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RASEI Fellow Steven Spurgeon has been named a (APS), nominated through the Society's .Ìý

APS Fellowship is one of the most respected honors in the physical sciences. It reflects the recognition of one's scientific peers, and each year's new cohort is limited to no more than one-half of one percent of the Society's membership.

Spurgeon was recognized ‘for pioneering the integration of autonomous microscopy and advanced data science to reveal fundamental physical mechanisms in complex oxides, interfaces, and radiation environments — bridging experiment and theory to forge a new paradigm for AI-accelerated materials discovery’

Spurgeon is a Senior Materials Data Scientist at the U.S. Department of Energy’s National Laboratory of the Rockies (NLR), where his research centers on autonomous experimentation and AI-driven materials characterization. He also holds a joint faculty appointment in Metallurgical and Materials Engineering at the Colorado School of Mines, where he leads . In Spurgeon's own words, the goal of his work is "accelerating the discovery of new materials by teaching scientific instruments how to think."

Spurgeon was applying AI and machine learning to materials data well before the current AI wave. Having spent years analyzing large sets of microscopy images to identify patterns and defects, he began asking whether computers could be trained to do that work. That question set him on a path that now spans a wide array of applications.

That early start has produced a substantial body of work: more than 90 publications, seven software packages, and two licensed technologies. In 2024, Spurgeon and his collaborators won an R&D 100 Award for the Autonomous Electron Microscope (AutoEM), developed with instrument manufacturer JEOL while he was at Pacific Northwest National Laboratory (PNNL). AutoEM is an AI-embedded microscope that helps guide experiments as they happen. At NLR, he is now extending this work through AM³, a platform for agentic, autonomous electron microscopy. In 2025, the Microscopy Society of America awarded him its Burton Medal. This year, his alma mater Drexel University named him to its 40 Under 40 list.

Spurgeon's research harnesses AI to understand and control how structure-property relationships emerge in functional materials, with applications in power electronics and microelectronics, energy storage, and quantum systems. His work at NLR and Mines spans autonomous experimentation across materials processing and characterization, data-driven electron microscopy, and atomistic modeling. Cutting-edge instrumentation now enables high-throughput experiments that rapidly generate multimodal data. That speed is of limited value, however, if researchers can't analyze the results and understand what they mean. That's where machine learning comes in: instruments trained to classify results and act on the data in real time. This approach helps researchers keep pace with high-throughput experimentation and more effectively steer autonomous materials design and synthesis.

Recognition from a physics society's data science group shows how central AI and data-driven methods have become across scientific disciplines. It also echoes the founding mission of APS, established in 1899 "to advance and diffuse the knowledge of physics" by connecting U.S. scientists to a growing international research community. Spurgeon's work shares that spirit: it is deeply collaborative, spanning national laboratories, universities, and industry partners.

Congratulations to Steven Spurgeon on this well-deserved recognition of the pioneering work he and his team are doing in data-driven, autonomous materials design.