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Meeting MS&T25: Materials Science & Technology
Symposium Enhancing the Accessibility of Machine Learning-Enabled Experiments
Presentation Title From automated to autonomous – creating a general active learning service for self-driving laboratories
Author(s) Stephen J. DeWitt, Ankit Shrivastava, Marshall McDonnell, Singanallur Venkatakrishnan, Chris Fancher, Jorge Ramirez Osorio, Paul Laiu, Lance Drane, Ayana Ghosh, David Joy
On-Site Speaker (Planned) Stephen J. DeWitt
Abstract Scope A key step in most scientific workflows is answering the question “Given the data I already have, what should I measure next?”. The field of sequential experimental design is targeted toward answering this question and is critical to convert automated experiments into autonomous (self-driving) experiments. In this presentation, we discuss the creation of Dial, a new open-source tool that applies active learning methods for sequential experimental design. Dial operates as a service in the broader INTERSECT ecosystem for autonomous experiments. After presenting the design and structure of Dial, we show examples where Dial was used to drive experiments, including neutron diffraction experiments to map residual strain in 3D-printed metal parts and to identify the temperature of the “spin flop” transition in hematite nanoparticles. This abstract has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.

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

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