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

Fanglin Che, University of Massachusetts Lowell

Presentations

08:00 AM

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

08:36 AM

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

08:54 AM

(426c) Latent Variable Machine Learning Framework for Catalysis
Chukwudi Nwaokorie, Gbolade Kayode, Matthew Montemore

09:12 AM

(426d) Discovery of Pt Trimetallic Electrocatalysts for Ammonia Oxidation with Interpretable Deep Learning
Hemanth Pillai, Yi Li, Shih-Han Wang, Qingmin Mu, Claire Pokrywka, Luke E. K. Achenie, Frank Abild-Pedersen, Gang Wu, Hongliang Xin

09:30 AM

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

09:48 AM

(426f) An Electronic Descriptor Based Machine Learned Model for Adsorption Energies on Oxide Materials
Benjamin Comer, Michal Bajdich, Kirsten Winther, Frank Abild-Pedersen, Jiang Li

10:06 AM

(426g) Maximum Entropy Inference in Chemical Reaction Networks with Unknown Kinetic Parameters
Mehrad Ansari, Andrew White