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Data and tools

Some data and tools available for researchers include:

• PyTorch library for auto-differentiable EBSD analysis

• Custom SPPARKS code used to run “candidate grain” simulations of abnormal grain growth

• Code for actually running “candidate grain” simulations

• Process for deploying high-throughput experiments with MLflow and Docke

• Code for running computer vision and graph neural network experiments for predicting AGG in these simulations

Collaborate

We encourage researchers affiliated with the center to submit additional data and tools to Michael Bockstaller.