November 12, 2019 UMD Home FabLab AIMLab
MLMR 2019
Machine Learning for Materials Research Bootcamp
&
Workshop on Autonomous Materials Research
August 5, 2019 - August 9, 2019

Last updated: July 25, 2019, 11:58 am EDT

All sessions held in Zupnik Lecture Hall, Kim Engineering Building.

Monday, August 5th

Introduction to Python and Data Preprocessing

Alex Belianinov (ORNL)

  • 7:30am Breakfast and Registration
  • 8:30am Welcome and announcements
  • 8:45am Introduction and basics of python
  • 10:00am Coffee break
  • 10:30am Continue
  • 12:00pm Lunch
  • 1:00pm Background subtraction and data preprocessing
  • 2:30pm Coffee break
  • 3:00pm Continue
  • 5:00pm Reception Monday night
Tuesday, August 6th

Unsupervised Machine Learning

A. Gilad Kusne (NIST)

  • 7:30am Breakfast and Registration
  • 8:30am Unsupervised machine learning
  • 10:00am Coffee break
  • 10:30am Continue
  • 12:00pm Lunch
  • 1:00pm Continue unsupervised machine learning
  • 2:30pm Coffee break
  • 3:00pm Continue
Wednesday, August 7th

Supervised Machine Learning

Daniel Samarov (NIST)

  • 7:30am Breakfast and Registration
  • 8:30am Supervised machine learning
  • 10:00am Coffee break
  • 10:30am Continue
  • 12:00pm Lunch
  • 1:00pm Continue supervised machine learning
  • 2:30pm Coffee break
  • 3:00pm Continue
  • 5:00pm Poster session
  • 6:00pm Banquet dinner
Thursday, August 8th

Gaussian Processes and Active Learning, Computational Approaches and Databases

  • 7:30am Breakfast and Registration
  • 8:30am Gaussian Processes Daniel Samarov (NIST)
  • 10:00am Coffee break
  • 10:30am Active Learning A. Gilad Kusne (NIST)
  • 12:00pm Lunch
  • 1:00pm Introduction and Applications of AFLOW Cormac Toher, Cory Oses, David Hicks, and Marco Esters (Duke)
  • 2:30pm Coffee break
  • 3:00pm AFLOW continued Cormac Toher, Cory Oses, David Hicks, and Marco Esters (Duke)
  • 4:30pm Data Mining of Experimental Databases Valentin Stanev (UMD)
Friday, August 9th

Symposium on Autonomous Experimentation

Theory and Robot Science
Chair: Ichiro Takeuchi
University of Maryland
9:00am
Kirthevasan Kandasamy
UC Berkeley
Bayesian Optimisation and Design of Experiments: Applications in Materials Science, Drug Discovery, and Astrophysics
9:30am
Loic Roch
Univ. of Toronto
Self-driving laboratories with ChemOS
10:00am
David Yaron
CMU
Neural Networks that Do Quantum Chemistry
10:30am
Coffee Break
11:00am
Keith Brown
Boston University
A Bayesian Experimental Autonomous Researcher for Mechanical Design
11:30am
Michael Otte
UMD
An Emergent Group Mind Across A Swarm of Robots: Collective Cognition and Distributed Sensing via a Shared Wireless Neural Network
12:00pm
Lunch
Autonomous Approach to High-throughput Experimentation
Chair: A. Gilad Kusne
NIST
1:00pm
Michael Krein
Lockheed Martin
Automating materials process discovery and optimization: a transition perspective
1:30pm
Brian DeCost
NIST
Autonomous acquisition of composition-temperature phase diagrams via active factorization of X-ray diffraction data
2:00pm
Voramon Dheeradhada
GE
The role of machine learning in acceleration of parameter development for Nickel alloys in power bed fusion
2:30pm
R. Bruce van Dover
Cornell
Autonomous Agents Integrate Materials Theory, Experiment and Computation
3:00pm
Coffee Break
3:30pm
Valentin Stanev
UMD
Active and Cooperative Learning in High-Throughput Materials Studies
4:00pm
Panel Discussion
4:45pm
Wrap up & Closing Remarks
5:00pm
Departure

Organizers

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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