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.

Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix to form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail to capture the three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography enabling three-dimensional imaging of multiple CSROs. We showcase our approach by addressing a long-standing question encountered in body-centred-cubic Fe-Al alloys that see anomalous property changes upon heat treatment. We use it to evidence non-statistical B₂-CSRO instead of the generally-expected D0₃-CSRO. We introduce quantitative correlations among annealing temperature, CSRO, and nano-hardness and electrical resistivity. Our approach is further validated on modified D0₃-CSRO detected in Fe-Ga. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in different materials and help design future high-performance materials.

The mechanical properties of metallic materials often degrade under harsh cryogenic conditions, posing challenges for low-temperature infrastructures¹. Here we introduce a dual-scale atomic-ordering nanostructure, characterized by an exceptionally high number density of co-existing subnanoscale short-range ordering (approximately 2.4 × 10²⁶ m⁻³) and nanoscale long-range ordering (approximately 4.5 × 10²⁵ m⁻³) domains, within a metallic solid-solution matrix in a CoNiV-based alloy to improve the synergy of strength and ductility at low temperatures. We observe an ordering-induced increase in dislocation shear stress as well as a more rapid dislocation multiplication owing to the dislocation blocking effect of nanoscale long-range ordering and the associated generation of new dislocations. The latter effect also releases stress concentrations at nanoscale long-range-ordered obstacles that otherwise would promote damage initiation and failure. Consequently, the alloy shows a strength–elongation product of 76 GPa % with a yield strength of approximately 1.2 GPa at 87 K, outperforming materials devoid of such ordering hierarchy, containing only short-range ordered or coherent precipitates of a few tens of nanometres. Our results highlight the impact of dual co-existing chemical ordering on the mechanical properties of complex alloys and offer guidelines to control these ordering states to enhance their mechanical performance for cryogenic applications.