Strong and ductile Resinvar alloys with temperature- and time-independent resistivity.

Materials with well-defined electrical resistivity that does not change with temperature or time are important in robotics, communication and automation. However, the challenge of designing such materials has remained elusive due to the temperature-dependent electron-phonon scattering. Moreover, resistive electrical conductors used in flexible and mobile systems under high mechanical loads must possess both high strength and ductility. Achieving such multi-properties presents a fundamental challenge. Here, we solve this problem by combining multicomponent alloy design with atomic-scale chemistry tuning. We term the resultant material 'Resinvar' alloy, due to its invariable resistivity (148 µΩ·cm) over wide temperature ranges from room temperature to 723 K. The alloy also has high tensile strength (948 MPa) at large tensile elongation (53%). The distorted lattice, chemical short-range order and ordered coherent nanoprecipitates in the material enable the invariant resistivity via promoting temperature-independent inelastic electron scattering, and contribute to the excellent strength-ductility synergy by manipulating dislocation slip.

Thermodynamics-Guided High-Throughput Discovery of Eutectic High-Entropy Alloys for Rapid Solidification.

Excellent castability, significantly refined microstructure, and good mechanical properties make eutectic high-entropy alloys (EHEAs) a natural fit for rapid solidification processes, e.g., additive manufacturing. Previous investigations have focused on developing EHEAs through trial and error and mixing known binary eutectic materials. However, eutectic compositions obtained from near-equilibrium conditions do not guarantee a fully eutectic microstructure under rapid solidifications. In this work, a thermodynamically guided high-throughput framework is proposed to design EHEAs for rapid solidification. Empirical formulas derived from past experimental observations and thermodynamic computations are applied and considered phase growth kinetics under rapid solidification (skewed phase diagram). The designed alloy candidate, Co25.6Fe17.9Ni22.4Cr19.1Ta8.9Al6.1 (wt.%), contains nanostructured eutectic lamellar and shows a high Vickers hardness of 675 Hv. In addition to this specific composition, the alloy design toolbox enables the development of new EHEAs for rapid solidification without the limitation of previous knowledge.

Strong and ductile high temperature soft magnets through Widmanstätten precipitates.

Fast growth of sustainable energy production requires massive electrification of transport, industry and households, with electrical motors as key components. These need soft magnets with high saturation magnetization, mechanical strength, and thermal stability to operate efficiently and safely. Reconciling these properties in one material is challenging because thermally-stable microstructures for strength increase conflict with magnetic performance. Here, we present a material concept that combines thermal stability, soft magnetic response, and high mechanical strength. The strong and ductile soft ferromagnet is realized as a multicomponent alloy in which precipitates with a large aspect ratio form a Widmanstätten pattern. The material shows excellent magnetic and mechanical properties at high temperatures while the reference alloy with identical composition devoid of precipitates significantly loses its magnetization and strength at identical temperatures. The work provides a new avenue to develop soft magnets for high-temperature applications, enabling efficient use of sustainable electrical energy under harsh operating conditions.

Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography.

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.

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

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.

A mechanically strong and ductile soft magnet with extremely low coercivity.

Soft magnetic materials (SMMs) serve in electrical applications and sustainable energy supply, allowing magnetic flux variation in response to changes in applied magnetic field, at low energy loss1. The electrification of transport, households and manufacturing leads to an increase in energy consumption owing to hysteresis losses2. Therefore, minimizing coercivity, which scales these losses, is crucial3. Yet meeting this target alone is not enough: SMMs in electrical engines must withstand severe mechanical loads; that is, the alloys need high strength and ductility4. This is a fundamental design challenge, as most methods that enhance strength introduce stress fields that can pin magnetic domains, thus increasing coercivity and hysteresis losses5. Here we introduce an approach to overcome this dilemma. We have designed a Fe-Co-Ni-Ta-Al multicomponent alloy (MCA) with ferromagnetic matrix and paramagnetic coherent nanoparticles (about 91 nm in size and around 55% volume fraction). They impede dislocation motion, enhancing strength and ductility. Their small size, low coherency stress and small magnetostatic energy create an interaction volume below the magnetic domain wall width, leading to minimal domain wall pinning, thus maintaining the soft magnetic properties. The alloy has a tensile strength of 1,336 MPa at 54% tensile elongation, extremely low coercivity of 78 A m-1 (less than 1 Oe), moderate saturation magnetization of 100 A m2 kg-1 and high electrical resistivity of 103 µΩ cm.

Ultrastrong and Ductile Soft Magnetic High-Entropy Alloys via Coherent Ordered Nanoprecipitates.

The lack of strength and damage tolerance can limit the applications of conventional soft magnetic materials (SMMs), particularly in mechanically loaded functional devices. Therefore, strengthening and toughening of SMMs is critically important. However, conventional strengthening concepts usually significantly deteriorate soft magnetic properties, due to Bloch wall interactions with the defects used for hardening. Here a novel concept to overcome this dilemma is proposed, by developing bulk SMMs with excellent mechanical and attractive soft magnetic properties through coherent and ordered nanoprecipitates (<15 nm) dispersed homogeneously within a face-centered cubic matrix of a non-equiatomic CoFeNiTaAl high-entropy alloy (HEA). Compared to the alloy in precipitate-free state, the alloy variant with a large volume fraction (>42%) of nanoprecipitates achieves significantly enhanced strength (≈1526 MPa) at good ductility (≈15%), while the coercivity is only marginally increased (<10.7 Oe). The ordered nanoprecipitates and the resulting dynamic microband refinement in the matrix significantly strengthen the HEAs, while full coherency between the nanoprecipitates and the matrix leads at the same time to the desired insignificant pinning of the magnetic domain walls. The findings provide guidance for developing new high-performance materials with an excellent combination of mechanical and soft magnetic properties as needed for the electrification of transport and industry.