Materials for wave control need to be both anisotropic and spatially distributed. Traditional method is to first design a microstructure with anisotropic property, and then change geometric parameters of the microstructure according to analytical theory or numerical calculation. Unlike the traditional method, mechanical properties of digital materials can be easily tuned by changing the 0/1 ordering without changing the geometry of digital materials. However, determining suitable orderings of digital materials according to target properties remains a key challenge. In this paper, we establish a digital structural genome to solve this problem. By combining the developed machine learning method with finite element method, we can quickly calculate elastic wave properties of digital materials with all orderings and finally establish a digital structural genome quickly and accurately. The complete digital structural genome provides us with a fast approach to design anisotropy and spatial distribution of materials. Our research unequivocally shows that the establishment of a complete structural genome database of digital materials is of great significance for inverse design multifunctional structures, and can opens an avenue to achieve wave control on demand, such as corner cloak and acoustic carpet cloak.

用于波控制的材料需要具有各向异性和空间分布性。传统方法是先设计具有各向异性的显微组织，然后根据解析理论或数值计算改变显微组织的几何参数。与传统方法不同，数字材料的机械性能可以通过改变 0/1 顺序轻松调整，而无需改变数字材料的几何形状。然而，根据目标属性确定合适的数字材料排序仍然是一个关键挑战。在本文中，我们建立了一个数字结构基因组来解决这个问题。通过将开发的机器学习方法与有限元方法相结合，我们可以快速计算所有排序的数字材料的弹性波特性，最终快速准确地建立数字结构基因组。完整的数字结构基因组为我们提供了一种设计材料各向异性和空间分布的快速方法。我们的研究明确表明，建立完整的数字材料结构基因组数据库对于逆向设计多功能结构具有重要意义，可以为按需实现波浪控制开辟一条途径，例如角斗篷和声学地毯斗篷。