A research team at the Shanghai Institute of Ceramics, Chinese Academy of Sciences (SICCAS), has published a comprehensive review that maps out how machine learning is transforming the development of functional ceramics. The review appears in Materials Science and Engineering: R: Reports.

The review was led by the Research Group of Passive Integrated Devices and Materials at SICCAS, with Dr. Jincheng QIN serving as the first author and Professor Zhifu LIU as the corresponding author.

Functional ceramics are essential in modern technologies because they can respond to electrical, magnetic, optical, thermal, and acoustic stimuli. However, their development has traditionally been slowed by the complex, non-linear relationships among composition, structure, and processing—making the conventional trial-and-error approach inefficient.

The review systematically outlines an end-to-end machine learning workflow that addresses these challenges. This workflow covers four key steps: data collection, featurization (converting raw material data into machine-readable inputs), algorithm selection, and model interpretation. Rather than relying solely on predefined physical laws, machine learning learns directly from data, enabling rapid property prediction and revealing hidden correlations across different ceramic systems.

A core contribution of this work is its systematic assessment of data-driven progress across major functional ceramic classes, including dielectric, ferroelectric, piezoelectric, electrocaloric, conductive, superconductive, magnetic, and luminescent ceramics. The authors analyzed more than 200 representative models, comparing their target properties and predictive accuracy to identify universal trends and gaps in the field. Beyond simple prediction, the review explores how machine learning can support material classification, calculation enhancement, process optimization, pattern recognition, device design, and failure analysis.

Looking forward, the review highlights the integration of self-driving laboratories and human-machine collaboration as a transformative approach to materials discovery. By combining closed-loop active learning with robotic synthesis and real-time characterization, research cycles can be significantly accelerated. The authors emphasize the need to overcome fragmented data ecosystems through standardized data infrastructures and small-data learning strategies. The synergy of explainable artificial intelligence, multimodal data fusion, and digital twins for real-time performance prediction represents the next frontier, offering a clear path toward the intelligent design and autonomous exploration of next-generation functional materials.

Data-driven research for functional ceramics

Links: https://doi.org/10.1016/j.mser.2026.101213

Contact: Jincheng Qin, Zhifu Liu

Shanghai Institute of Ceramics, Chinese Academy of Sciences

E-mail: qinjincheng@mail.sic.ac.cn, liuzf@mail.sic.ac.cn

Published online: April 1, 2026