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Training artificial neural network for optimization of nanostructured VO2-based smart window performance.

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    This study introduces a machine learning method to optimize vanadium dioxide (VO2) smart windows. The approach enhances luminous transmittance and solar modulation for improved window performance.

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    Area of Science:

    • Materials Science
    • Nanotechnology
    • Computational Physics

    Background:

    • Smart windows offer dynamic control over light and heat transmission.
    • Vanadium dioxide (VO2) is a promising material for smart windows due to its thermochromic properties.
    • Optimizing VO2 nanostructure design is crucial for maximizing smart window performance.

    Purpose of the Study:

    • To develop and apply a machine learning approach for designing and optimizing VO2-based nanostructured smart windows.
    • To establish a relationship between structural parameters and performance metrics (Tlum and ΔTsol).
    • To identify optimal structural parameters for enhanced smart window functionality.

    Main Methods:

    • Utilized an artificial neural network (ANN) trained on data from first-principle electromagnetic simulations (FDTD method).
    • Employed a classical trust region algorithm to find optimal combinations of Tlum and ΔTsol.
    • Integrated machine learning with electromagnetic simulations for materials design.

    Main Results:

    • Successfully trained an ANN to predict VO2 smart window performance based on structural parameters.
    • Identified optimal structural configurations for maximizing luminous transmittance and solar modulation.
    • Demonstrated the effectiveness of the machine learning approach in optimizing complex material systems.

    Conclusions:

    • The proposed machine learning method provides an efficient and flexible approach to designing and optimizing VO2 smart windows.
    • The methodology offers clear uncertainty limits, facilitating future experimental validation and realization.
    • This work paves the way for advanced, AI-driven design of functional nanomaterials.