Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determi...

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Main Authors: Thomas Martynec, Christos Karapanagiotis, Sabine H. L. Klapp, Stefan Kowarik
Format: Article
Language:English
Published: Nature Publishing Group 2021-09-01
Series:Communications Materials
Online Access:https://doi.org/10.1038/s43246-021-00188-1
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spelling doaj-7d4d7877ccbf48aa9043f85191147eca2021-09-05T11:25:11ZengNature Publishing GroupCommunications Materials2662-44432021-09-01211910.1038/s43246-021-00188-1Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growthThomas Martynec0Christos Karapanagiotis1Sabine H. L. Klapp2Stefan Kowarik3Institut für Theoretische Physik, Technische Universität BerlinBundesanstalt für Materialforschung und -prüfung (BAM)Institut für Theoretische Physik, Technische Universität BerlinUniversität Graz, Institut für Chemie, NAWI Graz, Physikalische und Theoretische ChemieExperiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determined.https://doi.org/10.1038/s43246-021-00188-1
collection DOAJ
language English
format Article
sources DOAJ
author Thomas Martynec
Christos Karapanagiotis
Sabine H. L. Klapp
Stefan Kowarik
spellingShingle Thomas Martynec
Christos Karapanagiotis
Sabine H. L. Klapp
Stefan Kowarik
Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
Communications Materials
author_facet Thomas Martynec
Christos Karapanagiotis
Sabine H. L. Klapp
Stefan Kowarik
author_sort Thomas Martynec
title Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
title_short Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
title_full Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
title_fullStr Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
title_full_unstemmed Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
title_sort machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
publisher Nature Publishing Group
series Communications Materials
issn 2662-4443
publishDate 2021-09-01
description Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determined.
url https://doi.org/10.1038/s43246-021-00188-1
work_keys_str_mv AT thomasmartynec machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth
AT christoskarapanagiotis machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth
AT sabinehlklapp machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth
AT stefankowarik machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth
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