研究目的
To propose an inversion design approach for dielectric materials based on the first-principle method, starting from specific demanded dielectric properties to solve for atomic structure characteristics.
研究成果
The first-principle inversion design of dielectric material is a new attempt in electromagnetic functional materials design, expected to provide a novel approach for material design by reversing the process from property to structure, leveraging advancements in quantum mechanics and optimization algorithms.
研究不足
The paper is theoretical and proposes an approach without experimental validation. Key issues such as quantization of field structures, polarization modeling, and development of uniform calculation methods are identified as challenges that need further research. The inversion problems are nonlinear and ill-posed, requiring advanced algorithms.
1：Experimental Design and Method Selection:
The paper proposes a theoretical inversion design method based on the first-principle, involving reverse engineering and lattice inversion methods. It discusses the use of quantum mechanics, perturbation theory, and optimization algorithms like artificial neural networks and genetic algorithms for solving inversion problems.
2：Sample Selection and Data Sources:
Not specified in the paper; it is a theoretical proposal without experimental data or samples.
3：List of Experimental Equipment and Materials:
Not specified; the paper is theoretical and does not mention specific equipment or materials.
4：Experimental Procedures and Operational Workflow:
The proposed workflow includes starting from demanded dielectric properties, calculating energy level structure and electronic density distribution, and then solving for atomic or molecular composition using inversion models. Steps involve quantization of electromagnetic and optical fields, polarization modeling, and consistent calculation methods across frequency bands.
5：Data Analysis Methods:
Involves first-principle calculations, density functional theory, and optimization algorithms for inversion solutions.
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