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

Author

Listed:
  • Bo Peng

    (Tsinghua University
    Tsinghua University)

  • Ye Wei

    (Tsinghua University)

  • Yu Qin

    (Peking University)

  • Jiabao Dai

    (Tsinghua University
    Tsinghua University)

  • Yue Li

    (Max-Planck-Institut für Eisenforschung)

  • Aobo Liu

    (Tsinghua University
    Tsinghua University)

  • Yun Tian

    (Peking University Third Hospital)

  • Liuliu Han

    (Max-Planck-Institut für Eisenforschung)

  • Yufeng Zheng

    (Peking University)

  • Peng Wen

    (Tsinghua University
    Tsinghua University)

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.

Suggested Citation

  • Bo Peng & Ye Wei & Yu Qin & Jiabao Dai & Yue Li & Aobo Liu & Yun Tian & Liuliu Han & Yufeng Zheng & Peng Wen, 2023. "Machine learning-enabled constrained multi-objective design of architected materials," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42415-y
    DOI: 10.1038/s41467-023-42415-y
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    References listed on IDEAS

    as
    1. Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
    2. Minh-Son Pham & Chen Liu & Iain Todd & Jedsada Lertthanasarn, 2019. "Damage-tolerant architected materials inspired by crystal microstructure," Nature, Nature, vol. 565(7739), pages 305-311, January.
    3. Hongtao Yang & Bo Jia & Zechuan Zhang & Xinhua Qu & Guannan Li & Wenjiao Lin & Donghui Zhu & Kerong Dai & Yufeng Zheng, 2020. "Alloying design of biodegradable zinc as promising bone implants for load-bearing applications," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    4. Minh-Son Pham & Chen Liu & Iain Todd & Jedsada Lertthanasarn, 2019. "Publisher Correction: Damage-tolerant architected materials inspired by crystal microstructure," Nature, Nature, vol. 567(7748), pages 14-14, March.
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    Cited by:

    1. Yingqi Jia & Ke Liu & Xiaojia Shelly Zhang, 2024. "Modulate stress distribution with bio-inspired irregular architected materials towards optimal tissue support," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Zhuoran Xia & Xiangyi Huang & Jiaqi Liu & Wen Dai & Liuxiong Luo & Zhaohan Jiang & Shen Gong & Yuyuan Zhao & Zhou Li, 2024. "Designing Ni2MnSn Heusler magnetic nanoprecipitate in copper alloy for increased strength and electromagnetic shielding," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Wei Li & Zhong-Hui Shen & Run-Lin Liu & Xiao-Xiao Chen & Meng-Fan Guo & Jin-Ming Guo & Hua Hao & Yang Shen & Han-Xing Liu & Long-Qing Chen & Ce-Wen Nan, 2024. "Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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