Data Set for Optimization and Simulation of Translucent Steel Using Genetic Algorithms and DFT-Based Calculations

Dataset Description

This dataset accompanies the theoretical study on the development of an innovative material called "translucent steel." The study aimed to combine high mechanical strength with optical translucency, utilizing genetic algorithms for optimizing structural and optical properties, along with Density Functional Theory (DFT)-based calculations.

The dataset includes:

Input parameters and results from calculations performed using the ORCA software.

Python scripts used for automating analyses, generating electron density maps, and processing results.

Detailed results from the genetic algorithms, including fitness evolution and optimal combinations tested.

Graphs and tables illustrating refractive indices, elastic modulus, and optimized structural properties.

This dataset is valuable for researchers in materials science, computational modeling, and applications of artificial intelligence in the development of advanced materials. It also provides a foundation for experimental validation of advanced metallic composites.

Keywords: Translucent Steel, Genetic Algorithms, DFT, Materials Science, ORCA, Computational Modeling.

数据集描述

本数据集配套于一项针对名为“半透明钢（translucent steel）”的创新材料研发的理论研究。该研究旨在兼顾高机械强度与光学半透明性，采用遗传算法优化结构与光学性能，并辅以基于密度泛函理论（DFT）的计算。

本数据集包含以下内容：
1. 使用ORCA软件完成的计算输入参数与计算结果；
2. 用于自动化分析、生成电子密度图及处理计算结果的Python脚本；
3. 遗传算法的详细结果，包括适应度演化历程与经测试的最优组合方案；
4. 用于展示折射率、弹性模量及优化后结构性能的图表。

本数据集对于材料科学、计算建模领域，以及人工智能在先进材料研发中的应用相关研究者具有重要价值，同时也为先进金属复合材料的实验验证提供了研究基础。

关键词：半透明钢（Translucent Steel）、遗传算法、密度泛函理论（DFT）、材料科学、ORCA、计算建模