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—highlights 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 understand materials at the atomic scale, discover new scientific laws, 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: AIML is seeking abstracts in areas including, but not limited to:
Driving Scientific Discovery through AI/ML: Utilizing AI/ML to develop and evaluate new materials, processes, and devices, thereby reducing the need for extensive experimental design and costly modeling, while predicting performance outcomes
AI/ML for Big Data: Designing experiments and data collection methods to enhance data generation and throughput, developing datasets and tools for model training, and ensuring model quality, uncertainty quantification, and trust in AI models
AI/ML for Modeling and Characterization: Applying AI/ML techniques for the modeling and characterization of materials and systems, including synthetic data generation
Compute & Memory for AI: Development of new materials to address challenges in memory bandwidth, storage, and energy consumption -e.g., neuromorphic, non-von Neumann
AI vs. Physical Principles: Exploring fundamental approaches to predicting material properties in contrast to AI/ML methodologies
AIML1: AI/ML for Scientific Discovery Oral Session
AIML2: AI/ML for Scientific Discovery Poster Session