June 21, 2018 UMD Home FabLab AIMLab
MLMR 2018
Machine Learning for Materials Research
Workshop on Machine Learning Quantum Materials
July 30, 2018 - August 3, 2018
Monday, July 30th

Tutorial on Python & Data Fundamentals and Preprocessing

  • Filtering: Noise Smoothing
  • Background Subtraction
  • Feature Extraction
    • Cross-correlation Wavelets
    • Edges
    • Closed Boundaries
    • Shapes
Tuesday, July 31st

Unsupervised Machine Learning

  • Theory
  • Similarity Measures
  • Latent Variable Analysis
  • Spectral Unmixing
  • Matrix Factorization
  • Clustering
Wednesday, August 1st

Supervised Machine Learning

  • Data Handling
    • Cross Validation
    • Prediction
  • Algorithms
    • Regularized Least Squared
    • Support Vector Machines
    • Neural Networks
    • Decision Trees & Ensemble Learning
    • Genetic Programming
Thursday, August 2nd

Machine Learning on DFT and Experimental Databases

  • Introduction to DFT
  • Machine Learning of DFT Based Data
  • JARVIS-ML: 2D/3D materials screening and genetic algorithm with ML model
  • Machine Learning of Experimental Databases: Case Study on Superconductivity
  • Tutorial on AFLOW
Friday, August 3rd

Workshop on Machine Learning Quantum Materials

Organized by Johnpierre Paglione (U. of Maryland) and Ichiro Takeuchi (U. of Maryland)

Confirmed Invited speakers

Antoine Georges (Center for Computational Quantum Physics)
Karin Rabe (Rutgers)
Sasha Balatsky (LANL/Nordita)
Andy Millis (Columbia)
Roger Melko (Waterloo)
Shoucheng Zhang (Stanford)
Mike Norman (ANL)

Experimental Approaches

Ichiro Takeuchi (UMD)
Sergei Kalinin (ORNL)
Benji Maruyama (AFRL)
Jiun-Haw Chu (Univ. Washington)

Computational Approaches

Stefano Curtarolo (Duke)
Gus Hart (BYU)
Giuseppe Carleo (Flatiron)
Miles Soudenmire (Flatiron)

Conference Support

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

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