2022 Annual Meeting

Session: Data Science & Machine Learning Approaches to Catalysis I: Interpretable and Theory-Guided Machine Learning For Catalysis Design and Understanding

Chair

Co-Chairs

Che, F., University of Massachusetts Lowell

Presentations

08:00 AM

(426a) Opportunities for Symbolic Machine Learning and Automated Reasoning in Chemical Engineering

08:36 AM

(426b) Enhanced Descriptor Identification and Mechanic Understanding for Catalytic Activity with Data-Driven Framework: Revealing the Interactions between Elementary Steps

08:54 AM

(426c) Latent Variable Machine Learning Framework for Catalysis

09:12 AM

(426d) Discovery of Pt Trimetallic Electrocatalysts for Ammonia Oxidation with Interpretable Deep Learning

09:30 AM

(426e) Dimensionality Reduction of Chemical Kinetics Based on Extent-of-Reaction in a Physics-Inspired Machine Learning Framework

09:48 AM

(426f) An Electronic Descriptor Based Machine Learned Model for Adsorption Energies on Oxide Materials

10:06 AM

(426g) Maximum Entropy Inference in Chemical Reaction Networks with Unknown Kinetic Parameters