Artificial Intelligence Applications in Ceramic Materials

Advanced ceramic materials are being developed, manufactured, characterized, and applied in ways that are progressively being transformed by artificial intelligence. To determine appropriate compositions, processing conditions, microstructures, and characteristics, traditional ceramic research frequently necessitates a great deal of testing. Large experimental and computational datasets can be analyzed by AI and machine-learning algorithms to find relationships that are challenging to find using traditional approaches. The main uses of AI in ceramic materials are reviewed in this work, including composition design, property prediction, processing optimization, microstructure analysis, defect detection, additive manufacturing, energy-efficient processing, and materials discovery. The advantages, limitations, and prospects of AI-assisted ceramic engineering are also discussed. The integration of AI with experimental methods, computational materials science, and automated manufacturing has the potential to significantly reduce development time, cost, and material waste while enabling the design of ceramics with improved performance.

Artificial Intelligence, Machine Learning, Ceramic Materials, Materials Science, Microstructure, Materials Discovery, Defect Detection, Additive Manufacturing

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