July 19, 2024 UMD Home FabLab AIMLab
MLMR 2020
Machine Learning for Materials Research Bootcamp
Workshop on Machine Learning Microscopy Data
July 20 - 24, 2020


We have moved to Online

This year's bootcamp and workshop has been moved online.

Save the Date!


This year's MLMR 2020 will be held from July 20th - 24th at The University of Maryland, College Park.

Online Bootcamp!

We have moved the bootcamp to an online platform due to COVID-19.

Bootcamp (Days 1-4)

Four days of lectures and hands-on exercises covering a range of data analysis topics from introduction to python and data pre-processing to advanced machine learning analysis techniques. Example topics include:

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

Hands-on exercises will include practical use of machine learning tools on real materials experimental data (scalar values, spectra, micrographs, etc.)

Scientists will also demonstrate how they performed recently published research, from loading and preprocessing data to analyzing and visualizing results, all in Jupyter notebooks. Day 4 will include hand-on exercises on how to use the AFLOW database online.

Please bring a poster showcasing your data for the poster session titled Machine Learn This! (evening of Day 3).



A. Gilad Kusne A. Gilad Kusne National Institute of Standards & Technology Materials Measurement Science Division
Alexei Belianinov Alexei Belianinov Oak Ridge National Laboratory Center for Nanophase Materials Sciences
Daniel Samarov Daniel Samarov National Institute of Standards and Technology Information Technology Laboratory
Ichiro Takeuchi Ichiro Takeuchi University of Maryland, College Park Department of Materials Science & Engineering

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

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