Metals strengthen with increasing temperature at extreme strain rates.

The strength of materials depends on the rate at which they are tested, as defects, for example dislocations, that move in response to applied strains have intrinsic kinetic limitations1-4. As the deformation strain rate increases, more strengthening mechanisms become active and increase the strength4-7. However, the regime in which this transition happens has been difficult to access with traditional micromechanical strength measurements. Here, with microballistic impact testing at strain rates greater than 106 s-1, and without shock conflation, we show that the strength of copper increases by about 30% for a 157 °C increase in temperature, an effect also observed in pure titanium and gold. This effect is counterintuitive, as almost all materials soften when heated under normal conditions. This anomalous thermal strengthening across several pure metals is the result of a change in the controlling deformation mechanism from thermally activated strengthening to ballistic transport of dislocations, which experience drag through phonon interactions1,8-10. These results point to a pathway to better model and predict materials properties under various extreme strain rate conditions, from high-speed manufacturing operations11 to hypersonic transport12.

Low-hysteresis shape-memory ceramics designed by multimode modelling.

Zirconia ceramics exhibit a martensitic phase transformation that enables large strains of order 10%, making them prospects for shape-memory and superelastic applications at high temperature1-5. Similarly to other martensitic materials, this transformation strain can be engineered by carefully alloying to produce a more commensurate transformation with reduced hysteresis (difference in transformation temperature on heating and cooling)6-11. However, such 'lattice engineering' in zirconia is complicated by additional physical constraints: there is a secondary need to manage a large transformation volume change12, and to achieve transformation temperatures high enough to avoid kinetic barriers6. Here we present a method of augmenting the lattice engineering approach to martensite design to address these additional constraints, incorporating modern computational thermodynamics and data science tools to span complex multicomponent spaces for which no data yet exist. The result is a new zirconia composition with record low hysteresis of 15 K, which is about ten times less transformation hysteresis compared to typical values (and approximately five times less than the best values reported so far). This finding demonstrates that zirconia ceramics can exhibit hysteresis values of the order of those of widely deployed shape-memory alloys, paving the way for their use as viable high-temperature shape-memory materials.

Nanotwinning-assisted dynamic recrystallization at high strains and strain rates.

Grain refinement is a widely sought-after feature of many metal production processes and frequently involves a process of recrystallization. Some processing methods use very high strain rates and high strains to refine the grain structure into the nanocrystalline regime. However, grain refinement processes are not clear in these extreme conditions, which are hard to study systematically. Here, we access those extreme conditions of strain and strain rate using single copper microparticle impact events with a laser-induced particle impact tester. Using a combined dictionary-indexing electron backscatter diffraction and scanning transmission electron microscopy approach for postmortem characterization of impact sites, we systematically explore increasing strain levels and observe a recrystallization process that is facilitated by nanotwinning, which we term nanotwinning-assisted dynamic recrystallization. It achieves much finer grain sizes than established modes of recrystallization and therefore provides a pathway to the finest nanocrystalline grain sizes through extreme straining processes.

Learning Grain-Boundary Segregation: From First Principles to Polycrystals.

The segregation of solute atoms at grain boundaries (GBs) can strongly impact the structural and functional properties of polycrystals. Yet, due to the limited availability of simulation tools to study polycrystals at the atomistic scale (i.e., interatomic potentials), there is a minimal understanding of the variation of solute segregation tendencies across the very complex space of GB microenvironments and the large range of alloys in which it can occur. Here, we develop an algorithmic framework that can directly learn the full spectrum of segregation energies for a metal solute atom in a metal polycrystal from ab initio methods, bypassing the need for alloy interatomic potentials. This framework offers a pathway to a comprehensive catalog of GB solute segregation with quantum accuracy, for the entire alloy space. As an initial demonstration in this pursuit, we build an extensive GB segregation database for aluminum-based alloys across the periodic table, including dozens of alloys for which there are substantially no prior data.

Direct Electric-Field Induced Phase Transformation in Paraelectric Zirconia via Electrical Susceptibility Mismatch.

