Meet CBI fellow Evan Spotte-Smith

My name is Evan Spotte-Smith (they/them), and I am excited to be part of the new generation of awarded CBI post-doctoral fellows at CMU. I obtained my Ph.D. in materials science and engineering from the University of California, Berkeley, where I was advised by Kristin Aslaug Persson and worked on applying computational simulations (e.g., quantum chemistry, statistical mechanics) and data science (e.g., reaction networks, machine learning) to understand reactivity in complex environments. I am also interested in thinking about developing ethical standards and policy to guide applications of machine learning, particularly in the chemical sciences.

What are your long-term goals?

I plan to continue my work in academic research at the intersection of computational chemistry, data science, and sustainability. Starting in Fall 2025, I'll be joining the CMU faculty as an assistant professor in chemical engineering. I'd love to continue collaborating with CBI and other organizations/groups at CMU on problems related to energy storage, sustainable chemical manufacturing, and reactivity more broadly.

How do you envision that the CBI fellowship program will help you reach the above goals?

CBI is the perfect jumping-off point for my career. Through this fellowship, I have the resources to pursue research that will give me new skills—particularly in machine learning and catalysis—and set me up for success in my faculty career. The mentorship provided by CBI is also invaluable. Bosch has a ton of rich expertise, and my mentors have already been quite generous in trying to connect me to these resources.

What is the biggest challenge(s) that you hope to tackle?

I'm motivated to address climate change and our current environmental catastrophe. During my postdoctoral fellowship, I'll be looking at ways to decarbonize the synthesis of feedstock chemicals. That is, can we use renewable energy and electrochemistry to more efficiently produce molecules like ammonia (a key precursor to fertilizers and one of the most widely synthesized chemicals in the world)? If we can, then there's a real possibility to dramatically reduce the greenhouse gas emissions of the chemical industry. When I'm not looking at electrosynthesis, I've also been thinking a lot about improving plastic recycling and batteries.

What obstacles do you see in addressing these challenges?

In the short term, most of my challenges are methodological. How do we effectively represent electrochemical systems without explicitly accounting for electrons? What information is most important for machine learning models to learn chemical reactivity? But stepping back, I think my biggest challenge will be connecting the research that I do—which mainly looks at the fundamental mechanisms of atoms and molecules—to real-world applications, whether that's through laboratory-scale experiments or industrial factories. It's great to reach a deep understanding of a reaction, or to theorize a new approach, but that won't do anything for climate change if no one can use what I've discovered.