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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Creating Benchmark Datasets for Electron-Microscopy Based Machine Learning Models

Author(s)

Katelyn Jones, Brian DeCost, June Lau, Francesca Tavazza

On-Site Speaker (Planned)

Katelyn Jones

Abstract Scope

Machine learning (ML) has been widely adopted in materials science and engineering, despite the lack of benchmark datasets to measure model performance. NIST's NexusLIMS, a laboratory information management system, has automatically collected data and metadata from 13 electron microscopes, resulting in a large dataset consisting of various materials and imaging modality types with labels. This project focuses on using the data from NexusLIMS to create benchmark datasets and guidelines for building future MSE datasets for convolutional neural networks (CNNs) and image-focused ML models using the NexusLIMS data. This study will focus on the combination of a variety of material and electron-based microscopy types to determine the best combination for future models in tasks such as classification and regression. By leveraging the NexusLIMS dataset, this project aims to advance the development of domain-specific pre-trained ML models and improve the application of ML in MSE.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, ICME, Machine Learning

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