Volume 3, 2025 - Issue 1
An open access journal
Artificial Intelligence: Impact on the Development of Electrical Materials
Abstract
Electrical materials play a pivotal role in electrical engineering, yet conventional research methodologies relying on experimental approaches and theoretical calculations face challenges of low efficiency and high costs. The rapid advancement of artificial intelligence (AI) technology has introduced novel tools for electrical material research, significantly enhancing the efficiency of material design, performance prediction, and experimental optimization. This paper systematically reviews recent progress in AI applications across semiconductor, alloy, energy storage, and metallic materials. In semiconductor research, machine learning has improved analytical precision through optimization of Hubbard U parameters and prediction of perovskite defect transition levels. For alloy materials, AI combined with active learning strategies has accelerated the discovery and performance optimization of high-entropy alloys.Our analysis reveals that AI technology substantially reduces material development cycles and experimental costs through multi-source data integration, quantitative model construction, and multi-objective optimization strategies. Looking forward, with advancements in data science and computational capabilities, AI is poised to play an increasingly critical role in multi-scale material design, performance prediction, and novel material development for electrical engineering, thereby driving technological innovations in power and electronic systems.
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