May 21, 2022 UMD Home FabLab AIMLab
MLMR 2021
Machine Learning for Materials Research Bootcamp
July 26 - 30, 2021


Bootcamp Schedule is Posted


The bootcamp schedule has been posted.

Registration is Live


Registration is now open. Click here to register.

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This year’s MLMR 2021 will be held virtually via zoom (like last year) July 26th-30th, 2021.

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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.


If you are a student (graduate, undergraduate, or high school), write to us first at, so we can send you a student discount code BEFORE you register. Write to us also for an academic discount code if you work at an academic institution (university, etc.) BEFORE you register.

Register here

Bootcamp Schedule

The bootcamp will run daily from 9:00am - 5:00pm (EDT). It will be live on zoom, and recorded sessions will be made available to participants afterwards.

Day 1:
  • Welcome and High-level intro to Machine Learning
  • Introduction to Python
  • Data Preprocessing
    • Filtering / Noise Smoothing
    • Normalization / Standardization
    • Background Subtraction
    • Feature Extraction: e.g. Cross-correction, wavelets, edges, boundaries, shapes
Day 2: Unsupervised Learning
  • Review of Linear Algebra and Notations
  • Dissimilarity Measures
  • Latent Variable Analysis
  • Spectral Unmixing / Matrix Factorization under constraints
  • Clustering
Day 3: Supervised Learning
  • Data Handling
  • Algorithms:
    • Regularized Linear Regression
    • The Kernel Trick
    • Gaussian Processes
    • Neutral Networks
    • Decision Trees & Ensemble Learning
    • Symbolic Regression
Day 4: Active Learning, DFT, and Natural Language Processing
  • Introduction to DFT and Tutorial on AFLOW
  • Machine Learning for DFT-based Data
  • Natural Language Processing
  • Active Learning, Bayesian Optimization, and Gaussian Processes
Day 5: Workshop on Advanced ML Techniques for Materials Discovery



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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