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Abstract
This paper introduces AdsorbML, a machine learning algorithm that significantly accelerates the computation of adsorption energies for adsorbate-catalyst surface interactions. The algorithm offers a range of accuracy-efficiency trade-offs, with one balanced option achieving an 87.36% success rate while being ~2000 times faster than traditional methods. To facilitate benchmarking, the authors introduce the Open Catalyst Dense dataset, containing nearly 1000 diverse surfaces and ~100,000 unique configurations.
Publisher
npj Computational Materials
Published On
Sep 22, 2023
Authors
Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi
Tags
AdsorbML
machine learning
adsorption energies
catalyst
Open Catalyst Dense dataset
efficiency
accuracy

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