Data Science & Machine Learning Approaches to Catalysis II: AI-Accelerated Modeling of Catalysts and Materials | AIChE

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Data Science & Machine Learning Approaches to Catalysis II: AI-Accelerated Modeling of Catalysts and Materials

Chair(s)

Ulissi, Z., Carnegie Mellon University

Co-chair(s)

Goeltl, F., The University of Arizona

Presentations

12:30 PM
(477a) AI-Accelerated Materials Modeling for Catalyst Discovery
1:06 PM
(477b) Finding Needles in a Haystack: Sifting through 16M Catalysts for Optimal Methane-to-Methanol Catalyst Design Under Weak Thermodynamic Scaling
1:24 PM
(477c) Finetuna: Fine-Tuning Accelerated Molecular Simulations
1:42 PM
(477d) Investigations of Electric Field Effects on Catalysis: A Combination of Deep Learning Models and Multi-Scale Simulations
2:00 PM
(477e) Machine Learning Predictions of Novel Ammonia Synthesis Catalysts Using Experimental and Literature Data
2:18 PM
(477f) Integrating Experimental and Theoretical Data for High Quality Predictions of Electrocatalytic Performance
2:36 PM
(477g) First-Principles Analysis of the Ammonia Decomposition Reaction on High Entropy Alloycatalysts

Topics 

Catalysis

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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

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