Machine learning-enabled constrained multi-objective design of architected materials

Bo Peng, Ye Wei (), Yu Qin (), Jiabao Dai, Yue Li, Aobo Liu, Yun Tian, Liuliu Han, Yufeng Zheng and Peng Wen ()
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Bo Peng: Tsinghua University
Ye Wei: Tsinghua University
Yu Qin: Peking University
Jiabao Dai: Tsinghua University
Yue Li: Max-Planck-Institut für Eisenforschung
Aobo Liu: Tsinghua University
Yun Tian: Peking University Third Hospital
Liuliu Han: Max-Planck-Institut für Eisenforschung
Yufeng Zheng: Peking University
Peng Wen: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-42415-y

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