A high-throughput framework for determining adsorption energies on solid surfaces
Surface chemistry: an automatic sense of attraction An automated procedure for determining the energy required for a molecule to adhere to a surface is developed by researchers in the United States. Joseph Montoya from the Lawrence Berkeley National Laboratory and Kristin Persson from the University...
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Nature Portfolio
2017
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oai:doaj.org-article:6d7647d87d2a454fb1658f93bad3f11d2021-12-02T11:50:54ZA high-throughput framework for determining adsorption energies on solid surfaces10.1038/s41524-017-0017-z2057-3960https://doaj.org/article/6d7647d87d2a454fb1658f93bad3f11d2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41524-017-0017-zhttps://doaj.org/toc/2057-3960Surface chemistry: an automatic sense of attraction An automated procedure for determining the energy required for a molecule to adhere to a surface is developed by researchers in the United States. Joseph Montoya from the Lawrence Berkeley National Laboratory and Kristin Persson from the University of California, Berkeley, introduce an algorithm for finding the adsorption sites on an arbitrary surface. Knowing the amount of energy required for molecular adsorption is crucial for identifying the best materials for use in electronics and catalysis. Density functional theory can predict adsorption energies but usually requires human intuition to tune the calculations. With so many combinations of surface and adsorbate, an automated method is required. Montoya and Persson use open-source computational tools from the Materials Project to present a workflow for performing high-throughput density functional theory calculations for arbitrary slabs and adsorbed species.Joseph H. MontoyaKristin A. PerssonNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 3, Iss 1, Pp 1-4 (2017) |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Joseph H. Montoya Kristin A. Persson A high-throughput framework for determining adsorption energies on solid surfaces |
description |
Surface chemistry: an automatic sense of attraction An automated procedure for determining the energy required for a molecule to adhere to a surface is developed by researchers in the United States. Joseph Montoya from the Lawrence Berkeley National Laboratory and Kristin Persson from the University of California, Berkeley, introduce an algorithm for finding the adsorption sites on an arbitrary surface. Knowing the amount of energy required for molecular adsorption is crucial for identifying the best materials for use in electronics and catalysis. Density functional theory can predict adsorption energies but usually requires human intuition to tune the calculations. With so many combinations of surface and adsorbate, an automated method is required. Montoya and Persson use open-source computational tools from the Materials Project to present a workflow for performing high-throughput density functional theory calculations for arbitrary slabs and adsorbed species. |
format |
article |
author |
Joseph H. Montoya Kristin A. Persson |
author_facet |
Joseph H. Montoya Kristin A. Persson |
author_sort |
Joseph H. Montoya |
title |
A high-throughput framework for determining adsorption energies on solid surfaces |
title_short |
A high-throughput framework for determining adsorption energies on solid surfaces |
title_full |
A high-throughput framework for determining adsorption energies on solid surfaces |
title_fullStr |
A high-throughput framework for determining adsorption energies on solid surfaces |
title_full_unstemmed |
A high-throughput framework for determining adsorption energies on solid surfaces |
title_sort |
high-throughput framework for determining adsorption energies on solid surfaces |
publisher |
Nature Portfolio |
publishDate |
2017 |
url |
https://doaj.org/article/6d7647d87d2a454fb1658f93bad3f11d |
work_keys_str_mv |
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1718395170069676032 |