Interpretable Models for the Physical World with Prof. Qian Yang

Two important challenges in scientific machine learning are interpretability – the ability to understand how and why models are making their predictions, and extrapolability – the ability to make predictions outside the data regime that the model was trained on. In the field of scientific machine learning, one can often leverage problem structure to address these challenges. In essence, having a scientific understanding of a problem enables us to “prime” the machine learning algorithm. This is important in materials science and chemistry, where practitioners may have a limited, but nonzero, understanding of how a system behaves.

In this talk, Prof. Qian Yang of the University of Connecticut will discuss three examples of how hierarchical structure and invariances can be used to build predictive machine learning models for materials properties. The first two examples use graph neural networks to automatically learn how substructures affect materials properties, and to learn properties of mixtures. Finally, the third example uses a hierarchical method for multi-class classification to recover structural information from experimental small angle scattering curves.

Join us for our reboot of Aionics Fortnightly on Thursday, August 17 at 10am PDT!