Conference Logo ProgramMaster Logo
Conference Tools for MS&T25: 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&T25: Materials Science & Technology
Symposium Enhancing the Accessibility of Machine Learning-Enabled Experiments
Presentation Title Foundational Workflows for Processing Legacy Data and Realizing Domain-Specific Multi-Modal AI Models
Author(s) Kevin Le, Andrew Walter Richards, Robert Errol Hackenberg
On-Site Speaker (Planned) Kevin Le
Abstract Scope Efforts to transfer expert knowledge to the next generation is fraught with inefficiency due to several factors including: inability to capture all facets of knowledge, limited access to predecessor's time, and hindrances posed by their time and ability to parse and understand large amounts of unorganized knowledge contained in heritage files. Although legacy reports are the most promising with a view toward knowledge capture, they nonetheless suffer from limitations such as being born analog, lacking keywords, and having limited metadata context. There is a need to extract knowledge efficiently from these reports to aid human learning as well as to feed emerging AI tools. Therefore, researchers aim to develop tools that make legacy data useful for current needs and to prepare it for incorporation into domain-specific AI models. This talk discusses workflows for automated meta data generation to improve accessibility, organization, and understanding.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Scientific Discovery with Machine Learning: Data Analysis for Computational Beamlines
Adaptive Workflows for Lab of the Future
ATOMIC: Autonomous Characterization of 2D Materials Through Foundation Models
Autonomous Atomic Force Microscopy using Large Language Model Agents
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer
Foundational Workflows for Processing Legacy Data and Realizing Domain-Specific Multi-Modal AI Models
From automated to autonomous – creating a general active learning service for self-driving laboratories
High-throughput, Ultra-fast Laser Sintering of Ceramics and Machine-learning Based Prediction on Processing-Microstructure-Property Relationships
Hypothesis Formation and Predictive Modeling of 2D Perovskite Spacer Cations Using Retrieval Augmented LLMs and Deep Kernel Learning
Ptychography Data Pipelines at the Advanced Photon Source
Pycroscopy, AEcroscopy, and data workflows: integrating customized control, data analysis and workflows in an autonomous microscopy facility

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement