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Review
. 2025 Jul 16;18(14):3334.
doi: 10.3390/ma18143334.

Multiscale Simulation of Nanowear-Resistant Coatings

Xiaoming Liu  1 , Kun Gao  2 , Peng Chen  1 , Lijun Yin  1 , Jing Yang  2
Affiliations
Review

Multiscale Simulation of Nanowear-Resistant Coatings

Xiaoming Liu et al. Materials (Basel). .

Abstract

Nanowear-resistant coatings are critical for extending the service life of mechanical components, yet their performance optimization remains challenging due to the complex interplay between atomic-scale defects and macroscopic wear behavior. While experimental characterization struggles to resolve transient interfacial phenomena, multiscale simulations, integrating ab initio calculations, molecular dynamics, and continuum mechanics, have emerged as a powerful tool to decode structure-property relationships. This review systematically compares mainstream computational methods and analyzes their coupling strategies. Through case studies on metal alloy nanocoatings, we demonstrate how machine learning-accelerated simulations enable the targeted design of layered architectures with 30% improved wear resistance. Finally, we propose a protocol combining high-throughput simulation and topology optimization to guide future coating development.

Keywords: atomic scale; macroscale; multiscale simulation; wear-resistant coating.

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Conflict of interest statement

Xiaoming Liu, Peng Chen and Lijun Yin are currently employed by “Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Power Research Institute Branch, Hohhot 010020, China” while contributing to this manuscript. Our contributions to this work and manuscript were made independently without any requirement, guidance or input by our employer. We received no financial compensation from any source for the contributions we made to the scientific work and manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Multiscale modeling of materials is a ‘divide-and-conquer’ approach to describe the complexity of material behavior. Reprinted with permission from Ref. [24]. Copyright 2021, Elsevier Ltd.
Figure 2
Figure 2
A flowchart detailing an example algorithm for achieving self-consistency using fixed-point (or Roothaan) iterations. Reprinted from Ref. [31].
Figure 3
Figure 3
High-entropy alloy friction simulation modeling. Reprinted with permission from Ref. [52]. Copyright 2021, Elsevier B.V.
Figure 4
Figure 4
Monte Carlo algorithm for predicting film thickness deposited by physical vapor deposition (PVD). Reprinted with permission from Ref. [61]. Copyright 2019, Elsevier B.V.
Figure 5
Figure 5
Meshed model of the substrate with a single track coating: (a) the overall view and (b) the front view, including the partially enlarged view of the cross-section of the coating. Reprinted with permission from Ref. [62]. Copyright 2022, Elsevier B.V.
Figure 6
Figure 6
The flowchart of the proposed machine learning-based atomistic–continuum multiscale analysis: (a) the ML process on the atomic dataset and (b) the ML-based multiscale analysis. Reprinted with permission from Ref. [90]. Copyright 2023, Elsevier Ltd.
See this image and copyright information in PMC

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