Over evolution, organisms develop complex material structures fit to their environments. Based on these time-tested designs, human-engineered bioinspired structures offer exciting possible materials configurations. However, navigating diverse structure spaces for attaining desired properties remains non-trivial. We focus on the hardest biological tissue in humans, tooth enamel, to examine the structure-property relationship. While typical hardness measurements are time consuming and destructive, we propose that artificial intelligence models can predict properties directly and enable high-throughput, non-destructive characterization. We train a deep image regression neural network as a surrogate model and visualize with gradient ascent and saliency maps to identify structural features contributing most to hardness. This model demonstrates improved spatial resolution and sensitivity compared with experimental hardness maps. Using this rapid hardness testing model, a generative adversarial model, and a genetic algorithm that operates in latent space, allows for guided materials design, yielding proposed designs for bioinspired structures with precisely controlled hardness.

在进化过程中，生物体发展出复杂的物质结构适合他们的环境。基于这些经过时间考验的设计，人类工程仿生结构提供了令人兴奋的可能材料配置。然而，导航不同的结构空间以获得所需的特性仍然很重要。我们专注于人类最坚硬的生物组织——牙釉质，以检验其结构与特性的关系。虽然典型的硬度测量非常耗时且具有破坏性，但我们建议人工智能模型可以直接预测特性并实现高通量、非破坏性表征。我们训练一个深度图像回归神经网络作为替代模型，并使用梯度上升和显着图进行可视化，以识别对硬度贡献最大的结构特征。与实验硬度图相比，该模型展示了改进的空间分辨率和灵敏度。使用这种快速硬度测试模型、生成对抗模型和在潜在空间中运行的遗传算法，可以指导材料设计，为具有精确控制硬度的仿生结构提出设计建议。