This symposium is designed to offer a platform for researchers to share insights into the integration of artificial intelligence (AI), machine learning (ML), and data science methodologies across various aspects of scientific research, including materials synthesis, device fabrication, characterization, advanced manufacturing, and stability and lifetime assessment. The symposium will explore a diverse range of AI/ML applications, including predictive modeling, classification, autonomous experimentation, AI-guided online monitoring and quality control, and human-AI collaboration. The symposium will foster an interdisciplinary exchange of ideas related to AI/ML among different ACerS divisions and by bridging expertise across disciplines, this symposium will drive forward the development of AI/ML-based frameworks for ceramics and glasses, fostering collaboration and accelerating innovation. This symposium is a collaborative effort spanning the Basic Science, Electronics, and Manufacturing Divisions.
Proposed Sessions/Topics
- Predictive modeling of material properties, device performance, and product lifetime
- Materials informatics and high-throughput experimentation
- AI-guided approaches for material discovery and design
- AI-guided defect detection, online monitoring, and quality control for advanced manufacturing
- AI-driven process optimization
- AI assisted process monitoring and quality assurance
- Defect analysis, degradation modeling, and failure prediction using AI
- Autonomous experimentation
- AI-enhanced spectroscopic and microscopic techniques
Symposium Organizer(s)
- Yongtao Liu Oak Ridge National Laboratory, USA
- Fei Peng, Clemson University, USA
- Bai Cui, University of Nebraska-Lincoln, USA
- Aiping Chen, Los Alamos National Laboratory, USA
- Davi Febba, National Renewable Energy Laboratory, USA
Point(s) of Contact
- Yongtao Liu; LIUY3@ORNL.GOV
Symposium Sponsor(s)
- Basic Science Division
- Electronics Division
- Manufacturing Division
ACerS Spring Meeting
April 12 • 16, 2026