Abstract
AbstractComputational 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 ~2000× speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1000 diverse surfaces and ~100,000 unique configurations.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
Reference53 articles.
1. Nørskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Fundamental Concepts in Heterogeneous Catalysis (John Wiley & Sons, 2014).
2. Chanussot, L. et al. Open catalyst 2020 (oc20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021).
3. Dumesic, J. A., Huber, G. W. & Boudart, M. Principles of Heterogeneous Catalysis (Wiley Online Library, 2008).
4. Zitnick, C. L. et al. An introduction to electrocatalyst design using machine learning for renewable energy storage. Preprint at https://arxiv.org/abs/2010.09435 (2020).
5. Choudhary, K. et al. Recent advances and applications of deep learning methods in materials science. NPJ Comput. Mater. 8, 59 (2022).
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