2023 AIChE Annual Meeting

Session: Data Science and Machine Learning Approaches to Catalysis I: Data-enhanced Multiscale Simulations

Chair

Fanglin Che, University of Massachusetts Lowell

Co-Chairs

Hongliang Xin, Virginia Tech

Presentations

08:00 AM

(661a) Data Science & Machine Learning Approaches to Catalysis
John Kitchin

08:36 AM

(661b) Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies
Asanka Wijerathne, Rohil Daya, Allison Sawyer, Christopher Paolucci

08:54 AM

(661c) Developing Physically Meaningful and Accurate Machine Learning Interatomic Potentials for Catalysis
Tibor Szilvasi, Tristan Maxson, Ademola Soyemi

09:12 AM

(661d) Using Neural Networks to Interpret Transient Kinetic Data
Andrew Medford, Gabriel Gusmão, Dingqi Nai

09:48 AM

(661e) Modeling Supported Sub-Nanometer Cluster Catalysts Via Multiscale Computations and Machine Learning
Salman A. Khan, Stavros Caratzoulas, Dionisios Vlachos

10:06 AM

(661f) Towards Domain-Informed Machine Learned Models from High Throughput Experimental Data
Martin Takac, Srinivas Rangarajan, Klea Ziu