Title: Multi-scale model predicting friction of crystalline materials
Authors: Torche, Paola C.
Silva, Andrea
Kramer, Denis 
Polcar, Tomas
Hovorka, Ondrej
Language: en
Keywords: 2D materials;density functional theory calculations;stochastic thermodynamics;tribology
Subject (DDC): DDC::500 Naturwissenschaften und Mathematik::530 Physik::536 Wärme
Issue Date: 13-Dec-2021
Publisher: Wiley-VCH
Document Type: Article
Journal / Series / Working Paper (HSU): Advanced Materials Interfaces 
Volume: 9
Issue: 4
Pages: ca. 9 Seiten
Publisher Place: Weinheim
Abstract: 
A multi-scale computational framework suitable for designing solid lubricant interfaces fully in silico is presented. The approach is based on stochastic thermodynamics founded on the classical thermally activated 2D Prandtl–Tomlinson model, linked with first principles methods to accurately capture the properties of real materials. It allows investigating the energy dissipation due to friction in materials as it arises directly from their electronic structure, and naturally accessing the time-scale range of a typical friction force microscopy. This opens new possibilities for designing a broad class of material surfaces with atomically tailored properties. The multi-scale framework is applied to a class of 2D layered materials and reveals a delicate interplay between the topology of the energy landscape and dissipation that known static approaches based solely on the energy barriers fail to capture.
Description: 
Funding Information: This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No. 721642: SOLUTION. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Publisher Copyright: {\textcopyright} 2021 Wiley-VCH GmbH Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Organization Units (connected with the publication): Computational Material Design 
ISSN: 2196-7350
DOI: 10.1002/admi.202100914
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