Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models

Nitish Govindarajan, Daniel Schwalbe-Koda, Joel B Varley

Keywords: neural networks, catalysis, monte-carlo sampling, MACE

TL;DR: Our findings demonstrate that simplified data generation routines and evaluating generalization of neural networks can be deployed at scale to understand lateral interactions in heterogeneous catalysis

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1039/D4DD00328D

Submission Number: 28