2023 AIChE Annual Meeting

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

Chair

Co-Chairs

Bukowski, B., Purdue University
Zhang, R., Clariant

Presentations

12:30 PM

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

01:06 PM

(662b) Intermetallic Catalyst Discovery for Selective Hydrogenation Reactions

01:24 PM

(662c) Integrating Experimental and Theoretical Data for High Quality Predictions of Material Performance Towards Electrochemical Reactions

01:42 PM

(662d) Machine Learning Electron Density for Chemical Property Predictions in Catalysis

02:00 PM

(662e) Predicting the Adsorption Energies of Cyclic Hydrocarbons Adsorbed on Bimetallic Nanoclusters Using Gaussian Process Regression

02:18 PM

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

02:36 PM

(662g) Clarifying Trust of Materials Property Predictions Using Neural Networks with Distribution-Specific Uncertainty Quantification