NIST/BNL High-Throughput Investigation of Chemical and Material Systems

Material Measurement Laboratory, Materials Measurement Science Division/Brookhaven Lab

NIST only participates in the February and August reviews.

This postdoctoral fellowship aims to study the relationship of structure and function in chemical and material systems by the integration of synchrotron X-ray measurement techniques with chemical processes and materials synthesis. The duty station would be at Brookhaven National Laboratory with the Synchrotron Science Group and involve occasional travel to Gaithersburg, MD. The candidate would be responsible for planning and performing high-throughput XAS, XRF, and XRD measurements mostly at NIST’s Beamline for Materials Measurements, collaborating with scientists from NIST Gaithersburg, and developing work-flow and data-analysis tools for managing and interpreting these experiments. Examples of science to be investigated with synchrotron measurement tools might include:

• Compositionally complex alloys, such as metallic glasses or high-entropy alloys, where local disorder as probed by X-ray Absorption Spectroscopy result in emergent material properties

• Machine-learning driven electrochemical deposition of coatings controlling surface properties of industrially relevant material surfaces where the machine learning is driven by in situ XAS, XRF, and XRD measurements

References:
“On-the-fly segmentation approaches for x-ray diffraction datasets for metallic glasses” F Ren, T Williams, J Hattrick-Simpers, A Mehta MRS Communications 7 (3), 613-620 (2017)

“Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments”  Fang Ren, Logan Ward, Travis Williams, Kevin J Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta Science Advances 4 (4), eaaq1566 (2018)

"Scientific AI in materials science: a path to a sustainable and scalable paradigm" Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt, Aaron Kusne, Eva Campo, Martin L. Green arXiv preprint arXiv:2003.08471 (2020)

materials genome initiative; high-throughput experimentation; combinatorial materials science; corrosion; machine learning; artificial intelligence; autonomous