Data Science and ML Approaches to Catalysis II: Surrogates, Bayesian Optimization, Microkinetics | AIChE

Other Sites & Tools

Technical Groups

Data Science and ML Approaches to Catalysis II: Surrogates, Bayesian Optimization, Microkinetics

Chair(s)

Zhang, R., Clariant

Co-chair(s)

Szilvasi, T., University of Alabama

Presentations

12:30 PM
(306a) Virtual Pareto-Front Mapping of Transition Metal-Catalyzed Gas-Liquid Reactions
12:48 PM
(306b) Surrogate Optimization of Liquid Organic Hydrogen Carrier Processes
1:06 PM
(306c) Leveraging Small Data for Faster and Better Catalysis
1:24 PM
(306d) Resolving Coverage Dependence By Combining Automated Quantum Chemistry Workflows with Interpretable Machine Learning
1:42 PM
(306e) Microkinetic Modeling and Design of Catalytic Materials with Fine-Tuned Machine-Learning Interatomic Potentials
2:00 PM
(306f) Machine Learning Enhancements to Transient Kinetic Models
2:18 PM
(306g) Automated Discovery of the Smallest Feasible Reaction Mechanism
2:36 PM
(306h) Discovering Equations for Acid/Base Interactions Using Symbolic Regression

Topics 

Catalysis

Checkout

Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings.

Checkout

Do you already own this?

Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

More Conference Links