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Meeting MS&T25: Materials Science & Technology
Symposium Enhancing the Accessibility of Machine Learning-Enabled Experiments
Presentation Title Accelerating Scientific Discovery with Machine Learning: Data Analysis for Computational Beamlines
Author(s) Tanny Chavez, Xiaoya Chong, Bowen Zheng, Anas Nassar, Monika Choudhary, Wiebke Koepp, Dylan McReynolds, Slavomir Nemsak, Alexander Hexemer
On-Site Speaker (Planned) Tanny Chavez
Abstract Scope As user facilities push the boundaries of experimental science, the need for intelligent, real-time computational tools becomes increasingly critical. This presentation introduces a suite of machine learning (ML) frameworks tailored to the unique challenges of data-intensive beamline science. These include automated data labeling through clustering and similarity search, interactive latent space navigation for real-time feedback during experiments, and cross-facility model adaptation to promote reusability and broader applicability. Deployed at the Advanced Light Source (ALS) and collaborating facilities, these tools support in situ analysis of complex, intricate scientific datasets, supporting researchers to make informed experimental decisions on-the-fly. We also highlight efforts to integrate physics-aware ML for the interpretation of multimodal data, such as X-ray scattering and X-ray Photoelectron Spectroscopy. Together, these solutions form the foundation of a scalable, reproducible, and facility-agnostic Machine Learning as a Service (MLaaS) ecosystem that closes the loop between data acquisition and insight.

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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