This focus topic 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 focus topic will bring together the community to disseminate the latest advances in the field, discuss challenges, and share future directions for AI & ML.
AIML-WeM: AI/ML for Scientific Discovery
- Brad Boyce, Sandia National Laboratories, USA,”Beyond Fingerprinting”: Rapid Process Exploration and Optimization via High-Throughput and Machine Learning”
- Noa Marom, Carnegie Mellon University, “Simulations of Epitaxial Inorganic Interfaces Using DFT with Machine-Learned Hubbard U Corrections”
AIML-ThP: AI/ML for Scientific Discovery Poster Session