose analytical form is extracted for tensors up to rank 6. Furthermore, we discuss an intrinsic parameter of SVM, the bias, which allows for a special interpretation in the classification of phases, and its utility in diagnosing the existence of phase transitions. We show that it can be exploited as an efficient way to explore the topology of unknown phase diagrams where the supervision is entirely delegated to the machine.