Electric field driven phase transformations require two phases with a mismatch in their electric polarization, as seen in antiferroelectric-to-ferroelectric transformations, where the ferroelectric phase has a permanent polarization that is favored under field. Many other nonferroelectric dielectric materials can become electrically polarized according to their electrical susceptibility, yet such induced polarizations are not generally considered capable of enabling a phase transformation. Here we explore a susceptibility-mismatch phase transformation in a paraelectric ceramic, yttria-doped zirconia. Using in situ x-ray diffraction at 550 °C we show that the monoclinic-to-tetragonal transformation can be driven directly by an electric field, providing experimental evidence of a paraelectric-to-paraelectric phase transformation. Considering the ∼1% mechanical strain of this transformation, the resulting electromechanical coupling may have potential for solid-state electrical actuators.

Higher Temperatures Yield Smaller Grains in a Thermally Stable Phase-Transforming Nanocrystalline Alloy.

Grains in crystalline materials usually grow with increased thermal exposure. Classical phenomena such as recrystallization may lead to a purely temporary decrease in the grain size, while recent advances in alloy design can yield thermally stable nanocrystalline materials in which grain growth stagnates. But grains never shrink, since there is a lack of interface-generating mechanisms at high temperatures, which are required to decrease the grain size if such was the system's thermodynamic tendency. Here we sidestep this paradigm by designing a nanocrystalline alloy having an allotropic phase transformation-an interface-generating mechanism-such that only the high-temperature phase is stabilized against grain growth. We demonstrate that for an Fe-Au alloy cycled through the α↔γ transformation, the high-temperature phase (γ-Fe) has a stable fine grain size, smaller than its low-temperature counterpart (α-Fe). The result is an unusual material in which an increase in temperature leads to finer grains that are stable in size.

Melt-driven mechanochemical phase transformations in moderately exothermic powder mixtures.

Usually, mechanochemical reactions between solid phases are either gradual (by deformation-induced mixing), or self-propagating (by exothermic chemical reaction). Here, by means of a systematic kinetic analysis of the Bi-Te system reacting to Bi2Te3, we establish a third possibility: if one or more of the powder reactants has a low melting point and low thermal effusivity, it is possible that local melting can occur from deformation-induced heating. The presence of hot liquid then triggers chemical mixing locally. The molten events are constrained to individual particles, making them distinct from self-propagating reactions, and occur much faster than conventional gradual reactions. We show that the mechanism is applicable to a broad variety of materials systems, many of which have important functional properties. This mechanistic picture offers a new perspective as compared to conventional, gradual mechanochemical synthesis, where thermal effects are generally ignored.

Melting Can Hinder Impact-Induced Adhesion.

Melting has long been used to join metallic materials, from welding to selective laser melting in additive manufacturing. In the same school of thought, localized melting has been generally perceived as an advantage, if not the main mechanism, for the adhesion of metallic microparticles to substrates during a supersonic impact. Here, we conduct the first in situ supersonic impact observations of individual metallic microparticles aimed at the explicit study of melting effects. Counterintuitively, we find that under at least some conditions melting is disadvantageous and hinders impact-induced adhesion. In the parameter space explored, i.e., ∼10 µm particle size and ∼1 km/s particle velocity, we argue that the solidification time is much longer than the residence time of the particle on the substrate, so that resolidification cannot be a significant factor in adhesion.

Effects of surface diffusion on high temperature selective emitters.

Using morphological and optical simulations of 1D tantalum photonic crystals at 1200K, surface diffusion was determined to gradually reduce the efficiency of selective emitters. This was attributed to shifting resonance peaks and declining emissivity caused by changes to the cavity dimensions and the aperture width. Decreasing the structure's curvature through larger periods and smaller cavity widths, as well as generating smoother transitions in curvature through the introduction of rounded cavities, was found to alleviate this degradation. An optimized structure, that shows both high efficiency selective emissivity and resistance to surface diffusion, was presented.

Multicomponent alloys designed to sinter.

