2023 AIChE Annual Meeting

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

Chair

Che, F., University of Massachusetts Lowell

Co-Chairs

Xin, H., Virginia Tech

Presentations

08:00 AM

(661a) Data Science & Machine Learning Approaches to Catalysis
Kitchin, J.

08:36 AM

(661b) Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies
Wijerathne, A., Daya, R., Sawyer, A., Paolucci, C.

08:54 AM

(661c) Developing Physically Meaningful and Accurate Machine Learning Interatomic Potentials for Catalysis
Szilvasi, T., Maxson, T., Soyemi, A.

09:12 AM

(661d) Using Neural Networks to Interpret Transient Kinetic Data
Medford, A., Gusmão, G., Nai, D.

09:48 AM

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

10:06 AM

(661f) Towards Domain-Informed Machine Learned Models from High Throughput Experimental Data
Takac, M., Rangarajan, S., Ziu, K.