.png){kind=icon}
返回
Machine learning-enabled constrained multi-objective design of architected materials
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-023-42415-y.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.
Keyword:
BUTTERFLY WING SCALES
DAMAGE-TOLERANT
BONE
SCAFFOLDS
OPTIMIZATION
FABRICATION
ULTRALIGHT
DISCOVERY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Foam topology bending versus stretching dominated architectures泡沫拓扑弯曲与拉伸主导架构.png)
ACTA MATERIALIA
ACTA MATERIALIAIF9.3
PREAI
分享收藏Designing complex architectured materials with generative adversarial networks使用生成对抗网络设计复杂的建筑材料SCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAI分享收藏
Geometry as a Factor for Tissue Growth: Towards Shape Optimization of Tissue Engineering Scaffolds
PREAI
分享收藏
查看更多内容