Machine learning hydrogen adsorption on nanoclusters through structural descriptors

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Machine learning hydrogen adsorption on nanoclusters through structural descriptors. / Jäger, Marc O.J.; Morooka, Eiaki V.; Federici Canova, Filippo; Himanen, Lauri; Foster, Adam S.

In: npj Computational Materials, Vol. 4, No. 1, 37, 01.12.2018, p. 1-8.

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@article{c7bb1410e95744b19dd9b95458ea86ba,
title = "Machine learning hydrogen adsorption on nanoclusters through structural descriptors",
abstract = "Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.",
author = "J{\"a}ger, {Marc O.J.} and Morooka, {Eiaki V.} and {Federici Canova}, Filippo and Lauri Himanen and Foster, {Adam S.}",
note = "| openaire: EC/H2020/676580/EU//NoMaD",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41524-018-0096-5",
language = "English",
volume = "4",
pages = "1--8",
journal = "npj Computational Materials",
issn = "2057-3960",
publisher = "Nature Publishing Group",
number = "1",

}

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TY - JOUR

T1 - Machine learning hydrogen adsorption on nanoclusters through structural descriptors

AU - Jäger, Marc O.J.

AU - Morooka, Eiaki V.

AU - Federici Canova, Filippo

AU - Himanen, Lauri

AU - Foster, Adam S.

N1 - | openaire: EC/H2020/676580/EU//NoMaD

PY - 2018/12/1

Y1 - 2018/12/1

N2 - Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

AB - Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

UR - http://www.scopus.com/inward/record.url?scp=85050615217&partnerID=8YFLogxK

U2 - 10.1038/s41524-018-0096-5

DO - 10.1038/s41524-018-0096-5

M3 - Article

VL - 4

SP - 1

EP - 8

JO - npj Computational Materials

JF - npj Computational Materials

SN - 2057-3960

IS - 1

M1 - 37

ER -

ID: 27132810