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Graph Convolutional Networks for Atomic Structures

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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.

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