Opportunity at National Institute of Standards and Technology (NIST)
High-throughput Materials Science Study of Multi-Component Alloys
Material Measurement Laboratory, Materials Measurement Science Division
Please note: This Agency only participates in the February and August reviews.
|Hattrick-Simpers, Jason Ryan
We are developing machine learning-driven autonomous research systems, designed with the goal of accelerating the discovery and optimization of advanced materials. These systems combine machine learning with machine-controlled materials synthesis and characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior physics knowledge into the data analysis, including both physics theory and databases of experimental and computational materials property data.
We are particularly interested in using these autonomous systems to verify and identify phase maps for thin films as well as phase maps for aqueous electrochemical materials.
The team has ongoing projects in half-heusler alloys for thermoelectric materials, intermetallic hydride materials for hydrogen refueling, high entropy alloys for molten salt reactors and metallic glasses for structural applications. We also has ongoing projects for offline and on-the-fly (e.g., real-time, during the measurement experiment) analysis of high throughput combinatorial “library” experiments.
High-throughput Experimental Science; corrosion; amorphous materials;
high entropy alloys; machine learning; artificial intelligence;
combinatorial materials science; structural materials
Open to U.S. citizens
Open to Postdoctoral applicants