Machine learning interatomic potentials (MLIPs) enable accurate atomistic modeling, but reliable uncertainty quantification (UQ) remains elusive. In this study, we investigate two UQ strategies, ensemble learning and D-optimality, within the atomic cluster expansion framework. It is revealed that higher model accuracy strengthens the correlation between predicted uncertainties and actual errors and improves novelty detection, with D-optimality yielding more conservative estimates. Both methods deliver well calibrated uncertainties on homogeneous training sets, yet they underpredict errors and exhibit reduced novelty sensitivity on heterogeneous datasets. To address this limitation, we introduce clustering enhanced local D-optimality, which partitions configuration space into clusters during training and applies D-optimality within each cluster. This approach substantially improves the detection of novel atomic environments in heterogeneous datasets. Our findings clarify the roles of model fidelity and data heterogeneity in UQ performance and provide a practical route to robust active learning and adaptive sampling strategies for MLIP development.

Universal machine-learning interatomic potentials (uMLIPs) are emerging as foundation models for atomistic simulation, offering near-ab initio accuracy at far lower cost. Their safe, broad deployment is limited by the absence of reliable, general uncertainty estimates. We present a unified, scalable uncertainty metric, U, built from a heterogeneous ensemble that reuses existing pretrained MLIPs. Across diverse chemistries and structures, U strongly tracks true prediction errors and robustly ranks configuration-level risk. Using U, we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels: for tungsten, we match full density-functional-theory (DFT) training using 4% of the DFT data; for MoNbTaW, a dataset distilled by U supports high-accuracy potential training. By filtering numerical label noise, the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data. This framework provides a practical reliability monitor and guides data selection and fine-tuning, enabling cost-efficient, accurate, and safer deployment of foundation models.

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.

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.

This study investigates the microstructure evolution and mechanical behavior of bimodal-sized sintered copper (Cu) nanoparticles (NPs) under varying sintering pressures. Micro-pillar compression tests reveal a transition from collapse-dominated to compaction-driven deformation as sintering pressure increases. Transmission electron microscopy (TEM) and transmission Kikuchi diffraction (TKD) analyses identify a two-stage deformation mechanism—initial pore compaction followed by intragranular slip—fundamentally distinct from bulk Cu. Molecular dynamics (MD) simulations further reveal that large particles promote dislocation-mediated plasticity by accommodating intragranular slip, while small particles enhance load transfer through localized shear-compaction, together enabling uniform strain distribution and supporting the experimentally observed strain accommodation. The resulting microstructure achieves a combination of high yield strength (up to 320 MPa) and low elastic modulus (20 GPa), offering a compliant yet robust response. These findings elucidate a unique processing–structure–property relationship and provide a rational basis for designing porous metal interconnects capable of withstanding thermomechanical stresses in advanced electronic packaging.

Metallic materials exhibit structural and performance anisotropy at various scales, including the crystal structure, microstructure, and bulk levels. The anisotropy influences how stress and strain distribute within the material. The localized stress concentration is closely related to deformation and several failure mechanisms. A systematic exploration of the anisotropy can deepen our understanding of stress concentration and material failure, and guide the design of high-performance materials tailored for specific applications. Additionally, this work supports sustainable development by enhancing the service life and recyclability of metallic materials—an increasingly critical priority in materials science.....

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.

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.

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.

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.