Topic: Open Catalyst: Advancements in AI Accelerated Catalyst Discovery
The field of computational chemistry has seen a surge of new developments over the last few years. In this talk I will overview the Open Catalyst Project’s recent efforts on accelerating catalyst discovery with AI. Following an overview of current modeling and dataset progress, I will discuss some of our more recent work on finding more favorable low energy adsorbate+catalyst configurations using state-of-the-art models - AdsorbML. In this work we expand the existing challenges presented by the Open Catalyst 2020 Dataset to bring us closer to more practical downstream applications. I will conclude with a discussion on some of the ongoing and future challenges that still remain.
|Topic||Open Catalyst: Advancements in AI Accelerated Catalyst Discovery|
|When||22.02.2023, 16:00 - 17:30 (Central European Time) / 07:00 (PST)|
Dr. Muhammed Shuaibi is a Research Engineer at FAIR, Meta AI where he works on AI applications for Chemistry, namely the Open Catalyst Project. He completed his Ph.D. in Chemical Engineering at Carnegie Mellon University under the supervision of Dr. Zachary Ulissi, where he worked on machine learning applications to catalysis, including active learning, graph neural networks, and large-scale datasets. Prior to his Ph.D., he worked for the U.S. Environmental Protection Agency (EPA) where he helped mitigate air and radiation pollution. His current focus lies at the intersection of bridging the gap between computational chemistry, ML/AI, and experimental results.