Machine Learning for Materials Research

Tuesday, June 27, 2017
8:00 a.m.
Kim Building
Martha Heil
301 405 0876

Machine learning for Materials Research Workshop & Bootcamp

June 27-30, 2017

Jeong H. Kim Engineering Building University of Maryland, College Park


Register now! https://www.nanocenter.umd.edu/events/mlmr/register/



The bootcamp consists of three days of lectures and hands-on exercises covering a range of data analysis topics from data pre-processing through advanced machine learning analysis techniques. The hands-on exercises will focus on demonstrating practical use of machine learning tools on real materials data. Attendees will learn to analyze a range of data types from scalar properties such as material hardness to high dimensional spectra and micrographs.

Sample topics include:

  • Identifying important features in complex/high dimensional data
  • Visualizing high dimensional data to facilitate user analysis.
  • Identifying the fabrication 'descriptors' that best predict variance in functional properties.
  • Quantifying similarities between materials using complex/high dimensional data


The workshop will feature talks by top researchers in the field and open discussions in which attendees can discuss their data analysis needs with experts.



Ichiro Takeuchi University of Maryland, Materials Science & Engineering

His recent research emphasis has been on development of informatics techniques to effectively handle, visualize, and analyze the large amount of data that are generated from combinatorial experiments.

Gilad Kusne National Institute of Standards & Technology, Materials Measurement Science Division

Kusne develops machine learning algorithms to accelerate the discovery and optimization of advanced materials as part of the Materials Genome Initiative at NIST.


Alexei Belianinov Oak Ridge National Laboratory, Center for Nanophase Materials Sciences

He incorporates multivariate statistical computation methods into High Performance Computing environments to process vast quantities of experimental and theoretical data and enable a real-time knowledge driven approach to science.

Tim Mueller Johns Hopkins University, Materials Science & Engineering

One of Mueller's research interests is materials informatics. In particular, his group created a database of efficient k-point grids to reduce the cost of calculations that require integration over the Brillouin zone.

Daniel Samarov National Institute of Standards and Technology, Information Technology Laboratory

Samarov is a mathematical statistician at NIST interested in multivariate statistics, machine learning, nonparametric statistics, sparse methods, big data applied to any and all interesting problems.


Machine Learning for Materials Research: Bootcamp

Data Fundamentals
Data Preprocessing

Supervised Learning
Theory, Data, Algorithms

Unsupervised Learning
Theory & Algorithms

Filtering: Noise Smoothing
Background Subtraction
Feature Extraction

  • Cross-correlation Wavelets
  • Edges
  • Closed Boundaries
  • Shapes

Data Handling

  • Cross Validation
  • Prediction


  • Regularized Least Squared
  • Support Vector Machines
  • Neural Networks
  • Decision Trees & Ensemble Learning
  • Genetic Programming

Similarity Measures
Latent Variable Analysis
Spectral Unmixing
Matrix Factorization


Audience: Graduate  Faculty  Post-Docs 


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