Nanoscale slip length prediction with machine learning tools

Abstract This work incorporates machine learning (ML) techniques, such as multivariate regression, the multi-layer perceptron, and random forest to predict the slip length at the nanoscale. Data points are collected both from our simulation data and data from the literature, and comprise Molecular D...

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Autores principales: Filippos Sofos, Theodoros E. Karakasidis
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/42a0d2d89ab94a18bd3f2b823ffb3de8
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