Machine learning is widely used in several systems in different subject areas, especially in materials discovery and design. In combination with fuzzy logic, the hybrid model can be developed for optimization and prediction of material properties and related phenomena, ensuring high accuracy for seeking the research gaps of existing advanced materials. The concept of Supervised learning. Unsupervised Learning, semisupervised learning, and reinforcement learning can be used in conjunction with Singleton fuzzifier, Gaussian fuzzifier, and trapezoidal or triangular fuzzifier. They can be used for creating novel materials considering certain design parameters. The data relating to materials formulated by the fuzzy machine learning approach can be used to resolve real-life problems. Moreover, it can help create future opportunities for new concepts in the industry.

The fuzzy logic provides a workspace for computation with words. It helps with managing uncertainty during the design of expert systems. It has now become an unavoidable part of machine learning as it can handle imprecise and uncertain situations. Fuzzy-machine learning in material science can suggest possible solutions and forecast the potential directions of future research. The deployment of new technologies to collect data are one of the challenges for applying this approach. The complexities of unstructured and heterogeneous data processing are problematic, especially if we need to predict and optimize models. Ethical implication is also a challenge.

The aim of this Special Issue is to bring together original research and review articles to discuss fuzzy machine learning-based materials discovery and design. Submissions should focus on the approaches used to improve the processes in the industry.

Potential topics include but are not limited to the following:

• Data analysis of fuzzy machine learning-based material properties
• Fuzzy machine learning-based material property prediction and optimization
• Fuzzy machine learning-based material design and discovery
• Model development of fuzzy machine learning-based materials
• Fuzzy-machine learning-based material design for medical applications
• Fuzzy-machine learning-based material design for electrical, optical, and thermal problems
• Fuzzy-machine learning-based material design for building materials
• Novel fuzzy machine learning-based materials design concepts
• Crystal structure prediction of fuzzy machine learning-based materials
• Component prediction to create fuzzy machine learning-based materials
• Fuzzy-machine learning-based material management
• Two-dimensional (2D) in fuzzy-machine learning-based materials
• Additive manufacturing in fuzzy machine learning-based materials
• Nanoparticles in fuzzy machine learning-based materials