Artificial intelligence relying on structure-property databases is an emerging powerful tool to discover new materials with targeted properties. However, this approach cannot be easily applied to tangible structures, such as plastic composites and fabrics, because of their high structural complexity. Here, we propose a deep learning computational framework that can implement virtual experiments on tangible structures. Structural representations of complex carbon nanotube films were conducted by multiple generative adversarial networks of scanning electron microscope images at four levels of magnifications, enabling a deep learning prediction of multiple properties such as electrical conductivity and surface area. 1716 virtual experiments were completed within an hour, a task that would take years for real experiments. The data can be used as a versatile database for material science, in analogy to databases of molecules and solids used in cheminformatics. Useful examples are the investigation of correlations between electrical conductivity, specific surface area, wall number phase diagrams, economic performance, and inversely designed supercapacitors.

依赖结构特性数据库的人工智能是一种新兴的强大工具，可以发现具有目标特性的新材料。然而，这种方法不能轻易应用于有形结构，例如塑料复合材料和织物，因为它们的结构复杂性很高。在这里，我们提出了一个深度学习计算框架，可以在有形结构上实施虚拟实验。复杂碳纳米管薄膜的结构表征是通过扫描电子显微镜图像的多个生成对抗网络在四个放大倍数下进行的，从而能够对多种特性（如电导率和表面积）进行深度学习预测。一个小时内完成了 1716 个虚拟实验，真正的实验需要数年时间才能完成。该数据可用作材料科学的通用数据库，类似于化学信息学中使用的分子和固体数据库。有用的例子是研究电导率、比表面积、壁数相图、经济性能和反向设计的超级电容器之间的相关性。