AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

TL;DR

AdsorbML introduces a machine learning approach that significantly accelerates and improves the accuracy of adsorption energy calculations in computational catalysis, enabling high-throughput screening with a new benchmark dataset.

Contribution

The paper presents a novel ML potential method for adsorption energy calculations, achieving high accuracy and speed, and introduces the Open Catalyst Dense dataset for benchmarking.

Findings

Achieves 87.36% success in finding lowest energy configurations

Provides up to 2000x speedup over traditional methods

Offers a spectrum of accuracy-efficiency trade-offs

Abstract

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.