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Coatings
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Coatings Design and Characterization Using AI and Machine Learning Methods
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Coatings Design and Characterization Using AI and Machine Learning Methods

A Special Issue of Coatings (ISSN 2079-6412) belonging to the section "Surface Characterization, Deposition and Modification".

Deadline for manuscript submissions: 10 November 2026 | Viewed by 2294

Editor

Dr. Yuyun Yang

E-Mail Website
Guest Editor
Institute of Surface/Interface Science and Technology, Department of Material Science and Chemical Engineering, Harbin Engineering University, Harbin 15001, China
Interests: surface modification and treatment; coatings properties and characterization; bioactive and biocompatible coatings; anti-corrosive and anti-friction coatings; nanostructured surface modification; additive manufacture of coatings; machine learning in coating properties
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integration of artificial intelligence (AI) and machine learning (ML) in the design, characterization, and optimization of coatings. It invites research papers that explore how AI/ML can enhance the development of new coatings, improve performance predictions, and streamline processes. Key themes include AI-driven coatings design, predictive modeling, advanced characterization, digital twins, and smart coatings. This Special Issue aims to showcase how AI/ML can accelerate innovation in the coatings industry, from sustainable materials development to autonomous systems for coating application and quality control.

This Special Issue will serve as a forum for papers covering the following themes:

  • AI-driven coatings design and optimization;
  • Predictive modeling for coatings performance;
  • Advanced characterization of coatings with AI/ML integration;
  • Digital twins and virtual testing of coatings;
  • AI in additive manufacturing and functional coatings;
  • Sustainable and environmentally friendly coatings through AI;
  • Data-driven approaches to coatings development;
  • AI-powered multi-scale modeling and simulation of coatings;
  • AI for smart coatings and autonomous systems;
  • AI in coating process optimization and quality control.

Dr. Yuyun Yang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Coatings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • coatings design
  • material characterization
  • artificial intelligence (AI)
  • machine learning (ML)
  • data-driven design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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12 pages, 6629 KB  
Article
WC-Reinforced Nickel–Aluminum Bronze Coatings: Tribological and Corrosion Behavior
by Shikang Lin, Yuyun Yang, Heyue Yin, Peijia Liu, Jingyu Zhang, Jiaming Zheng, Xiufang Cui, Guo Jin, Lu Zhao and Peng She
Coatings 2026, 16(2), 232; https://doi.org/10.3390/coatings16020232 - 12 Feb 2026
Viewed by 803
Abstract
This study investigates the tribological and electrochemical corrosion behavior of laser-clad nickel–aluminum bronze (NAB) coatings reinforced with WC particles (0, 8, 16 wt.%). Through microstructural characterization and phase analysis, it was found that in the NAB coating containing 16% WC, the WC particles [...] Read more.
This study investigates the tribological and electrochemical corrosion behavior of laser-clad nickel–aluminum bronze (NAB) coatings reinforced with WC particles (0, 8, 16 wt.%). Through microstructural characterization and phase analysis, it was found that in the NAB coating containing 16% WC, the WC particles and carbides were uniformly distributed, serving as a reinforcing scaffold. During the friction and wear process, they effectively reduced the contact area between the counter ball and the NAB matrix to a certain extent, smoothing the wear process and resulting in a more stable friction coefficient. Electrochemical testing demonstrates that WC addition significantly enhances corrosion resistance: NAB + 8%WC exhibits a low corrosion current density (icorr), the highest polarization resistance, and the densest protective film. The dual mechanisms—grain boundary blocking and ion channel obstruction—reduce selective Al/Fe leaching and minimize Cl− penetration. The 8% WC formulation optimizes the electrochemical performance, providing excellent corrosion resistance in a simulated marine environment. Full article
(This article belongs to the Special Issue Coatings Design and Characterization Using AI and Machine Learning Methods)
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Review

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44 pages, 4498 KB  
Review
Precision Edible Coating Engineering: Deposition Physics, Image Metrology and a Roadmap Toward Digital-Twin-Ready Edible Surface Interfaces
by Cristian Aarón Dávalos-Saucedo, Giovanna Rossi-Márquez, Sergio Rodríguez-Miranda and Carlos E. Castañeda
Coatings 2026, 16(7), 812; https://doi.org/10.3390/coatings16070812 - 8 Jul 2026
Viewed by 502
Abstract
Edible coatings are widely studied as food-compatible formulations for reducing moisture loss, oxidation, microbial spoilage, oil uptake, and quality deterioration. Their translation from laboratory formulation to industrial use, however, depends not only on film-forming composition but also on controlled deposition, retained dose, surface [...] Read more.
Edible coatings are widely studied as food-compatible formulations for reducing moisture loss, oxidation, microbial spoilage, oil uptake, and quality deterioration. Their translation from laboratory formulation to industrial use, however, depends not only on film-forming composition but also on controlled deposition, retained dose, surface coverage, drying history, defect formation, hygienic operation, and reproducible performance on heterogeneous food surfaces. An OpenAlex-supported evidence-map audit (2014–2026) was used to separate direct food-coating validation from adjacent engineering models. This review reframes edible coatings as engineered deposited interfaces and proposes a claim-controlled, evidence-tiered framework linking food-grade biopolymer fluids, processability, atomization, droplet impact, wet-film evolution, dry-film structure, image-based metrology, multiphase modeling, and food-performance endpoints. This review outlines the prerequisites for future digital-twin-ready edible coating workflows by linking functional biopolymer fluids, deposition technologies, droplet physics, intelligent image metrology, Computational Fluid Dynamics (CFD), Volume of Fluid (VOF), uncertainty reporting, food-performance endpoints, safety, Life-Cycle Assessment (LCA), Techno-Economic Analysis (TEA) and patent-aware innovation. Digital twins are treated as a future integration target that depends on validated inputs, standardized reporting, deposition metrology and food-specific model validation. The central argument is that progress in edible coatings requires fewer isolated formulation claims and stronger validated links between deposited-interface properties and food-relevant function. A minimum reporting checklist is proposed to support reproducible comparison of deposition routes, coating structures, and translation potential. Full article
(This article belongs to the Special Issue Coatings Design and Characterization Using AI and Machine Learning Methods)
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