ArticleACS combinatorial science2020
Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.
Article in ACS combinatorial science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters.ACS omega · 2026Article
- Guided electrocatalyst design through in-situ techniques and data mining approaches.Nano convergence · 2025Review
- Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction.Molecules (Basel, Switzerland) · 2025Review
- Transferable, Living Data Sets for Predicting Global Minimum Energy Nanocluster Geometries.Journal of chemical theory and computation · 2024Article
- Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.Nano-micro letters · 2023Review
- Computational Approaches to Alkaline Anion-Exchange Membranes for Fuel Cell Applications.Membranes · 2022Review
- Determination of PbNanomaterials (Basel, Switzerland) · 2022Article
- Predicting hydration layers on surfaces using deep learning.Nanoscale advances · 2021Article
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Authors and funding
5 authors.
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Abstract
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the
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