Center for Correlated Electron Systems


Machine Learning Data from Electronic Quantum Matter

July 11, 2018l Hit 1394
Date : July 19, 2018 16:00 ~ 17:00
Speaker : Prof. Eun-Ah Kim(Cornell University)
Location : Bldg. 19, Rm. 210

Abstract:   In recent years, enormous data sets have begun to appear in real-space  visualizations (scanning probes) and reciprocal-space visualizations (scattering probes) of electronic quantum matter. Increasing volume and variety of such data present new challenges and opportunities that are ripe for a new approach: machine learning. However, the scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons: (1) quantum mechanical imaging of electronic behavior is probabilistic, (2) inference from data should be subject to fundamental laws governing microscopic interactions. In this talk, I will review the aspects of machine learning that are appealing for dealing with quantum complexity and present how we implemented a machine learning approach to analysis of scanning tunneling spectroscopy data.  




Host: Prof. Tae Won Noh