ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Data-Driven Materials Research and Development for Functional Coatings.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Recent Advances in Polypyrrole-Based Functional Coatings: Surface Protection and Emerging Applications.Materials (Basel, Switzerland) · 2026Review
- Robot End-Effectors Adaptive Design Method Based on Embedding Domain Knowledge into Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- A Multi-Step Deposition Strategy for β-SiC Coatings on CAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Public Image Dataset for Surface Defect Detection of Water-Based Coated Wood Products.Scientific data · 2026Article
- A big data approach to artificial intelligence driven predictive modelling for optimizing material properties in additive manufacturing.Scientific reports · 2026Article
- AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications.Polymers · 2025Review
- Toward a New Generation of Organic Coatings for Corrosion Protection Based on Poly(phenylene methylene).Langmuir : the ACS journal of surfaces and colloids · 2025Review
- Physics-Informed Neural Network Modeling of Inflating Dielectric Elastomer Tubes for Energy Harvesting Applications.Polymers · 2025Article
- Nanographene horizons: the emerging role of hexa-RSC advances · 2025Review
- Development of data driven models to accurately estimate density of fatty acid ethyl esters.Scientific reports · 2025Article
- Environmentally sustainable epoxy nanocomposite coating reinforced with chitosan derived nitrogen doped graphene for enhanced corrosion resistance and mechanical performance.Scientific reports · 2025Article
- Article
- Article
- Enhancing abrasive wear resistance of IS-2062 steel in hydroturbine applications with WC-12 Co and WC-10 Co-4 Cr HVOF coatings.Scientific reports · 2025Article
- Data-Driven Materials Research and Development for Functional Coatings.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.