About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Using Uncertainty in Machine Learning to Inform Decision Making on Structural Characterization of Materials |
Author(s) |
Austin Mcdannald, Brian DeCost, A. Gilad Kusne |
On-Site Speaker (Planned) |
Austin Mcdannald |
Abstract Scope |
We show how quantifying and propagating uncertainty in the machine learning analysis of structural characterization data can inform the decision making about those measurements. This allows us to answer questions about what can be inferred from the measurements, and about what conditions or locations to investigate next. We present two case studies on this topic. In neutron powder diffraction for the identification of phase transitions there is a need to choose the temperature to measure at that quickly spans the range of interest without missing pertinent information. Since the transitions can be abrupt and hysteretic the experiment temperature is limited to only increase. We show how the Autonomous Neutron Diffraction Explorer uses uncertainty to efficiently navigate this space. In a second case study we show how uncertainty information can be used to inform the acquisition of orientation data from the Electron Backscatter Diffraction Measurement maps of grain structure. We encode the relevant physical constraints such as the requiring contiguous grains, and abrupt changes in orientation at grain boundaries, as well as the crystal symmetry in the algorithms and their uncertainty. This is key to providing an accurate understanding of the uncertainty landscape and allows us to choose the locations for new measurements. |
Proceedings Inclusion? |
Undecided |