AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
arXiv:2211.16486 · doi:10.1038/s41524-023-01121-5
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.
26 pages, 7 figures. Submitted to npj Computational Materials
References in corpus (8)
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
- The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
- Deep Potentials for Materials Science
- GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
- Rotation Invariant Graph Neural Networks using Spin Convolutions
- Spherical Channels for Modeling Atomic Interactions
- Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
- Learned Force Fields Are Ready For Ground State Catalyst Discovery