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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Author(s) Finley Holt, Redad Mehdi, Weiqi Yue, PawanT Tripathi, Daniel Savage, Matthew Willard, Frank Ernst, Roger French
On-Site Speaker (Planned) Finley Holt
Abstract Scope Neutron and X-ray powder diffraction techniques generate patterns characterized by reflections in accordance with Bragg’s Law. The Rietveld refinement method is instrumental in analyzing crystalline materials. Currently, Rietveld refinement software necessitates substantial expertise and multiple analysis iterations to achieve convergence. However, by leveraging adaptive neural networks specifically tailored to the experimental setup and materials, the number of iterations required for satisfactory convergence is significantly reduced. This advancement enhances the usability and efficiency of Rietveld refinement software. The specialized neural networks are trained using simulated diffractograms generated by the diffraction simulation capabilities of GSASII. These neural networks are designed to accurately predict phase fractions, lattice parameters, and the phases present in the sample. Our approach has demonstrated an average deviation of 0.39% from the true phase fractions, with a maximum deviation of 4.45%. Similarly, lattice parameters have been predicted with an average error of 0.46% and a maximum error of 4.06%.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

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