MS&E Seminar Series: Dr. Saryu Fensin, Los Alamos National Laboratory

UW-Madison Department of Materials Science and Engineering welcomes Dr. Saryu Fensin. Her seminar, “SPARK: Accelerating Alloy Discovery with AI and Self-Driving Labs”, will take place on Thursday, November 13 from 1-2 p.m. in MSE 265.

Bio

Dr. Saryu J. Fensin is a staff scientist at Los Alamos National Laboratory (LANL) in the Materials Physics and Applications Division. Her research career has been defined by advancing the understanding of deformation and failure in materials under extreme conditions, with particular emphasis on high strain rate phenomena relevant to national security and advanced manufacturing. She has led experimental and modeling efforts to uncover how microstructure, defects, and interfaces control mechanical response across a wide range of alloys and composites.

Building on this foundation, Dr. Fensin has recently expanded her portfolio to include AI- and automation-driven materials discovery, serving as Principal Investigator on several projects that integrate machine learning with high-throughput experiments and autonomous laboratories. Her group’s work explores the strengths and limitations of AI in predicting alloy performance and demonstrates how thin film screening, bulk validation, and automated synthesis can be connected in closed-loop workflows for accelerated discovery.

She received her Ph.D. in Materials Science and Engineering from the University of California, Davis, and has published widely on high-entropy alloys, shock physics, and automated approaches to alloy design. At LANL, she collaborates across disciplines to pioneer new paradigms for both fundamental understanding and rapid innovation in structural materials.

Abstract

The search for next-generation materials increasingly demands approaches that are faster and more predictive than trial-and-error. Our work began by exploring thin film deposition as a high-throughput method to screen refractory high-entropy alloys. Combinatorial sputtering allowed us to create entire libraries of compositions and rapidly probe phase stability and hardness trends. Thin films proved powerful for capturing intrinsic effects, such as the stability of single-phase BCC alloys, but direct comparisons with arc-melted bulk samples exposed their limits. Microstructural evolution—grain coarsening, segregation, and defect formation—often broke the simple link between film hardness and bulk yield strength. These insights motivated us to expand from thin films toward systematic bulk synthesis and validation.

To accelerate this process, we have coupled artificial intelligence (AI) and machine learning (ML) with experimental design. Our models predict yield strength and ductility across multi-principal element alloys, narrowing vast compositional spaces to promising candidates. Yet they also reveal their blind spots: defect-driven phenomena like segregation-induced embrittlement remain difficult to capture.

I will also introduce our automated laboratory platforms, where alloy synthesis, property measurement, and characterization are integrated into closed-loop, “self-driving” workflows. These systems enable rapid iteration between AI predictions and experiment while building the rich datasets needed to improve future models.

Together, thin film screening, bulk validation, and AI-driven automation point toward a new paradigm in materials discovery—where human intuition, machine learning, and autonomous laboratories combine to design structural alloys with unprecedented speed and precision.