ProgramMaster Logo
Conference Tools for MS&T24: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Author(s) Kai He
On-Site Speaker (Planned) Kai He
Abstract Scope Transmission electron microscopy (TEM) is an indispensable methodology to characterize materials structures and compositions at the atomic scale. Recent advancement of in-situ TEM techniques enables unprecedented capabilities for spatiotemporal and multi-modal characterizations of dynamic material transformations during physical and chemical processes, thereby generating massive high-dimensional datasets. It is highly desired that such large volumes of data can be efficiently and effectively analyzed to accelerate scientific discovery. With rapid growth of artificial intelligence (AI), machine learning (ML) approaches have demonstrated the unique power in analyzing and processing microscopy-related datasets, such as time-sequential and hyperspectral images. Herein, we present our recent efforts in ML-enhanced data analytics to enable high-throughput image processing with less human perception. These methods, demonstrated in atomic-resolution crystal lattices and nanoscale particle systems, allow us to perform reliable and reproducible analysis of TEM-based datasets in multi-modalities and multi-dimensionalities.

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

Questions about ProgramMaster? Contact programming@programmaster.org