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About this Abstract
Meeting MS&T25: Materials Science & Technology
Symposium Autonomous Platforms for Designing and Understanding Materials
Presentation Title Self Driving Labs and and Digital Twins
Author(s) James A. Warren
On-Site Speaker (Planned) James A. Warren
Abstract Scope Self-driving laboratories are poised to remake the research landscape. Obviously, the ability to rapidly create/design fit-for-purpose advanced materials is a long-held dream of the materials R&D, but SDLs offer much more. Specifically, SDLs have the potential to provide the critical data needed to create materials digital twins, and associated AI-powered surrogate models that will revolutionize manufacturing.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Digital Laboratory with Modular Measurement System and Standardized Data Format
Ferroics Reimagined with Causal Machine Learning
From Deposition to Degradation of Thin Films and Devices Through Autonomous Experimentation
Knowledge Graphs for Chemical Synthesis: Using Historical Data for Querying and Semantic Reasoning
Materials Discovery Using Deep Microscopic Optics
Self Driving Labs and and Digital Twins
Sparse Sampling and Inpainting for High-Throughput Scanning Transmission Electron Microscopy
Towards Autonomous Imaging and Analysis of Magnetic Domains
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