Data Science & Machine Learning Approaches to Catalysis III: Applications of Machine Learning to Heterogeneous Catalysis: From Porous Materials to Cluster Catalysis | AIChE

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Data Science & Machine Learning Approaches to Catalysis III: Applications of Machine Learning to Heterogeneous Catalysis: From Porous Materials to Cluster Catalysis

Chair(s)

Xin, H., Virginia Tech

Co-chair(s)

Alexopoulos, K., University of Delaware

Presentations

8:00 AM
(582a) Crystal Engineering of a Zeolite Using Machine Learning
8:18 AM
Break
8:36 AM
(582c) Electronic Environment-Based Correction Scheme Using DFT and Wft-Level Energies to Accurately Predict Energetics of Catalytic Interfaces
8:54 AM
(582d) Computational Design of Metal Oxide Nanoclusters for Selective Partial Oxidation of Hydrocarbons
9:12 AM
(582e) Predicting Thermochemical Properties of Hydrocarbon Adsorbates on Metal Surfaces Using Machine Learning
9:30 AM
(582f) A Data-Driven Approach Towards Assessing the Stability and Activity of Nanoparticles Containing on Order of Thousand Atoms: A Case Study Using the Oxygen Reduction Reaction
9:48 AM
(582g) Modeling the Distribution of Supported Sub-Nanometer Cluster Catalysts
10:06 AM
(582h) Site Geometry Driven Selective Hydrogenation on Intermetallic Catalysts

Topics 

Catalysis

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