Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a 2000x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 100,000 unique configurations.
@article{arxiv.2211.16486,
title = {AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials},
author = {Janice Lan and Aini Palizhati and Muhammed Shuaibi and Brandon M. Wood and Brook Wander and Abhishek Das and Matt Uyttendaele and C. Lawrence Zitnick and Zachary W. Ulissi},
journal= {arXiv preprint arXiv:2211.16486},
year = {2023}
}
Comments
26 pages, 7 figures. Submitted to npj Computational Materials