Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session C62: Machine Learning Applications in Experimental Quantum Materials Research
2:30 PM–5:30 PM,
Monday, March 4, 2019
BCEC Room: 258C
Sponsoring Unit: DCOMP
Chair: Jonpierre Grajales, New Jersey Inst of Tech
Abstract: C62.00001 : Learning Quantum Emergence with AI.*
2:30 PM–3:06 PM
View Presentation 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 the analysis of scanning tunneling spectroscopy data and X-ray scattering data.
*Theoretical studies are supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Science and Engineering under Award DE-SC0018946.
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