About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Using Machine Learning Methods to Decode VOx Diffractograms |
Author(s) |
Saaketh Desai, Suvo Banik, Haidan Wen, Subramanian Sankaranarayanan, Remi Dingreville |
On-Site Speaker (Planned) |
Saaketh Desai |
Abstract Scope |
Understanding the effect of structural defects on the metal-to-insulator transformation in vanadium oxide is critical to tuning the transformation for various applications. Diffractograms offer a non-intrusive way of characterizing defects, but can be challenging to interpret comprehensively, limiting our understanding of structure-property relationships. In this talk, we discuss how to employ machine learning methods to identify critical features of diffractograms that correlate with defect statistics and structure phases in VOx structures. We compute X-ray and electron diffraction patterns for stable and metastable VOx structures, whose defects are captured via one-point and two-point statistics such as defect density and defect pair correlation function. Key features of diffractograms are obtained via non-negative matrix factorization, and a Gaussian process model predicts one-point and two-point statistics using these features. We discuss how our automated workflow comprehensively analyzes diffractograms, accurately capturing defect statistics for simulated and experimental diffraction patterns, reducing the need for additional characterization. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, Computational Materials Science & Engineering |