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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Presentation Title Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
Author(s) Brian DeCost, Howie Joress, Bruce Ravel, Mitra Taheri
On-Site Speaker (Planned) Brian DeCost
Abstract Scope Machine learning systems are being widely deployed to accelerate measurements of the structure and performance of materials. The black-box nature of the models used by these systems can limit the ability to decouple the effects of competing physical phenomena, particularly in the few-sample setting. Our approach blends Bayesian physical modeling and non-parametric machine learning models. Two challenging structural materials characterization tasks highlight this approach: quantitative analysis of multiphase x-ray diffraction (XRD) data and quantification of chemical short range order in multicomponent alloys via EXAFS. We show how to incorporate physical intuition into hierarchical priors, and how to incorporate flexible Gaussian Process modeling components for features without concrete physical models. Our long term goal is automated online analysis that can drive adaptive measurement selection with the aim of enabling comprehensive understanding of the relationship between structure and properties.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Advanced Mechanical Properties Prediction of Functionally Graded Materials through High-Throughput Characterization.
Advances in Atom Probe Crystallographic Analysis
Connectivity of Experimental Equipment and Interoperability of Experimental Data: Challenges and Opportunities
Data-driven Discovery of Dynamics from Time-resolved Coherent Scattering
Data Management in Additive Manufacturing – Lessons Learned and Opportunities
Data Management, Data Sharing and the Future of Federal Research Funding
Deep Learning-Driven Semantic Segmentation of large 4D Lab-Scale X-ray Tomography Data for Quantification of Microstructural Features
Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Enabling Uninterrupted In-situ X-ray Experiments through Rapid Data Feedback and On-the-fly Experiment Optimization
G-19: Accessing the Microstructure State Space
G-20: TESCAN TENSOR a 4D-STEM for Multimodal Characterization of Challenging and Interesting Specimens
Galaxy: A Critical Framework for Large Data Volumes and Data-intensive Processing in the Synchrotron World
Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
HPC+AI@Edge Enabled Real-Time Materials Characterization
Melt Pool Quantification from In Situ Radiography of Directed Energy Deposition of Nickel Superalloys
New Strong and Ductile Titanium-oxygen-iron Alloys Enabled by AM and Insights from Multiscale Microscopy
Probabilistic Orientation Analysis via Direct ODF Calculation from Far Field HEDM
Quantitative 2D and 3D Characterization of Precipitates Microstructure in the Additively Manufactured Titanium Alloy
Real-Time In-Situ Characterization with Web Technologies at Any Scale
Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Understanding Relaxation Dynamics Beyond Equilibrium Using AI-Informed X-ray Photon Correlation Spectroscopy
Using Video Games for Training Data on Microstructural Design
Utilizing Advanced Computer Vision Techniques Based on Machine Learning and Artificial Neural Networks to Process Micrographs of Ni-base Superalloys
Utilizing Deep Learning Techniques to Accelerate X-ray Absorption and Diffraction Contrast Imaging

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