December 13, 2018 UMD Home FabLab AIMLab
MLMR 2018
Machine Learning for Materials Research
&
Workshop on Machine Learning Quantum Materials
July 30, 2018 - August 3, 2018

Last updated: August 2, 2018, 8:11 am EDT

Monday - Thursday sessions begin at 9am with lunch at noon. Afternoon sessions end at 5pm.

Monday, July 30th

Tutorial on Python & Data Fundamentals and Preprocessing

Alex Belianinov (ORNL) and Brian DeCost (NIST)

  • Filtering: Noise Smoothing
  • Background Subtraction
  • Feature Extraction
    • Cross-correlation Wavelets
    • Edges
    • Closed Boundaries
    • Shapes

Evening Reception

Tuesday, July 31st

Unsupervised Machine Learning

A. Gilad Kusne (NIST)

  • Theory
  • Similarity Measures
  • Latent Variable Analysis
  • Spectral Unmixing
  • Matrix Factorization
  • Clustering

Hackathon Start

Wednesday, August 1st

Supervised Machine Learning

Dan Samarov (NIST)

  • Data Handling
    • Cross Validation
    • Prediction
  • Algorithms
    • Regularized Least Squared
    • Support Vector Machines
    • Neural Networks
    • Decision Trees & Ensemble Learning
    • Genetic Programming

Evening Poster Session

We will have our annual Poster Session Wednesday Evening titled: Machine Learn This. Please bring a poster on your work on machine learning or on your favorite data for which we will discuss machine learning approaches. Please indicate if you are planning to bring a poster when you register.

Thursday, August 2nd

Machine Learning on DFT and Experimental Databases

Dane Morgan (U. of Wisconsin)

  • Introduction to DFT
  • Machine Learning of DFT Based Data

Kamal Choudhary (NIST)

  • JARVIS-ML: 2D/3D materials screening and genetic algorithm with ML model

Valentin Stanev (U. of Maryland)

  • Machine Learning of Experimental Databases: Case Study on Superconductivity

Cormac Toher, Corey Oses, and David Hicks (Duke U.)

  • Tutorial on AFLOW

Banquet

Friday, August 3rd - University of Maryland at Shady Grove

Workshop on Machine Learning Quantum Materials

8:15am
Johnpierre Paglione and Ichiro Takeuchi
University of Maryland
Welcome
Theory
Chair: Andy Mills
Flatiron Institute/Columbia University
8:30am
Roger Melko
University of Waterloo/Perimeter Institute
Reconstructing quantum wavefunctions with stochastic neural networks
9:00am
Alexander Balatsky
Nordita/LANL
Organic Materials Database and a search for novel organic quantum materials
9:30am
Karin Rabe
Rutgers University
First principles data and modeling for design and discovery of functional perovskite superlattices
10:00am
Mike Norman
ANL
Do Materials Genome Concepts Really Work for Predicting New Superconductors?
10:30am
Coffee Break
Experimental Approaches
Chair: Jiun-Haw Chu
University of Washington
11:00am
Ichiro Takeuchi
University of Maryland
Combinatorial Experimentation and Machine Learning for Materials Discovery
11:30am
Sergei Kalinin
Oak Ridge National Laboratory
New Opportunities in Electron Microscopy: from Learning Physics to Atom by Atom manipulations
12:00pm
Benji Maruyama
Air Force Research Laboratory
12:30pm
Lunch
Computational Approaches
Chair: Antoine Georges (Flatiron Institute)
2:00pm
Stefano Curtarolo
Duke University
data, disorder and materials
2:30pm
Giuseppe Carleo
Flatiron Institute
An introduction to neural-network quantum states, and to the latest applications in quantum chemistry and quantum computing
3:00pm
Gus Hart
Brigham Young University
Machine Learning for Materials: Importance of Representations
3:30pm
Miles Stoudenmire
Flatiron Institute
"Wavefunctions" of Data: Using Tensor Networks for Machine Learning
4:00pm
Coffee Break
4:30pm
Panel Discussion on Future Directions of Machine Learning Quantum Materials
Chairs: Johnpierre Paglione and Ichiro Takeuchi
University of Maryland
Panelists:
Antoine Georges (Flatiron Institute)
Andy Millis (Flatiron Institute/Columbia University)
Mike Norman (Argonne National Lab)
Alexander Balatsky (Nordita/LANL)
5:30pm
Concluding Remarks

Conference Support

Colleges A. James Clark School of Engineering
The College of Computer, Mathematical, and Natural Sciences

Communicate Contact Us
Contact the Webmaster
Google+
Follow us on TwitterTwitter logo

Links Privacy Policy
Sitemap
RSS

Copyright The University of Maryland University of Maryland
2004-2018