Step Changes in Open Datasets and AI/ML Models with Relevance to Semiconductor Materials and Manufacturing
Materials simulation has long been critical to discovering new semiconductor materials and designing surface process and etching processes. Pre-trained AI/ML models are on the verge of revolutionizing this field as generalizable replacements for simulation methods like DFT. I will discuss some of the large open science datasets and models released by the FAIR Chemistry team for inorganic materials science and surface chemistry relevant to semiconductor manufacturing, including the recently release OMat24 and OC20/OC22 datasets. These datasets comprise hundreds of millions of structures and are being used by us and others in the community build increasingly accurate AI/ML models. This talk will also highlight the importance of community and open-science efforts to addressing multi-disciplinary challenges.
BIOGRAPHY
Zack Ulissi is a senior research manager on the FAIR Chemistry team in Meta’s Fundamental AI Research lab and an Adjunct Professor of Chemical Engineering at Carnegie Mellon University. He has led several open science projects and community efforts. Prior to Meta, he was an Associate Professor of Chemical Engineering at CMU. He completed his undergraduate work at the University of Delaware, M.A.St. at Cambridge, PhD at MIT on carbon nanotube devices, and post-doc in catalysis at Stanford/SLAC.