Treffer: AI-Based Enhanced Production of Vanadium Dioxide Thermochromic Films.

The study focuses on optimizing the synthesis process of Vanadium Dioxide (VO<subscript>2</subscript>) films through advanced machine learning (ML) algorithms, enabling precise control over key parameters such as temperature, pressure, and deposition techniques. By utilizing Artificial Intelligence AI-driven predictive modeling aim to achieve improved film quality, uniformity, and thermochromic (TC) performance. This study suggested a novel Tabu Search Optimized Adaptive Long Short-Term Memory (TSO-ALSTM) for the integration of AI to facilitate realtime monitoring and adjustment of production conditions, reducing defects and minimizing waste. The data was preprocessed using Min-max normalization. The proposed method is implemented using Python software. Compared the suggested method with other existing methods. Experimental results demonstrate that AIenhanced processes lead to VO<subscript>2</subscript> films with larger optical switching characteristics, broadening their potential applications in smart windows, advanced thermal management systems, and energy-efficient buildings. The outcomes demonstrate that the suggested approach outperforms the other method in terms of accuracy (90.74 %), recall (76 %), F1 score (81 %), and specificity (99.1 %). This work highlights the transformative impact of AI technologies in materials science, paving the way for the next generation of smart materials. [ABSTRACT FROM AUTHOR]

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