Data‐Driven Materials Research and Development for Functional Coatings

Autor: Kai Xu, Xuelian Xiao, Linjing Wang, Ming Lou, Fangming Wang, Changheng Li, Hui Ren, Xue Wang, Keke Chang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Advanced Science, Vol 11, Iss 42, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 2198-3844
70696659
DOI: 10.1002/advs.202405262
Popis: 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.
Databáze: Directory of Open Access Journals
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