2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session K53: Designing Neural Networks for the Structure of Physics Data
3:00 PM–6:00 PM,
Tuesday, March 7, 2023
Room: Room 307
Sponsoring
Units:
GDS DCOMP
Chair: William Ratcliff, National Institute of Standards and Technology
Abstract: K53.00006 : Metric geometry tools for automatic structure phase map generation
4:48 PM–5:00 PM
Abstract
Presenter:
Kiran Vaddi
(University of Washington)
Authors:
Kiran Vaddi
(University of Washington)
Karen Li
(University of Washington)
Lilo Pozzo
(University of Washington)
Extracting a phase map that provides a hierarchical summary of high-throughput experiments is a long-standing bottleneck for achieving acceleration in material discovery. A phase map that underpins the inherent properties of materials is typically denoted using a composition-structure map but can be extended to other relevant parameters such as synthesis. In this talk, we describe a statistical tool to efficiently obtain a phase map from high-throughput measurements. Specifically, we focus on the multi-scale characterization of nanostructures using small-angle scattering (SAS) that identifies structural features and correlations at different length scales when the scattering intensity at different angles is measured. The resulting plot of intensity vs wave-vector q, which is related to angle, is then inspected to understand and correlate structural features to material properties, composition, and processing conditions combinedly defined as the design space. Advances in high-throughput experiments and measurement speeds at synchrotron facilities allow us to collect high-quality data at a much faster rate, but the analysis of such data then becomes a bottleneck. A phase map provides a quick summary of correlations in the design space based on the structures formed and characterized by their SAS profiles. Prior studies have shown that the phase map needs to be continuous over the design space and the correlations are to be defined by a similarity between SAS profiles. We pose both constraints as a geometric feature of the phase map where continuity is obtained by defining a shape-based metric topology of SAS profiles and geometric diffusions defined by the linear operators on the design space. We apply the proposed methodology to scattering, diffraction, and spectroscopy to showcase the broad applicability of the method. We show that resulting phase maps are continuous, have an inherent shape similarity between regions identified as the same phase, and are invariant to shifted, broad, or missing peaks that might result from experimental limitations.