About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Latent Variable Rietveld Model for High Throughput Quantitative X-ray Diffraction Analysis |
Author(s) |
Brian DeCost, Austin McDannald, Howie Joress, Jason Hattrick-Simpers |
On-Site Speaker (Planned) |
Howie Joress |
Abstract Scope |
Recent advances in applied machine learning have enabled automated qualitative analysis of high throughput x-ray diffraction data. However, expert inspection and interpretation of model outputs remains an integral component current unsupervised diffraction models, and even supervised methods are not yet capable of quantitative diffraction analysis. We seek to bridge this gap in high throughput quantitative analysis by combining a flexible probabilistic machine learning approach with established physics-based Bayesian Rietveld modeling approaches. The joint analysis of related diffraction data enabled by this approach should allow for higher sensitivity to minority or trace phases. The principal weakness of this approach is that the prior distribution of constituent phases must be explicitly specified; discovery of novel, unanticipated phases is not feasible in this framework. However, the physical underpinnings of the model enable quantitative inference of phase fractions and coarse microstructural information on high throughput x-ray diffraction data. |
Proceedings Inclusion? |
Undecided |