Powder sintering is a low-energy, net-shape processing route for many new products in the additive manufacturing space. We advance the viewpoint that for future manufacturing, alloys should be designed from materials science principles to sinter quickly at lower temperatures and with controlled final microstructures. Specifically, we illustrate the computational design of multinary Ni-base alloys, whose chemistries permit a low-temperature solid-state sintering scheme without any pressure- or field-assistance, as well as heat-treatability after sintering. The strategy is based on sequential phase evolutions designed to occur during sintering. The reactions involve rapid reorganization of matter to full density in cycles up to just 1200 °C, while conventional Ni alloys sintered in the solid-state require about ten times longer, or more than 250 °C degrees higher temperature. Our approach yields an alloy that benefits from precipitation hardening, has an increased strength ~ 50% higher than solid-state processed commercial Ni alloys, and yet exhibits extensive plasticity beyond 35% uniaxial strain. The results point to a generalizable design scheme for many other alloys designed for solid-state powder processing that can enable greater value from additive manufacturing.

Microparticle Impact Testing at High Precision, Higher Temperatures, and with Lithographically Patterned Projectiles.

In the first decade of high-velocity microparticle impact research, hardly any modification of the original experimental setup has been necessary. However, future avenues for the field require advancements of the experimental method to expand both the impact variables that can be quantitatively assessed and the materials and phenomena that can be studied. This work explores new design concepts for the launch pad (the assembly that launches microparticles upon laser ablation) that can address the root causes of many experimental challenges that may limit the technique in the future. Among the design changes contemplated, the substitution of a stiff glass launch layer for the standard elastomeric polymer layer offers a number of improvements. First, it facilitates a reduction of the gap between launch pad and target from hundreds to tens of micrometers and thus unlocks a reproducibility in targeting a specific impact location better than the diameter of the test particle itself (±1.75 µm for SiO2 particles 7.38 µm in diameter). Second, the inert glass surface enables experiments at higher temperatures than previously possible. Finally-as demonstrated by the launch of thin-film Au disks-a launch pad made of materials standard in microfabrication paves the way to facile microfabrication of advanced impactors.

Atomistic simulation of slow grain boundary motion.

Existing atomistic simulation techniques to study grain boundary motion are usually limited to either high velocities or temperatures and are difficult to compare to realistic experimental conditions. Here we introduce an adapted simulation method that can access boundary velocities in the experimental range and extract mobilities in the zero driving force limit at temperatures as low as ∼0.2T(m) (T(m) is the melting point). The method reveals three mechanistic regimes of boundary mobility at zero net velocity depending on the system temperature.

Nonlocal superelastic model of size-dependent hardening and dissipation in single crystal Cu-Al-Ni shape memory alloys.

We propose a nonlocal continuum model to describe the size-dependent superelastic effect observed in recent experiments of single crystal Cu-Al-Ni shape memory alloys. The model introduces two length scales, one in the free energy and one in the dissipation, which account for the size-dependent hardening and dissipation in the loading and unloading response of micro- and nanopillars subject to compression tests. The information provided by the model suggests that the size dependence observed in the dissipation is likely to be associated with a nonuniform evolution of the distribution of the austenitic and martensitic phases during the loading cycle.

Atomistic Assessment of Solute-Solute Interactions during Grain Boundary Segregation.

Grain boundary solute segregation is becoming increasingly common as a means of stabilizing nanocrystalline alloys. Thermodynamic models for grain boundary segregation have recently revealed the need for spectral information, i.e., the full distribution of environments available at the grain boundary during segregation, in order to capture the essential physics of the problem for complex systems like nanocrystalline materials. However, there has been only one proposed method of extending spectral segregation models beyond the dilute limit, and it is based on simple, fitted parameters that are not atomistically informed. In this work, we present a physically motived atomistic method to measure the full distribution of solute-solute interaction energies at the grain boundaries in a polycrystalline environment. We then cast the results into a simple thermodynamic model, analyze the Al(Mg) system as a case study, and demonstrate strong agreement with physically rigorous hybrid Monte Carlo/molecular statics simulations. This approach provides a means of rapidly measuring key interactions for non-dilute grain boundary segregation for any system with an interatomic potential.

Learning grain boundary segregation energy spectra in polycrystals.

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency-quantified by the segregation enthalpy spectrum-of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

Global optimization for accurate determination of EBSD pattern centers.

