Dresden 2020 – scientific programme
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DY: Fachverband Dynamik und Statistische Physik
DY 62: Critical Phenomena and Phase Transitions
DY 62.8: Talk
Friday, March 20, 2020, 11:30–11:45, ZEU 118
Detecting hidden and composite orders in layered models via machine learning — •Wojciech Rzadkowski1, Nicolo Defenu2, Silvia Chiacchiera3, Andrea Trombettoni4, and Giacomo Bighin1 — 1Institute of Science and Technology Austria (IST Austria), Am Campus 1, 3400 Klosterneuburg, Austria — 2Institut für Theoretische Physik, Universität Heidelberg, D-69120 Heidelberg, Germany — 3Science and Technology Facilities Council (STFC/UKRI), Daresbury Laboratory,Keckwick Lane, Daresbury, Warrington WA44AD, United Kingdom — 4CNR-IOM DEMOCRITOS Simulation Center, Via Bonomea 265, I-34136 Trieste, Italy
Recently, machine learning is applied to the study of phase diagrams of spin models. After initial success with supervised approaches, unsupervised techniques are being extensively developed.
We use an unsupervised technique to study layered spin models where composite order parameters may emerge as a consequence of the interlayerer coupling. We determine their phase diagram, applying an algorithm based on convolutional neural networks to the raw Monte Carlo data. Remarkably our technique [1] is able to characterize all the system phases also in the case of hidden order parameters, i.e. order parameters whose expression in terms of the microscopic configurations would require additional preprocessing of the data fed to the algorithm.
[1] W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, arXiv:1907.05417 (2019)