Combinatorial Synthesis & High Throughput Characterization of Shape Memory Alloy Thin Film Libraries for Thermoelastic Cooling
POSTER
Abstract
We utilize combinatorial deposition of metallic alloy thin films and high throughput characterization to generate large sets of data for machine learning. Autonomous capability designed in our group to achieve rapid characterization with upgradable feedback loops is applied to shape memory alloy thin film libraries to determine composition with ambient transformation temperatures. We demonstrate the clustering analysis of crystal structure for fast identification, grouping, and qualitative assessment.
*This work was supported by Caloric Cooling Consortium (CaloriCool). The Consortium is a member of the US Department of Energy (DoE) Energy Materials Network, and is supported by the Advanced Manufacturing Office of the Office of Energy Efficiency & Renewable Energy of the DoE. NAH's work is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1322106.
Presenters
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Naila Al Hasan
- Graduate Research Assistant