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A Machine Learning Approach to Graph-Theoretical Cluster Expansions of the Energy of Adsorbed Layers

Vignola, E; Steinmann, S; Vandegehuchte, B; Curulla, D; Stamatakis, M; Sautet, P; (2017) A Machine Learning Approach to Graph-Theoretical Cluster Expansions of the Energy of Adsorbed Layers. Journal of Chemical Physics , 147 (054106) 10.1063/1.4985890. Green open access

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Abstract

The accurate description of the energy of adsorbate layers is crucial for the understanding of chemistry at interfaces. For heterogeneous catalysis, not only the interaction of the adsorbate with the surface but also the adsorbate-adsorbate lateral interactions significantly affect the activation energies of reactions. Modeling the interactions of the adsorbates with the catalyst surface and with each other can be efficiently achieved in the cluster expansion Hamiltonian formalism, which has recently been implemented in a graph-theoretical kinetic Monte Carlo (kMC) scheme to describe multi-dentate species. Automating the development of the cluster expansion Hamiltonians for catalytic systems is challenging and requires the mapping of adsorbate configurations for extended adsorbates onto a graphical lattice. The current work adopts machine learning methods to reach this goal. Clusters are automatically detected based on formalized, but intuitive chemical concepts. The corresponding energy coefficients for the cluster expansion are calculated by an inversion scheme. The potential of this method is demonstrated for the example of ethylene adsorption on Pd(111), for which we propose several expansions, depending on the graphical lattice. It turns out that for this system, the best description is obtained as a combination of single molecule patterns and a few coupling terms accounting for lateral interactions.

Type: Article
Title: A Machine Learning Approach to Graph-Theoretical Cluster Expansions of the Energy of Adsorbed Layers
Open access status: An open access version is available from UCL Discovery
DOI: 10.1063/1.4985890
Publisher version: http://dx.doi.org/doi.org/10.1063/1.4985890
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Adsorption Artificial intelligence Hamiltonian mechanics Monte Carlo methods Chemical compounds
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1565398
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