Abstract: Materials science presents a unique set of challenges and opportunities for machine learning methods in terms of data size, data sparsity, available domain knowledge, and multi-scale physics. In this talk, Dr. Ling will discuss how machine learning can be used to accelerate materials discovery through a sequential learning workflow. You'll examine how domain knowledge can be integrated into data-driven models, the role of uncertainty quantification in driving exploration of new design candidates, and how to forecast the impact of a data-driven approach on a given materials discovery campaign.
Refreshments will be served from 1-1:30pm on a first-come, first-served basis.
Speaker Bio: Dr. Julia Ling received her bachelors in Physics from Princeton University and her PhD in Mechanical Engineering from Stanford University. She was a Harry S. Truman Fellow at Sandia National Labs, where her researched focused on applying machine learning to turbulence modeling. She is currently the Director of Data Science at Citrine Informatics, leading a team that applies data-driven methods to materials science applications.