Foundations and Applications of Chemical Engineering Data Science


Foundations and Applications of Chemical Engineering Data Science

Prof. Jim Pfaendtner, University of Washington

12:30 p.m.–1:45 p.m., August 31, 2021   |   107 Carey Auditorium, Hesburgh Library

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between.

This talk will focus on Prof. Jim Pfaendtner’s group’s research in the area of discovery, characterization, and design of new molecules through the lens of data science.

Jim Pfaendtner
Prof. Jim Pfaendtner

He will introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

Prof. Pfaendtner will illustrate these phases by highlighting relevant research from his group, including de novo design of new molecules, data mining of the research literature, and the use of new generative models that can be used in proposing new candidate molecules.

Jim Pfaendtner is the Rogel Professor & Chair of Chemical Engineering and Professor of Chemistry at the University of Washington and Staff Scientist at Pacific Northwest National Laboratory. He holds a B.S. in Chemical Engineering (Georgia Tech, 2001) and a Ph.D. in Chemical Engineering (Northwestern University, 2007). He also serves as Associate Vice Provost for Research Computing at the University of Washington. Prof. Pfaendtner’s research focus is computational molecular science and his recent teaching interests are in the area of teaching data science skills.