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Machine Learning Quantum Matter Data

Workshop

Computational Quantum Physics

When

  • All Day

  • All Day

Where

162 Fifth Avenue, 9th Floor Classroom

160 Fifth Avenue

New York, NY 10010

This workshop will bring together a select group of experimentalists and theorists to address the new challenges raised by the prospect of using machine learning methods to analyze the new, complex, large datasets emerging in many classes of experiments. The workshop will feature a mix of experimental and theoretical talks, along with plenty of time for discussion. Our aim is to reach a consensus on the current status, near-term possibilities, and conceptual and practical challenges of this new area of science.

Agenda

Thursday, January 23

8:00 – 9:00 AM Breakfast
9:00 – 9:15 AM Eun-ah Kim and Andy Millis Welcome and Overview
9:15- 12:00 PM Scanning and Tunneling Spectroscopy
9:15 – 10:00 AM (30+15) Jenny Hoffman (Harvard University) Local correlations in disordered materials with neural networks
10:00 – 10:45 AM (30+15) Abhay Pasupathy (Columbia University) Blind deconvolution approach to pattern recognition in STM
images
10:45 – 11:15 PM (30) Break
11:15 – 12:00 AM (30+15) Erica Carlson (Purdue University) Fractal views on quantum materials: learning physics from surface probe images Slides
12:00 – 2:00 PM Lunch and Discussion
2:00 – 4:15 PM Theory
2:00 – 2:45 PM (30+15) Roger Melko (Perimeter Institute/Waterloo) Reconstructing quantum states with generative models Slides
2:45 – 3:30 PM (30+15) Titus Neupert (University of Zurich) Many problems and some solutions for machine learning quantum matter
3:30 – 4:15 PM (30+15) Andrew Wilson (New York University) Bayesian Deep Learning Applied to Scientific Data
4:15 – 4:30 PM (15) Break
4:30 – 6:00 PM Quantum Gas Microscopy
4:30 – 5:15 PM (30+15) Giacomo Torlai (CCQ) Generative modeling of quantum simulators Slides
5:15 – 6:00 PM (30+15) Eugene Demler (Harvard University) Solving real world quantum problems with simulators and
machine learning

Friday, January 24

8:00 – 9:00 AM Breakfast
9:00 – 12:00 PM Scattering and TEM
9:00 – 9:45 AM (30+15) Ray Osborn (Argonne National Laboratories) Challenges in Studying Correlated Disorder
9:45 – 10:30 AM (30+15) Simon Billinge (Columbia University) Machine learning materials science from experimental and theoretical data Slides
10:30 – 11:00 AM (30) Break
11:00 – 11:45 AM (30+15) David Mueller (Cornell University) Phase Retrieval for Petavoxel Electron imaging as a big and deep data problem Slides
11:45 – 12:30 PM (30+15) Sergei Kalinin (Oak Ridge National Laboratories) Causal learning from structural electron and scanning probe microscopy data
12:30 – 2:00 PM Lunch and Informal Discussion
2:00 – 2:45 PM (30+15) Markus Greiner (Harvard University) Quantum simulation of the doped Hubbard model and machine
learning
2:00 – 4:00 PM Eun-Ah Kim (Cornell University) Summary and Discussion
Participants
Sayan Basak Purdue University
Simon Billinge Columbia University
Michael Cao Cornell University
Giuseppe Carleo CCQ
Erica Carlson Purdue University
Iris Cong Harvard University
Eugene Demler Harvard University
Antoine Georges CCQ
Paul Ginsparg Cornell University
Markus Greiner Harvard University
Kaylie Hausknecht Harvard University
Jenny Hoffman Harvard University
Sergei Kalinin Oak Ridge National Laboratory
Eun-Ah Kim Cornell University
Guangqi Li Columbia University
Mike Matty Cornell University
Roger Melko University of Waterloo, Perimeter Institute
Andy Millis CCQ
Lukas Muechler CCQ
David Muller Cornell University
Titus Neupert University of Zurich
Ray Osborn Argonne National Laboratories
Miles Stoudenmire CCQ
Giacomo Torlai CCQ
Andrew Wilson NYU

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