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.
AIML1-ThM: Linking AI/ML Tools with Diagnostics and Film Growth: PLD/CVD/ALD
- Sumner B. Harris, Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, “AI-Driven Synthesis by Pulsed Laser Deposition: Autonomous Experimentation and Human-AI Collaboration”
AIML2-ThM: AI/ML Tools in Molecular Beam Epitaxy & Flash Session
- Stephanie Law, Pennsylvania State University, “AI/ML Techniques for Molecular Beam Epitaxy”
AIML1-ThA: AI/ML in Materials Characterization
Boris Slautin, The University of Tennessee, “Combining Optimization and Agentic Control in Automated Scanning Probe Microscopy”
- AIML2-ThA: AI/ML-enhanced RHEED in Thin Film Growth
- Ryan Comes, University of Delaware, “Turning RHEED into a Tool for Stoichiometry Analysis and Real-time Control of Epitaxial Film Growth Using Machine Learning”
AIML-ThP: Poster Session
AIML1-FrM: Materials Design and Autonomous Discovery
- Gregory Doerk, Brookhaven National Laboratory, “Accelerating Discovery and Design for Thin Film Polymer Self-Assembly through Machine Learning”
AIML2-FrM: AI/ML in Semiconductor Processing & Manufacturing
AIML3-FrM: Connecting Datasets, Models, and Characterization
- Linda Hung, Toyota Research Institute, “Multimodal AI Meets Materials Characterization”
AIML4-FrM: Panel: Funding Agencies