Materials Horizons Emerging Investigator Series: Professor Chris Bartel, University of Minnesota, USA

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Abstract

Our Emerging Investigator Series features exceptional work by early-career researchers working in the field of materials science.

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Chris Bartel (ORCID: https://orcid.org/0000-0002-5198-5036 ) is an assistant professor in the Department of Chemical Engineering and Materials Science (CEMS) at the University of Minnesota. Prior to joining CEMS in 2022, he earned a PhD in chemical engineering from the University of Colorado and worked as a postdoctoral researcher in materials science at UC Berkeley. Chris now leads the “Design of Materials on Computers Lab,” which leverages first-principles calculations, thermodynamic modeling, solid-state chemistry, and machine learning to accelerate the design of solid-state materials for energy-related applications. He has been recognized as a Scialog Fellow in Negative Emissions Science and Sustainable Minerals, Metals, and Materials. Chris grew up near New Orleans and earned a BS in chemical engineering from Auburn University. To learn more about the latest work from his research group, please visit https://bartel.cems.umn.edu.

Read Chris Bartel’s Emerging Investigator Series article ‘Establishing baselines for generative discovery of inorganic crystals’ ( https://doi.org/10.1039/D5MH00010F ) and read more about him in the interview below:

MH: Your recent Materials Horizons Communication establishes comprehensive baselines for the generative discovery of inorganic crystals, comparing traditional methods with generative approaches. How has your research evolved from your first article to this most recent article and where do you see your research going in future?

CB: The biggest evolution of my research is the prominence today of machine learning and especially deep learning models in materials science. When I was publishing my first articles around 2016, machine learning papers were popping up here and there, but deep learning was seldom being used in the prediction of material properties. In less than a decade later, this field has exploded. The quality and diversity of open source tools and datasets has enabled new lines of research and rapidly shifted the kinds of questions we can answer as computational materials scientists.

MH: What aspect of your work are you most excited about at the moment?

CB: I’m most excited about my collaborative research with experimental groups, which includes coming up with synthesis pathways for new materials and designing materials for electrochemical energy storage and conversion applications. I feel these collaborations put a lot of good pressure on computational researchers as it forces us to deal with “real” observations, whether or not they agree with our current model systems or interpretation of what’s happening. At the same time, as computational researchers, we are able to model with a level of atomistic detail that’s seldom possible experimentally, allowing us to hopefully tease out the true origin of experimental observations. There is nothing more exciting in my research than having one of our predictions tested experimentally, especially when the results surprise us.

MH: In your opinion, what are the most important questions to be asked/answered in this field of research?

CB: I’d say the most important question (and one related to this paper) is “what defines the ‘discovery’ of a new material?” This leads to questions about novelty, disorder, synthesis, and characterization. The pipeline for discovery is complex and requires expertise across the spectrum of materials science. Research that addresses shortcomings in each of these areas and bridges the gaps between them will help improve the pace of materials design for many important technologies.

MH: What do you find most challenging about your research?

CB: One of the biggest challenges these days is keeping up with all the exciting research in the deep learning field at large and even within the smaller sub-field of deep learning applied to materials science and chemistry problems. The field is moving so quickly that staying on top of everything is impossible. I just hope to catch the right bits and pieces that help us solve the problems we’re working on.

MH: In which upcoming conferences or events may our readers meet you?

CB: I usually attend the AIChE Fall meeting, the MRS Spring meeting, and various other conferences related to machine learning (e.g., CECAM), electrochemistry (e.g., ECS), and solid-state chemistry (e.g., NASSCC).

MH: How do you spend your spare time?

CB: I spend most of my spare time enjoying the company of my wife, our daughter, and our dog. We like to take long walks around Minneapolis checking out the lakes and great neighborhood restaurants around the city. When I’m not with them, I’m often practicing yoga, playing tennis, or obsessively watching my hometown sports teams, the New Orleans Saints and New Orleans Pelicans.

MH: Can you share one piece of career-related advice or wisdom with other early career scientists?

CB: Becoming (and being) a scientist is a continuous process that involves lots of small steps, and research comes with countless ups and downs. It can be intimidating, especially early on, to see so many “finished products” like published papers or conference talks from established scientists. What you don’t often see are the hours, days, and years of reading, thinking, writing, and struggling through many failed ideas before getting to that finished product. My advice is to aim for getting a little bit better and learning a little bit more each week without putting too much pressure on yourself to figure everything out right away.

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