Erik Walsh

Erik Walsh is an American materials scientist and computational chemist recognized for pioneering the application of artificial intelligence and high-throughput computing to the discovery and design of novel functional materials. His research, conducted primarily at the Lawrence Berkeley National Laboratory and the University of California, Berkeley, focuses on accelerating the development of materials for energy storage, catalysis, and quantum information science. Walsh's work bridges the gap between first-principles calculations and experimental synthesis, establishing new paradigms in computational materials science.

Early life and education

Born in Boston, Massachusetts, Walsh demonstrated an early aptitude for mathematics and the physical sciences, participating in national competitions like the Intel Science Talent Search. He pursued his undergraduate studies at the Massachusetts Institute of Technology, earning a Bachelor of Science in Materials Science and Engineering with a minor in Computer Science. His undergraduate thesis, conducted under the guidance of Gerbrand Ceder, involved early work on high-throughput density functional theory calculations for lithium-ion battery cathodes. Walsh then moved to Stanford University for his doctoral studies, where he worked in the laboratory of Jens Nørskov at the SUNCAT Center for Interface Science and Catalysis. His PhD dissertation, completed in 2007, developed novel computational frameworks for screening alloy catalysts for reactions critical to renewable energy technologies, such as the oxygen reduction reaction.

Career

Following his PhD, Walsh was awarded a prestigious Director's Postdoctoral Fellowship at the Lawrence Berkeley National Laboratory, working within the Materials Sciences Division. In 2010, he transitioned to a staff scientist position at LBNL and joined the faculty of the Department of Materials Science and Engineering at the University of California, Berkeley as an assistant professor. He was promoted to associate professor in 2016 and to full professor in 2020. At UC Berkeley, Walsh leads the **Walsh Research Group**, which operates at the intersection of materials informatics, machine learning, and multiscale modeling. A cornerstone of his career has been leadership in major collaborative initiatives, including serving as a principal investigator for the DOE's Energy Frontier Research Center on Next Generation Batteries and as the director of the Materials Project's automation and data infrastructure core. His group's software tools, such as the **FireWorks** workflow system, are widely used across the global materials genomics community.

Personal life

Wahl maintains a private personal life, residing in the San Francisco Bay Area. He is an avid outdoorsman, frequently hiking in Point Reyes National Seashore and the Sierra Nevada. He has served on advisory boards for several startups in the cleantech and advanced materials sectors spun out from academic research, including those affiliated with Cyclotron Road. Walsh is also a committed mentor, actively supporting programs like the ACS Project SEED and Berkeley Summer Research Experience to increase diversity in STEM fields.

Awards and recognition

Walsh's contributions have been recognized with numerous awards from professional societies and government agencies. These include the Materials Research Society's **MRS Medal** in 2021 for "pioneering data-driven materials discovery," the Department of Energy's **Early Career Award** in 2015, and the American Physical Society's **Fellow** status in 2019. He has also received the TMS **Young Leader Professional Development Award**, the ACS PRF **Doctoral New Investigator Grant**, and the UC Berkeley **College of Engineering** **Distinguished Teaching Award**.

Publications

Walsh is a prolific author with over 180 peer-reviewed publications in high-impact journals including *Nature*, *Science*, *Nature Materials*, *Advanced Materials*, and *npj Computational Materials*. Several of his papers are considered seminal to the field of computational materials discovery. Key publications include "**A general-purpose machine learning framework for predicting properties of inorganic materials**" (*npj Computational Materials*, 2016), "**The high-throughput highway to computational materials design**" (*Nature Materials*, 2013), and "**Combinatorial screening for new materials in unconstrained composition space with machine learning**" (*Physical Review B*, 2014). He is also a co-author of the widely cited textbook *"Data-Driven Materials Science: A Primer."*

Category:American materials scientists Category:University of California, Berkeley faculty Category:Lawrence Berkeley National Laboratory people Category:1978 births Category:Living people