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240206s2024 xx |||||o 00| ||eng c |
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|a 10.1021/acs.langmuir.3c03401
|2 doi
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|a eng
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|a Sun, Chenghan
|e verfasserin
|4 aut
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| 245 |
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|a Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 29.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community
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|a Journal Article
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|a Goel, Rajat
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|a Kulkarni, Ambarish R
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|i Enthalten in
|t Langmuir : the ACS journal of surfaces and colloids
|d 1992
|g 40(2024), 7 vom: 20. Feb., Seite 3691-3701
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|g volume:40
|g year:2024
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|g pages:3691-3701
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