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

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

Chair(s)

Co-chair(s)

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

Presentations

12:30 PM
(662a) Machine Learning for Homogeneous Open-Shell Transition Metal Catalyst Discovery
1:06 PM
(662b) Intermetallic Catalyst Discovery for Selective Hydrogenation Reactions
1:24 PM
(662c) Integrating Experimental and Theoretical Data for High Quality Predictions of Material Performance Towards Electrochemical Reactions
1:42 PM
(662d) Machine Learning Electron Density for Chemical Property Predictions in Catalysis
2:00 PM
(662e) Predicting the Adsorption Energies of Cyclic Hydrocarbons Adsorbed on Bimetallic Nanoclusters Using Gaussian Process Regression
2:18 PM
(662f) Improving the Accuracy of ML-Models for Catalysis through Bulk Electronic Structure Descriptors
2:36 PM
(662g) Clarifying Trust of Materials Property Predictions Using Neural Networks with Distribution-Specific Uncertainty Quantification

Topics 

Catalysis

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Non-Members $225.00

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