Session 3

Coming soon…

Brett Savoie is the inaugural Coyle Mission Collegiate Professor of Engineering in the Department of Chemical and Biomolecular Engineering at the University of Notre Dame. Brett graduated with degrees in chemistry and physics from Texas A&M University in 2008, obtained his Ph.D. in theoretical chemistry from Northwestern University in 2014, and from 2014-2017 was a postdoc with Thomas Miller at Caltech. In 2017, Brett joined the faculty of the Davidson School of Chemical Engineering at Purdue University, where he established an independent research group to develop physics-based and machine learning methods to characterize and discover new organic materials. In 2022, Brett was promoted to the Charles Davidson Associate Professor of Chemical Engineering at Purdue University. In July 2024, Brett joined the faculty at Notre Dame to advance computational materials research and lead the university’s Scientific AI (SAI) initiative. Brett is the recipient of the ACS PRF, NSF CAREER, Dreyfus Machine Learning in the Chemical Sciences, and ONR YIP awards.

The development of electronic polymers has lagged behind the rapidly growing demand for advanced materials in flexible devices, large-scale printable electronics, and sustainable energy applications. This slow progress stems from the vast design space and complex processing conditions required, making precise design a formidable challenge. Balancing critical properties—like electronic mobility, strength, ionic conductivity, sustainability, and processability—further complicates the development pipeline. To address these challenges, we are pioneering new approaches to accelerate the electronic polymer development pipeline. While AI-driven materials research has seen rapid advances, applying these technologies to electronic polymer design remains challenging, particularly due to the limited data availability stemming from the lengthy design-make-test-analyze cycle in electronics. Our work focuses on accelerating the design of functional polymers by leveraging AI and automated robotic experimentation. This talk will highlight research conducted in our self-driving lab, Polybot, covering topics from the inverse discovery of electrochromic polymer structures, the controlled assembly of conducting polymers through solution processing, and the discovery of design principles for mixed-conducting polymers in electrochemical transistors. We will also discuss ongoing efforts to evolve Polybot into a more adaptive system with enhanced human-machine interfaces and as a community resource by building a specialized functional polymer digital ecosystem.

Jie Xu is a scientist at Argonne National Lab and a CASE Affiliated Scientist at the University of Chicago’s Pritzker School of Molecular Engineering. Her research focuses on precision engineering of functional polymers through molecular packing structure engineering, chemical design, and self-driving laboratories (https://www.anl.gov/cnm/polybot). Jie earned her PhD in chemistry from Nanjing University, specializing in nanoconfined soft matter, and completed postdoctoral training at Stanford in stretchable electronics. She received the Materials Research Society Postdoctoral Award and is named to the MIT Technology Review’s list of Innovators Under 35, Newsweek list of America’s Greatest Disruptors as a budding disruptor, and 2023 Polymeric Materials: Science and Engineering Early Investigator Honoree by the American Chemical Society.

The seamless integration of synthetic materials with biological systems long remains a grand challenge, often curtailed by the sheer complexity of the cell-material interface. For decades, biomaterial scientists and engineers have designed around this complexity by rationally designing new materials one experiment at a time. However, recent advances in laboratory automation, high throughput analytics, and artificial intelligence / machine learning (AI/ML) now provide a unique opportunity to fully automate the design process. In this seminar, we put forth our efforts to develop a biomaterials acceleration platform (BioMAP) (i.e., self-driving biomaterials lab) that can rapidly iterate through design spaces and identify unique material properties that perfectly synergize with biological complexity.

Adam Gormley is an Associate Professor of Biomedical Engineering at Rutgers University, Executive Editor of Advanced Drug Delivery Reviews, and co-founder of Plexymer, Inc. Prior to Rutgers, Adam was a Marie Skłodowska-Curie Research Fellow at the Karolinska Institutet (2016) and a Whitaker International Scholar at Imperial College London (2012-2015) in the laboratory of Professor Molly Stevens. He obtained his PhD in Bioengineering from the University of Utah in the laboratory of Professor Hamid Ghandehari (2012), and a BS in Mechanical Engineering from Lehigh University (2006). In January 2017, Adam started the Gormley Lab which seeks to develop bioactive nanobiomaterials using robotics and artificial intelligence. Dr. Gormley is currently the PI of an NIH R35 MIRA Award, an NSF CBET Award, and an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) Award. He is the recipient of the A. Walter Tyson Assistant Professorship, the Young Innovator Award by Cellular and Molecular Bioengineering, and the Presidential Fellowship for Teaching Excellence.