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Condensed Matter > Materials Science
Title: AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
(Submitted on 29 Nov 2022 (v1), last revised 15 Sep 2023 (this version, v3))
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.
Submission history
From: Muhammed Shuaibi [view email][v1] Tue, 29 Nov 2022 18:54:55 GMT (6408kb,D)
[v2] Wed, 4 Jan 2023 20:35:22 GMT (37249kb,D)
[v3] Fri, 15 Sep 2023 19:56:43 GMT (6512kb,D)
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