SYMPOSIUM 23 – AI/ML for Materials Discovery and Intelligent Manufacturing

The symposium focuses on AI/ML applications in autonomous experimentation and self-driving laboratories for materials discovery and manufacturing. Special attention will be given to synergistic human–AI interactions, where domain expertise and AI capabilities jointly accelerate scientific discovery; explainable and interpretable AI approaches that enhance reliability and scientific understanding; AI-assisted scientific reasoning; and agentic AI systems and digital twins.

This joint symposium, spanning multiple ACerS divisions, is designed to provide a collaborative platform for researchers to share advances in integrating AI/ML and data science methodologies across materials synthesis, device fabrication, characterization, processing, and intelligent manufacturing. By fostering interdisciplinary exchange among different ACerS divisions, this symposium aims to bridge expertise across materials science, ceramics and glass processing, manufacturing, computation, and data science . This cross-cutting forum will encourage collaborative research efforts, and inspire transformative approaches to scientific exploration, autonomous experimentation, and intelligent manufacturing.

Session Topics:

Autonomous experimentation and self-driving laboratories
Materials informatics and multimodal data fusion
AI-guided approaches for material discovery, inverse design, and optimization
Human–AI collaborative frameworks for self-driving labs
Explainable and interpretable AI for materials science and manufacturing
Open-ended and no-boundary AI-driven discovery
Agentic AI systems and digital twins
AI-assisted scientific reasoning and decision support

Symposium Organizer(s):

Yongtao Liu, Oak Ridge National Laboratory, USA
Wei Chen, Texas A&M University, USA
Fei Peng, Clemson University, USA
Aiping Chen, Los Alamos National Laboratory, USA
Bai Cui, University of Nebraska-Lincoln, USA

Point(s) of Contact:

Yongtao Liu; liuy3@ornl.gov

Division Sponsor(s):

Basic Science Division
Electronics Division
Manufacturing Division