Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials

An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. I...

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Autores principales: Kastuar, S. M., Ekuma, C. E., Liu, Z. -L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904584/
https://www.ncbi.nlm.nih.gov/pubmed/35260681
http://dx.doi.org/10.1038/s41598-022-07819-8
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author Kastuar, S. M.
Ekuma, C. E.
Liu, Z. -L.
author_facet Kastuar, S. M.
Ekuma, C. E.
Liu, Z. -L.
author_sort Kastuar, S. M.
collection PubMed
description An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures including their temperature-dependent mechanical properties, and developed a machine learning algorithm for exploring predicted properties.
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spelling pubmed-89045842022-03-09 Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials Kastuar, S. M. Ekuma, C. E. Liu, Z. -L. Sci Rep Article An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures including their temperature-dependent mechanical properties, and developed a machine learning algorithm for exploring predicted properties. Nature Publishing Group UK 2022-03-08 /pmc/articles/PMC8904584/ /pubmed/35260681 http://dx.doi.org/10.1038/s41598-022-07819-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kastuar, S. M.
Ekuma, C. E.
Liu, Z. -L.
Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title_full Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title_fullStr Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title_full_unstemmed Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title_short Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
title_sort efficient prediction of temperature-dependent elastic and mechanical properties of 2d materials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904584/
https://www.ncbi.nlm.nih.gov/pubmed/35260681
http://dx.doi.org/10.1038/s41598-022-07819-8
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