Center for Functional Nanomaterials Seminar

"Using Modeling and Machine Learning to Accelerate High-Throughput Experimental Materials Discovery"

Presented by Jason R. Hattrick-Simpers, National Institute of Standards and Technology

Monday, November 13, 2017, 11:00 am — CFN, Bldg. 735, Conference Room A, 1st Floor

Over the past 10 years there has been a resurgent interest in the development of novel metallic alloys, both as multiple principle component solid solution alloys, so-called high entropy alloys (HEA) as well as amorphous metallic glasses. Although a number of empirical rules have been proposed for the prediction of potential alloy compositions, calculating their stability and quantifying their properties of interest at operating temperatures from first principles represents a significant challenge. In fact, even high-throughput experimental studies struggle to effectively explore such large composition-processing-property parameter spaces efficiently.
Here, I will discuss an approach that seeks to address the rational experimental exploration of such alloys by combining theory, experiment and data science. Our approach is to use insights from the literature, theory, and/or data mining to identify the regions of parameter space most likely to yield interesting materials. We then employ computationally guided high-throughput synthesis techniques to strategically probe composition and processing space. In situ synchrotron diffraction studies yield tens of thousands of data sets describing the evolution of the alloy phase and corrosion products. The data are evaluated using automated knowledge extraction techniques, enabling us to assess our experiments, update the models used to generate the initial lead materials, and plan the next material system to study. In this talk, I will emphasize our recent work using these techniques to investigate phase stability in metallic glasses.

Hosted by: Matthew Sfeir

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