The integration of AI and machine learning across various domains—from accelerating scientific discovery and optimizing big data experiments to enhancing characterization techniques and advancing semiconductor technologies—is delivering a transformative approach that not only leverages computational power for innovative materials and devices but also seeks to reconcile AI methodologies with fundamental physical principles to deepen our understanding of material properties. This mini-symposium will bring together leaders in the rapidly growing field of data science, artificial intelligence, and machine learning (AI/ML) for materials, processes, and interfaces to drive scientific discovery. AI, ML, and deep learning (DL) are being utilized to learn empirical representations of complex processes, understand materials at the atomic scale, and even design the next generation of advanced microelectronics for AI/ML. As researchers from academia to industry search for more effective means of advancing technology, AI/ML is being utilized as a means to reduce the burden on resources that have long relied on traditional experiments and computationally heavy modeling and simulation. This mini-symposium will bring together the community to disseminate the latest advances in the field, discuss challenges, and share future directions for AI & ML.
Areas of Interest: Abstracts are sought on topics including (but not limited to):
- ML-driven/autonomous thin-film growth
- Physics-inspired ML models in growth and processing
- AI/ML techniques in materials synthesis and automated deposition tools and processes
- AI-driven automated synthesis with looped characterization
- AI for integrating experimental and computational discovery
- Uncertainty quantification
Other Highlights and Invited Topics:
- AI in MBE and RHEED-guided MBE growth of 2D and thin film materials
- ML, simulation, and high-throughput experimental dataset integration to accelerate thin film and process discovery
- ML in atomic-scale deposition
- AI/big data across semiconductor process development and foundry manufacturing
- AI for synthesis‐property relationship prediction in thin films
- Self-driving labs for thin film optimization
- ML in ALD optimization and precursor identification
Other Planned Activities:
- Student Awards
- Panel discussion on AI/ML directions in thin films
AIML1: AI/ML/Autonomous Experimentation for Thin Films Processing Oral Session
Invited Speakers:
- Ryan Comes, University of Delaware
- Sumner Harris, Oak Ridge National Laboratory, “Approaches for AI-Driven Pulsed Laser Deposition Using in Situ and Real-Time Diagnostics”
- Linda Hung, Toyota Research Institute
- Stephanie Law, Pennsylvania State University, “Understanding and Optimizing Synthesis of 2D Materials by Molecular Beam Epitaxy Using Machine Learning Techniques”
- Boris Slautin, University of Tennessee, Knoxville
AIML2: AI/ML Autonomous Experimentation for Thin Films Processing Poster Session