Graph Convolutional Networks for Atomic Structures

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• Rachel Kurchin, Carnegie Mellon University
• Monday 07 December 2020, 17:00-17:30
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09.

If you have a question about this talk, please contact Bingqing Cheng .

I am developing two packages in the Julia programming language to facilitate graph-based machine learning in atomic systems: crystals, surfaces, molecules, etc. In this talk, I will first give a brief tutorial on the math behind graph convolution, then introduce the packages: ChemistryFeaturization.jl for building and featurizing the atomic graphs, and AtomicGraphNets.jl for building and training the models. I will also compare the capabilities and performance of my code to the Python-based implementation of a similar model.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09

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