Cold-drawn pearlite wire is widely used in industry due to its exceptional high strength. Understanding the deformation mechanisms during the cold-drawing process of pearlite, particularly the deformation and decomposition of cementite, is of great significance. In this study, a bicrystal model tailored to lamellar structures is developed to calculate the elastic properties and stress concentration of pearlite. By analyzing slip activation in both ferrite and cementite, along with the yield strength, we reveal the significant influence of loading direction on pearlite deformability. Notably, the yield strength varies from 9.5 GPa to 17.0 GPa. Under specific loading conditions, plastic deformation is observed to initiate in cementite, challenging the conventional assumption that slip bands always originate in ferrite. Furthermore, factors that influence the plastic deformation of pearlite are discussed. A successive strengthening mechanism is proposed to explain the excellent deformability and high strength of pearlite after extensive deformation. This work introduces a novel method for directional loading of lamellar structures. The surprising finding that plastic deformation, without fracture, can initiate in cementite, might offer directions for developing other structural materials with extreme tensile strength and deformability.

The mechanical strength of sintered nanoparticles (NPs) limits their application in advanced electronics packaging. In this study, we explore the anisotropy in the microstructure and mechanical properties of sintered copper (Cu) NPs by combining experimental techniques with molecular dynamics (MD) simulations. We establish a clear relationship between processing conditions, microstructural evolution, and resulting properties in pressure-assisted sintering of Cu NPs. Our findings reveal that pressure-assisted sintering induces significant anisotropy in the microstructure, as evidenced by variations in areal relative density and the orientation distribution of necks formed during sintering. Specifically, along the direction of applied pressure, the microstructure exhibits reduced variation in areal relative density and a higher prevalence of necks with favorable orientations. The resulting anisotropic mechanical properties, with significantly higher strength along the pressure direction compared to other directions, are demonstrated through micro-cantilever bending tests and tensile simulations. This anisotropy is further explained by the combined effects of strain localization (influenced by areal relative density) and the failure modes of necks (determined by their orientation relative to the loading direction). This work provides valuable insights into the analysis of sintered NPs microstructures and offers guidance for optimizing the sintering process.

Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.

Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). These models offer broad applicability across the periodic table, achieving first-principles accuracy at a fraction of the computational cost of traditional DFT calculations. In this study, we demonstrate that state-of-the-art pretrained uMLIPs can effectively replace DFT for accurately modeling complex defects in a wide range of metals and alloys. Our investigation spans diverse scenarios, including grain boundaries and general defects in pure metals, defects in high-entropy alloys, hydrogen-alloy interactions, and solute-defect interactions. Remarkably, the latest EquiformerV2 models achieve DFT-level accuracy on comprehensive defect datasets, with root mean square errors below 5 meV atom−1 for energies and 100 meV Å−1 for forces, outperforming specialized machine learning potentials such as moment tensor potential and atomic cluster expansion. We also present a systematic analysis of accuracy versus computational cost and explore uncertainty quantification for uMLIPs. A detailed case study of tungsten (W) demonstrates that data on pure W alone is insufficient for modeling complex defects in uMLIPs, underscoring the critical importance of advanced machine learning architectures and diverse datasets, which include over 100 million structures spanning all elements. These findings establish uMLIPs as a robust alternative to DFT and a transformative tool for accelerating the discovery and design of high-performance materials.

This study presents a dual approach combining molecular dynamics simulations and experimental analysis to explore the sintering behavior of copper (Cu) nanoparticles. Our simulation model comprises 240 nanoparticles, through which we systematically examine the coalescence kinetics during the sintering process. The simulations provide a detailed view of the particle interactions, structural evolution, and the mechanisms driving nanoparticle fusion at the atomic level. Complementing the simulations, we conducted 3D reconstructions using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) to characterize the microstructure of the sintered nanoparticles. This hybrid approach not only deepens our understanding of the fundamental processes governing the sintering of Cu nanoparticles but also bridges the gap between theoretical predictions and experimental observations, offering insights into the optimization of sintering processes in practical applications.

In a material under stress, grain boundaries may give rise to stress discontinuities. The stress state at grain boundaries strongly affects microscopic processes, such as diffusion and segregation, as well as failure initiation, such as fatigue, creep, and corrosion. Here the general condition of incompatibility stress at grain boundaries is studied with a bicrystal model for linear elastic materials. In materials with cubic crystal structures, it is proven that hydrostatic stress does not lead to a stress discontinuity at grain boundaries. For bicrystals with inclined grain boundaries under uniaxial stress, the extreme values of the incompatibility stress as a function of the inclination angle are obtained by a simulated annealing method. A simple criterion is proposed to classify cubic materials into three groups. For cubic crystals with at most moderate anisotropy, the highest incompatibility stress occurs when the grain boundary plane is perpendicular to the uniaxial stress. For highly anisotropic materials, such as alkali metals and polymorphic high-temperature phases, the highest incompatibility stress occurs on grain boundaries with an inclination of about 47^o.

In a material under stress, grain boundaries may give rise to stress discontinuities. Stress localization is crucial to materials' behavior such as segregation, precipitation, and void nucleation. Here, the stress state at a grain boundary perpendicular to a uniaxial external stress is studied systematically. The grain boundary with the most extreme stress discontinuity is determined for cubic materials within the elastic limit for a bicrystal model. Additionally, grain boundaries with negligible stress discontinuity are identified. The influence of the elastic tensor components, C₁₁, C₁₂, and C₄₄, and grain orientation is studied quantitatively.

Superelastic metallic materials possessing large recoverable strains are widely used in automotive, aerospace and energy conversion industries. Superelastic materials working at high temperatures and with a wide temperature range are increasingly required for demanding applications. Until recently, high-temperature superelasticity has only been achievable with multicomponent alloys fabricated by complex processes. In this study, a novel framework of multi-scale models enabling texture and microstructure design is proposed for high-performance NiTi fabrication via laser powder bed fusion. Based on the developed framework, a Ni-lean Ni(49.4 at.%)-Ti alloy is, for the first time, endowed with a 4% high-temperature compressive superelasticity. A 001 texture, unfavorable for plastic slip, is created to realize enhanced functionality. The unprecedented superelasticity can be maintained up to 453 K, which is comparable with but has a wider superelastic temperature range (∼110 K) than rare earth alloyed NiTi alloys, previously only realizable with grain refinement, and other complicated post-processing operations. At the same time, its shape memory stability is also improved due to existing textured 100 martensite and intergranular precipitation of Ti₂NiOx. This discovery reframes the way that we design superior performance NiTi based alloys through directly tailoring crystallographic orientations during additive manufacturing.