AI/ML in designing materials for Spintronics applications

Following to the initiation of artificial intelligence (AI) and machine learning (ML) methodologies for their prediction, optimization, and synthesis towards the spintronic device development. Ferrites and their related spinel oxides which are becoming very useful for spintronics due to their tunable magnetic, electronic, and structural properties. Thus, this review compiles recent developments in performance toward integration of knowledge and AI/ML-based applications on ferrites concerning spintronic traits. It discusses predictions regarding cation distribution engineering, surrogate modeling, defect synthetic optimization, and device-level tailoring or property manipulation. The whole spectrum of studies includes ferrites (e.g., Mn-Zn ferrites, doped nickel ferrites), and these advances are also spelt out in terms of how AI/ML could distinguish fresh designs. Issues such as data scarcity, interpretability, and how AI could integrate with experimental validation will also be touched on. It outlines the various opportunities for AI to be coupled to autonomous laboratories, highthroughput simulations, or even quantum machine learning.