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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Foundation models for multimodal data mining with applications in materials science.
Author(s) Aikaterini Vriza, Maria Chan, Henry Chan, Jie Xu
On-Site Speaker (Planned) Aikaterini Vriza
Abstract Scope In the rapidly evolving landscape of artificial intelligence (AI), Foundation Models (FMs) have emerged as transformative tools with potential applications across many scientific sectors. While there exist considerable efforts in the materials science field on using Large Language Models (LLMs) for text, research focusing on image data extraction related to spectroscopy and microscopy remains sparse. This study explored the potential of FMs for data mining multimodal datasets related to materials characterization techniques. Our methodology involves augmenting a cutting-edge data mining toolkit designed to extract image-text pairs from scientific journals, which can be foundational for creating multimodal models and advancing semantic searches. A direct comparison of FMs to human evaluators highlighted the proficiency of these models in discerning scientific research, adeptly categorizing images, and allocating relevant tags. Overall, the study suggests FMs hold promise for materials science but highlights the importance of ensuring accurate and safe applications.

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Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
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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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