Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design
Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design
复制标题
DOI：
10.1016/j.matt.2023.03.031
复制
发表时间：
2023-04
期刊：
影响因子：
18.9
通讯作者：
Andrew J. Lew;C. Stifler;A. Cantamessa;A. Tits;D. Ruffoni;P. Gilbert;M. Buehler
中科院分区：
文献类型：
--
作者：
Andrew J. Lew;C. Stifler;A. Cantamessa;A. Tits;D. Ruffoni;P. Gilbert;M. Buehler
在进化过程中，生物发展出适应环境的复杂物质结构。基于这些经过时间考验的设计，人类工程的生物启发结构提供了令人兴奋的可能的材料配置。然而，导航不同的结构空间以获得所需的属性仍然是非常重要的。我们专注于人类最坚硬的生物组织，牙釉质，以检查结构-性质的关系。虽然典型的硬度测量耗时且具有破坏性，但我们提出人工智能模型可以直接预测性能，并实现高通量、非破坏性的表征。我们训练了一个深度图像回归神经网络作为代理模型，并使用梯度上升图和显著性图进行可视化，以识别对硬度贡献最大的结构特征。与实验硬度图相比，该模型具有更高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操作的遗传算法，允许引导材料设计，产生具有精确控制硬度的生物启发结构的建议设计。
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.