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Patrick Riley1

1, Google Research, Mountain View, California, United States

Deep learning has brought many significant successes and a lot of hype in many fields. Through two examples of novel architectures applied to chemical problems I'll illustrate a core idea of learnable feature representations. I'll cover the main ideas and empirical results of Message Passing Neural Networks (for graph data) and Tensor Field Networks (for naturally rotationally equivariant 3D tensor data). These examples should help materials researchers understand what they should, and should not be, excited about modern machine learning methods.

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