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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Autores principales:	Thomas Martynec, Christos Karapanagiotis, Sabine H. L. Klapp, Stefan Kowarik
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/7d4d7877ccbf48aa9043f85191147eca
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spelling	oai:doaj.org-article:7d4d7877ccbf48aa9043f85191147eca2021-12-02T19:10:21ZMachine learning predictions of surface migration barriers in nucleation and non-equilibrium growth10.1038/s43246-021-00188-12662-4443https://doaj.org/article/7d4d7877ccbf48aa9043f85191147eca2021-09-01T00:00:00Zhttps://doi.org/10.1038/s43246-021-00188-1https://doaj.org/toc/2662-4443Experiments 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.Thomas MartynecChristos KarapanagiotisSabine H. L. KlappStefan KowarikNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENCommunications Materials, Vol 2, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Thomas Martynec Christos Karapanagiotis Sabine H. L. Klapp Stefan Kowarik Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
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.
format	article
author	Thomas Martynec Christos Karapanagiotis Sabine H. L. Klapp Stefan Kowarik
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 Portfolio
publishDate	2021
url	https://doaj.org/article/7d4d7877ccbf48aa9043f85191147eca
work_keys_str_mv	AT thomasmartynec machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth AT christoskarapanagiotis machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth AT sabinehlklapp machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth AT stefankowarik machinelearningpredictionsofsurfacemigrationbarriersinnucleationandnonequilibriumgrowth
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Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

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