Accurate pattern center determination has long been a challenge for the electron backscatter diffraction (EBSD) community and is becoming critically accuracy-limiting for more recent advanced EBSD techniques. Here, we study the parameter landscape over which a pattern center must be fitted in quantitative detail and reveal that it is both "sloppy" and noisy, which limits the accuracy to which pattern centers can be determined. To locate the global optimum in this challenging landscape, we propose a combination of two approaches: the use of a global search algorithm and averaging the results from multiple patterns. We demonstrate the ability to accurately determine pattern centers of simulated patterns, inclusive of effects of binning and noise on the error of the fitted pattern center. We also demonstrate the ability of this method to accurately detect changes in pattern center in an experimental dataset with noisy and highly binned patterns. Source code for our pattern center fitting algorithm is available online.

Melt-driven erosion in microparticle impact.

Impact-induced erosion is the ablation of matter caused by being physically struck by another object. While this phenomenon is known, it is empirically challenging to study mechanistically because of the short timescales and small length scales involved. Here, we resolve supersonic impact erosion in situ with micrometer- and nanosecond-level spatiotemporal resolution. We show, in real time, how metallic microparticles (~10-µm) cross from the regimes of rebound and bonding to the more extreme regime that involves erosion. We find that erosion in normal impact of ductile metallic materials is melt-driven, and establish a mechanistic framework to predict the erosion velocity.

Achieving Ultralow Wear with Stable Nanocrystalline Metals.

Recent work suggests that thermally stable nanocrystallinity in metals is achievable in several binary alloys by modifying grain boundary energies via solute segregation. The remarkable thermal stability of these alloys has been demonstrated in recent reports, with many alloys exhibiting negligible grain growth during prolonged exposure to near-melting temperatures. Pt-Au, a proposed stable alloy consisting of two noble metals, is shown to exhibit extraordinary resistance to wear. Ultralow wear rates, less than a monolayer of material removed per sliding pass, are measured for Pt-Au thin films at a maximum Hertz contact stress of up to 1.1 GPa. This is the first instance of an all-metallic material exhibiting a specific wear rate on the order of 10-9 mm3 N-1 m-1 , comparable to diamond-like carbon (DLC) and sapphire. Remarkably, the wear rate of sapphire and silicon nitride probes used in wear experiments are either higher or comparable to that of the Pt-Au alloy, despite the substantially higher hardness of the ceramic probe materials. High-resolution microscopy shows negligible surface microstructural evolution in the wear tracks after 100k sliding passes. Mitigation of fatigue-driven delamination enables a transition to wear by atomic attrition, a regime previously limited to highly wear-resistant materials such as DLC.

Correlated grain-boundary distributions in two-dimensional networks.

In polycrystals, there are spatial correlations in grain-boundary species, even in the absence of correlations in the grain orientations, due to the need for crystallographic consistency among misorientations. Although this consistency requirement substantially influences the connectivity of grain-boundary networks, the nature of the resulting correlations are generally only appreciated in an empirical sense. Here a rigorous treatment of this problem is presented for a model two-dimensional polycrystal with uncorrelated grain orientations or, equivalently, a cross section through a three-dimensional polycrystal in which each grain shares a common crystallographic direction normal to the plane of the network. The distribution of misorientations theta, boundary inclinations phi and the joint distribution of misorientations about a triple junction are derived for arbitrary crystal symmetry and orientation distribution functions of the grains. From these, general analytical solutions for the fraction of low-angle boundaries and the triple-junction distributions within the same subset of systems are found. The results agree with existing analysis of a few specific cases in the literature but present a significant generalization.

Correlation-space description of the percolation transition in composite microstructures.

We explore the percolation threshold shift as short-range correlations are introduced and systematically varied in binary composites. Two complementary representations of the correlations are developed in terms of the distribution of phase bonds or, alternatively, using a set of appropriate short-range order parameters. In either case, systematic exploration of the correlation space reveals a boundary that separates percolating from nonpercolating structures and permits empirical equations that identify the location of the threshold for systems of arbitrary short-range correlation states. Two- and three-dimensional site lattices with two-body correlations, as well as a two-dimensional hexagonal bond network with three-body correlations, are explored. The approach presented here should be generalizable to more complex correlation states, including higher-order and longer-range correlations.