2022 Annual Meeting

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

Chair

Zachary Ulissi, Carnegie Mellon University

Co-Chair

Florian Goeltl, The University of Arizona

Presentations

12:30 PM

(477a) AI-Accelerated Materials Modeling for Catalyst Discovery
Nongnuch Artrith

01:06 PM

(477b) Finding Needles in a Haystack: Sifting through 16M Catalysts for Optimal Methane-to-Methanol Catalyst Design Under Weak Thermodynamic Scaling
Aditya Nandy, Heather Kulik

01:24 PM

(477c) Finetuna: Fine-Tuning Accelerated Molecular Simulations
Joseph Musielewicz, Zachary Ulissi, Tian Tian, Xiaoxiao Wang

01:42 PM

(477d) Investigations of Electric Field Effects on Catalysis: A Combination of Deep Learning Models and Multi-Scale Simulations
Fanglin Che, Mingyu Wan, Hongfu Liu, Han Yue

02:00 PM

(477e) Machine Learning Predictions of Novel Ammonia Synthesis Catalysts Using Experimental and Literature Data
Withana A R Jayarathna, Jochen Lauterbach

02:18 PM

(477f) Integrating Experimental and Theoretical Data for High Quality Predictions of Electrocatalytic Performance
Shyam Deo, Melissa Kreider, Michaela Burke Stevens, Kirsten Winther, Johannes Voss, Frank Abild-Pedersen, Thomas Jaramillo

02:36 PM

(477g) First-Principles Analysis of the Ammonia Decomposition Reaction on High Entropy Alloycatalysts
Zuhal Cakir, Liangbing Hu, Chao Wang, Jeffrey Greeley