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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Autonomous approaches for determining structure-processing-property relationships in materials
Author(s) Rama Krishnan Vasudevan, Sumner Harris, Yongtao Liu, Arpan Biswas
On-Site Speaker (Planned) Rama Krishnan Vasudevan
Abstract Scope The use of traditional statistical and machine learning approaches have shown to be highly promising for understanding and predicting structure-processing-property relationships in materials, with methods including matrix factorization, random forests, and deep neural networks showing strong effectiveness at a range of tasks. However, the ability to rapidly learn these properties and change the way experiments are performed on the basis of these correlations is less explored. Here, we will review our autonomous efforts in both scanning probe microscopy (SPM) as well as pulsed laser deposition (PLD), with Bayesian methodologies used to quickly optimize targets whilst simultaneously providing a quantifiable prediction of the structure-property relationships. More flexible kernel methods and their extensions to both SPM and PLD will be discussed, which can be used to reduce the number of overall experiments compared to more inflexible kernels. Extensions to incorporate robustness of the solution into the Bayesian framework will be discussed.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
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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