Training graduate students to tackle sustainability issues across disciplines

When Andrea Diaz first became a chemical engineer, she set her sights on improving sustainability in consumer-packaged goods. But she quickly discovered a gap between the research being done in academia and the conversation around sustainability within the industry.

Still determined, Diaz took positions with a couple more companies, seeking more depth in research but kept encountering similar limitations. She was surprised but she was ready to make a change.

Diaz returned to academia, joining the Patel Group at the University of Chicago’s Pritzker School of Molecular Engineering where she's taking part in AI-enabled Molecular Engineering of Materials and Systems for Sustainability (AIMEMS), a program aimed at supporting new approaches for graduate education training in science, technology, engineering, and mathematics (STEM) with the ultimate goal of helping students develop the skills, knowledge, and competencies needed to pursue a host of STEM careers.

Andrea Diaz
Andrea Diaz

Embedded in this program is the idea that artificial intelligence, or AI, is a powerful tool that is rapidly transforming almost every aspect of society. Through the AIMEMS program, UChicago graduate students in engineering, computer science, the social sciences and the humanities form cross-disciplinary teams to learn technical and professional skills to develop novel solutions for global challenges. By integrating AI across these fields, new opportunities for creating complex, multifunctional materials, processes, and systems are emerging.

Using automated AI programs allows researchers to identify key characteristics of materials and model millions of material combinations in much shorter timeframes. They can use these tools, for example, to engineer plastics that are more recyclable or biodegradable, or find combinations of materials that would produce more efficient batteries.

Faculty involved with the effort hope that by teaching students to integrate AI from the start, graduates will be able to dramatically accelerate scientific discovery and technological innovation.

As part of this effort, AIMEMS students attend boot camps to learn skills outside their fields and are paired with mentors in both academia and industry. They can also leverage user facilities at Argonne National Laboratory.

“Problems surrounding energy, water, and sustainability aren’t just pure science problems — they are societal problems,” said Diaz, now a fellow in the program whose research involves upcycling plastic waste. “These challenges require a different approach, and programs such as this one are a great way to develop a new perspective.”

After completing the first year of the effort, both students and faculty are excited about the possibilities.

“PME is already structured to approach global challenges through an interdisciplinary lens, and the AIMEMS program is now providing additional support to train our graduate students to use this thinking throughout their careers,” said Juan de Pablo, Liew Family Professor of Molecular Engineering and the program’s principal investigator. “Even after one year we are already seeing the success of this transformative way of approaching graduate education, and our students are primed to become leaders at the frontiers of knowledge in AI-enabled molecular engineering.”

Connecting across disciplines to understand the emerging role of AI

For AIMEMS fellow Aaron Peng, a graduate student in the de Pablo Group, the program is a way for him to build a new network while preparing for a career in academia. His research involves using machine learning to generate novel polymer candidates for metal-free batteries, and the program’s emphasis on interdisciplinary exposure, such as in brown bag lunches – where students gather to discuss their research and potential crossover opportunities – has him excited to find new collaborators not only in the PME but in other departments such as computer science and the social sciences.

Aaron Peng
Aaron Peng

He also is developing a curriculum for a boot camp this summer to teach AIMEMS students outside of engineering the basics of materials and energy research.

“I’m excited to learn new teaching skills and to learn from my fellow students,” he said. “Hopefully the techniques we work on together will be easily translatable to a large host of problems.”

One member of that network is AIMEMS fellow Eamon Duede, a graduate student in the Department of Philosophy and the Committee on the Conceptual and Historical Studies of Science who works to understand how scientific institutions and fields interact with one another and society to propel research and outcomes in certain directions.

He joined the AIMEMS program, with its focus on connecting science with societal needs, to better understand the emerging role of AI in science.

“AI is becoming increasingly embedded in these interfaces of institutions and scientific discovery of things like novel materials, and I’m interested in what role AI is meant to play and what role it actually plays,” he said.

Eamon Duede
Eamon Duede

Through the program, he is working with an interdisciplinary group of researchers — including from Argonne — to develop AI models that can learn the language of science and ultimately be used to discover new properties of materials or physical systems.

“This program engages a diverse set of disciplines to grapple with these problems, which is a very UChicago thing to do,” he said.

The AIMEMS program provides students with opportunities to network outside of the University as well. Both Diaz and Peng traveled to Paris this spring for a conference on sustainability at the UChicago Center in Paris. There, they met with leaders in the field from across Europe, including from some of the region’s top companies.

“The event really showed the kind of collaboration that was possible globally when people are focused on research,” Diaz said. “There were shared ideas and discussions around problems and potential solutions. I thought, ‘This is perfect.’”

AMIEMS is funded through the National Science Foundation's Division of Graduate Education.