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Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks

Published on: May 8, 2015

Data-Driven Materials Research and Development for Functional Coatings.

Kai Xu¹, Xuelian Xiao^1,2, Linjing Wang¹

¹Key Laboratory of Advanced Marine Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, 315201, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|September 19, 2024

Summary

This summary is machine-generated.

Data-driven approaches accelerate the discovery of functional coatings by identifying correlations between material properties and performance. This review highlights advances in data-driven research and development (R&D) for coatings, overcoming traditional experimental limitations.

Keywords:

artificial intelligence data‐driven functional coatings machine learning materials design

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Last Updated: Jun 22, 2026

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Area of Science:

Materials Science
Surface Engineering
Computational Materials Science

Background:

Functional coatings are crucial across industries for protection and unique properties.
Traditional coating design relies on time-consuming, multi-parameter experimentation.
Limitations in conventional methods necessitate innovative R&D strategies.

Purpose of the Study:

To summarize recent advances in data-driven research and development (R&D) for functional coatings.
To highlight the importance, data sources, processes, and applications of data-driven approaches in coating science.
To provide an overview of the potential and future directions in data-driven functional coating development.

Main Methods:

Review of recent literature on data-driven materials R&D for functional coatings.
Discussion of challenges in traditional coating design methods.
Introduction of typical data-driven processes and their application in identifying parameter-performance correlations.

Main Results:

Data-driven approaches enable efficient prediction and design of functional coatings.
Case studies demonstrate accelerated discovery of tailored coating properties across diverse industries.
Identification of correlations between input parameters and coating performance is facilitated.

Conclusions:

Data-driven methods significantly enhance the efficiency and effectiveness of functional coating R&D.
Integrating advanced techniques and diverse data sources will drive future innovations in coating design.
The paradigm shift towards data-driven materials science offers substantial potential for developing next-generation functional coatings.