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 Adaptive Workflows for Lab of the Future
Author(s) Olga Ovchinnikova
On-Site Speaker (Planned) Olga Ovchinnikova
Abstract Scope The growing demand for high-throughput, reproducible, and scalable analyses is driving a paradigm shift toward digitally enabled, autonomous laboratories. Traditional workflows are often constrained by manual interventions, rigid experimental pipelines, and disjointed data systems that hinder real-time responsiveness. As advanced characterization tools evolve in their ability to generate rich, high-dimensional datasets, they expose a new bottleneck: the lack of integrated data flow architectures capable of dynamically informing experimental decisions. The Lab-in-the-Loop concept is built on a linking instrument control, sample tracking, data processing, and machine learning-driven decision logic into a seamless operational loop. I discuss the required architecture consisting of (1) Data Acquisition, interfacing with scientific instrumentation for high-resolution readouts; (2) Data Integration and Orchestration that harmonizes diverse data modalities and metadata streams using structured ontologies and APIs; (3) Analytics and Inference and (4) Execution Layer, where robotic or scripted agents adjust protocols in real time based on feedback.

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