Magnesium (Mg) has the lowest density of all structural metals and has excellent potential for wide use in structural applications. While pure Mg has inferior mechanical properties; the addition of further elements at various concentrations has produced alloys with enhanced mechanical performance and corrosion resistance. An important consequence of adding such elements is that the saturated Mg matrix can locally decompose to form solute clusters and intermetallic particles, often referred to as precipitates. Controlling the shape, number density, volume fraction, and spatial distribution of solute clusters and precipitates significantly impacts the alloy’s plastic response. Conversely, plastic deformation during thermomechanical processing can dramatically impact solute clustering and precipitation. In this paper, we first discuss how solute atoms, solute clusters, and precipitates can improve the mechanical properties of Mg alloys. We do so by primarily comparing three alloy systems: Mg–Al, Mg–Zn, and Mg–Y-based alloys. In the second part, we provide strategies for optimizing such microstructures by controlling nucleation and growth of solute clusters and precipitates during thermomechanical processing. Toward the end, we briefly highlight how one can enable inverse design of Mg alloys by a more robust Integrated Computational Materials Design (ICMD) approach.

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

Magnetically-controlled and elastically-coupled multistable pendula are shown to serve as versatile structural analogs of ferroelectric crystals, mimicking atomic-level phenomena of domain patterning, domain nucleation, and Allen–Cahn-type domain wall motion under an applied bias, as found, e.g., in ferroelectric switching. We demonstrate the quantitative analogy with material-level transitions via a homogenized continuum description, including structural-level realizations of temperature and lattice defects. Existing photonic, phononic, and topological metamaterials are thus complemented by a new mechanical analog of the nonlinear dissipative kinetics of structural transformations.

We study the influence of temperature on the interaction of nano-pores with ferroelectric domain walls in bulk single-crystalline lead zirco- nate titanate as a function of pore size and concentration. Using a density functional theory-informed finite-temperature phase-field model, we determine the electric field required to unpin 180 -domain walls from a periodic array of pores, thus gaining insight into the effect of tem- perature on domain wall kinetics in ferroelectrics with good qualitative agreement between simulated results and experimental measurements.