Artificial Intelligence—Engineering Magnetic Materials: Current Status and a Brief Perspective

Abstract

The implementation of artificial intelligence into the research and development of (currently) the most economically relevant classes of engineering hard and soft magnetic materials is addressed. Machine learning is nowadays the key approach utilized in the discovery of new compounds, physical–chemical properties prediction, microstructural/magnetic characterization, and applicability of permanent magnets and crystalline/amorphous soft magnetic alloys. Future opportunities are envisioned on at least two fronts: (a) ultra-low losses materials, as well as processes that enable their manufacturing, unlocking the next step for higher efficiency electrification, power conversion, and distribution; (b) additively manufactured magnetic materials by predicting and developing novel powdered materials properties, generative design concepts, and optimal processing conditions.

Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

“…Additionally, the inverse design of biosensors using predefined nanomaterials allows for the formation of a programmable biosensor to achieve the desired optical and chemical characteristics with enhanced sensitivity. In this regard, machine learning algorithms have been used to synthesize different types of nanoparticles (metallic, polymers, and carbon-based), some of which are also useful in nanomedicine [24][25][26][27][28]. A flowchart representing the general principle of using AI techniques to predict biosensors' characteristics is illustrated in Figure 3.…”