The design of stealthy hyperuniform materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I upload the dataset for the concept of evolving wave networks. The datasets are applied to introduce the concept of evolving wave networks (dataset for Fig. 2), classify material states according to network parameters (dataset for Fig. 3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (dataset for Fig. 4), realize preferential attachment in evolving wave networks (dataset for Fig. 5), and obtain the following phase diagram of disordered materials (dataset for Fig. 6). raw_data_FigXabc: the file represents the data for the figure X and abc denotes the sub-figure number. raw_data_Fig6_alpha_MMM: the file represents the data for figure 6: SHU states, and MMM denotes the value of alpha that determines the preference / anti-preference. raw_data_Fig6_Crystal_NNN: the file represents the data for figure 6: Crystal states, and NNN denotes the particle number. raw_data_Fig6_Poisson: the file represents the data for figure 6: Poisson state. Each file is composed of two-column data, representing (x,y) positions of particles. While each realization possesses 500 particles (500 row data), [raw_data_Fig3adgj], [raw_data_Fig3behk], [raw_data_Fig4a], [raw_data_Fig4b], [raw_data_Fig4c], [raw_data_Fig4d], [raw_data_Fig4e], [raw_data_Fig4f], [raw_data_Fig5abc], [raw_data_Fig5def], and [raw_data_Fig5ghi] include the data for 100 realizations: 50000 row data. In the data for Fig. 6, the SHU and Poisson states also possess 50000 row data with 100 realizations, while the crystal states have 500 row data with 1 realization.

The design of stealthy hyperuniform materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I upload the dataset for the concept of evolving wave networks. The datasets are applied to introduce the concept of evolving wave networks (dataset for Fig. 2), classify material states according to network parameters (dataset for Fig. 3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (dataset for Fig. 4), realize preferential attachment in evolving wave networks (dataset for Fig. 5), and obtain the following phase diagram of disordered materials (dataset for Fig. 6). raw_data_FigXabc: the file represents the data for the figure X and abc denotes the sub-figure number. raw_data_Fig6_alpha_MMM: the file represents the data for figure 6: SHU states, and MMM denotes the value of alpha that determines the preference / anti-preference. raw_data_Fig6_Crystal_NNN: the file represents the data for figure 6: Crystal states, and NNN denotes the particle number. raw_data_Fig6_Poisson: the file represents the data for figure 6: Poisson state. Each file is composed of two-column data, representing (x,y) positions of particles. While each realization possesses 500 particles (500 row data), [raw_data_Fig3adgj], [raw_data_Fig3behk], [raw_data_Fig4a], [raw_data_Fig4b], [raw_data_Fig4c], [raw_data_Fig4d], [raw_data_Fig4e], [raw_data_Fig4f], [raw_data_Fig5abc], [raw_data_Fig5def], and [raw_data_Fig5ghi] include the data for 100 realizations: 50000 row data. In the data for Fig. 6, the SHU and Poisson states also possess 50000 row data with 100 realizations, while the crystal states have 500 row data with 1 realization.

The design of stealthy hyperuniform materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I upload the dataset for the concept of evolving wave networks. The datasets are applied to introduce the concept of evolving wave networks (dataset for Fig. 2), classify material states according to network parameters (dataset for Fig. 3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (dataset for Fig. 4), and realize preferential attachment in evolving wave networks (dataset for Fig. 5). raw_data_FigXabc: X represents the figure number and abc denotes the sub-figure number. Each file is composed of two-column data, representing (x,y) positions of particles. While each realization possesses 500 particles (500 row data), [raw_data_Fig3adgj], [raw_data_Fig3behk], [raw_data_Fig4a], [raw_data_Fig4b], [raw_data_Fig4c], [raw_data_Fig4d], [raw_data_Fig4e], [raw_data_Fig4f], [raw_data_Fig5abc], [raw_data_Fig5def], and [raw_data_Fig5ghi] include the data for 100 realizations: 50000 row data.