2023 AIChE Annual Meeting

Session: Data Science and Machine Learning Approaches to Catalysis II: Catalytic Materials Design

Chair

Co-Chairs

Brandon Bukowski, Purdue University
Renqin Zhang, Clariant

Presentations

12:30 PM

(662a) Machine Learning for Homogeneous Open-Shell Transition Metal Catalyst Discovery
Heather Kulik

01:06 PM

(662b) Intermetallic Catalyst Discovery for Selective Hydrogenation Reactions
Jin LI, Angela Nguyen, Unnatti Sharma, Zachary Ulissi, Michael Janik, Zi-kui Liu, Rushi Gong, Griffin A. Canning, Robert Rioux

01:24 PM

(662c) Integrating Experimental and Theoretical Data for High Quality Predictions of Material Performance Towards Electrochemical Reactions
Shyam Deo, Kirsten Winther, Johannes Voss, Dr. Michaela Burke Stevens, Gaurav A. Kamat, Dr. Melissa E. Kreider

01:42 PM

(662d) Machine Learning Electron Density for Chemical Property Predictions in Catalysis
Ethan Sunshine, Muhammed Shuaibi, Zachary Ulissi, John Kitchin

02:00 PM

(662e) Predicting the Adsorption Energies of Cyclic Hydrocarbons Adsorbed on Bimetallic Nanoclusters Using Gaussian Process Regression
Chuhong Lin, Uzma Anjum, Chak Sing Bryan Lee, Asmee Prabhu, Tej Choksi

02:18 PM

(662f) Improving the Accuracy of ML-Models for Catalysis through Bulk Electronic Structure Descriptors
Kirsten Winther

02:36 PM

(662g) Clarifying Trust of Materials Property Predictions Using Neural Networks with Distribution-Specific Uncertainty Quantification
Varun Madhavan, Cameron Gruich, Bryan Goldsmith, Yixin Wang