Annual Machine Learning for Materials Research Bootcamp

Summary

The Machine Learning for Materials Research Bootcamp is a week-long bootcamp that starts with basic Python, builds through machine learning fundamentals, and ends with how to build a recommendation engine for materials research. The bootcamp is fully hands-on using open source libraries. Mini-bootcamps have also been held at major conferences including MRS, APS, TMS, among others.

Description

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.

Annual Machine Learning for Materials Research Bootcamp Event Webpage

Major Accomplishments

The bootcamp has educated thousands of scientists from a total of 27 countries. Attendees include industry, academic, and national lab scientists. Represented industries span a wide range of industries semiconductors and microelectronics, microscopy, medicine, telecommunication, fabrics, among many others. Representatives also include scientists from 25+ national labs and 125+ universities.

Past attendees from around the world.