Materials Theory最新文献

Metal organic frameworks (MOFs) have emerged as promising electrode materials for supercapacitor (SC) due to their high surface areas, tunable porosity, and redox active sites. However, the vast chemical space of MOFs leads to millions of possible structures, makes experimental trial and error discovery inefficient. This review provides a focused perspective on how density functional theory (DFT) and machine learning (ML) are enabling the accelerated discovery and rational design of MOF-based SC electrodes. Key insights from DFT are discussed in relation to three critical performance descriptors as electrical conductivity, electrochemical and structural stability, and redox activity. In parallel, recent advances in ML-driven screening are reviewed, covering the development and use of large-scale MOF databases, descriptor engineering strategies, and predictive model architectures. Case studies demonstrating the successful integration of DFT and ML for identifying high-performance MOFs are highlighted in the review. Finally, the current limitations are analysed, including the discrepancy between idealized computational models and real polycrystalline electrodes, intrinsic trade-offs between conductivity and stability, and the need for interpretable and physics-informed ML models. Overall, this review outlines a computational roadmap for the rapid discovery and optimization of next-generation MOF-based SC electrodes.

A peridynamic model for polycrystalline materials is introduced to model the evolution of elastic-brittle damage under fatigue conditions. The peridynamic simulations for elastic deformations in a polycrystal are verified against corresponding Finite Element solutions for the synthetic microstructure René 88DT, an FCC polycrystalline Ni-based superalloy. The model is used to simulate the nucleation and propagation of brittle damage from fatigue loading. Damage nucleation locations obtained by the peridynamic model match those with high maximum Schmid factors for the given microstructure.

Many applied materials are cooled from high temperatures to a suitable temperature; however, their physical properties may be affected by different cooling conditions. In this study, a numerical simulation of the physical changes in multiphase materials during cooling was conducted. A three-dimensional physical model based on a typical multiphase material with a core-bonding structure synthesized from calcium, silicon, and iron oxides was constructed by serial sectioning technology. Next, to study the temperature and stress distribution of such materials under different cooling conditions and at different times, the Biot number (Bi) and Fourier number (Fo) were introduced. Furthermore, transient coupled thermomechanical calculations were performed to elucidate the mechanism of crack initiation. During cooling, the simulation results suggested that the temperature distribution of the material is affected mainly by geometric factors instead of the phase composition and that the rate of decrease in the temperature near the holes increases significantly. With respect to the stress distribution, the distribution of the maximum principal stress is affected mainly by the physical properties of the phase, and the stress gradient is concentrated at the interface of the phases, especially along the boundary between the bonding and core phases. In addition, the results revealed that the surface unit experienced the highest von Mises stress, followed by the radial midpoint unit and the center unit. Sensitivity analysis revealed that even with a 40% reduction in the elastic modulus or a 10% increase in the thermal expansion coefficient, the relative stress ranking (surface > radial midpoint > center) remained unchanged. These findings provide quantitative guidance for optimizing cooling process, particularly when slower initial cooling (800–500 °C) is recommended to reduce thermal gradients at the surface.

The discovery of materials with extreme lattice thermal conductivity (κ_L)—spanning from sub-air thermal insulators to metallic conductors that rival diamond—represents one of the most consequential frontiers in contemporary materials physics and engineering. This Perspective traces the evolution of thermal transport science from its empirical origins through the current AI-driven renaissance. We examine the limitations of conventional density-functional-theory-based phonon workflows, the emergence of universal machine learning interatomic potentials (uMLPs), graph-neural-network screening architectures such as the Crystal Attention Graph Neural Network (CATGNN), and generative inverse-design frameworks including the Physics-Guided Crystal Generative Model (PGCGM) and InvDesFlow-AL. Representative discovery and AI opportunities—including a computationally predicted room-temperature κ_L of 0.071 Wm⁻¹ K⁻¹ in Cs₂HgPtCl₆ and approximately 1,100 Wm⁻¹ K⁻¹ in metallic θ-TaN—are presented as concrete demonstrations of AI-driven computational power and human physical intuition, respectively. We conclude with a forward-looking discussion spanning five frontier challenges: machine learning frameworks for phonon–electron and phonon–ion interactions (with emphasis on electron–phonon coupling and its role in superconductivity, thermoelectrics, and carrier mobility); interpretable and symbolic AI approaches that extract closed-form physical principles from learned representations; out-of-distribution generalization strategies essential for discovering genuinely unprecedented materials; autonomous self-driving laboratory ecosystems that close the loop between computational prediction and experimental validation; and AI-accelerated design of interfacial thermal transport for high-power electronics and data center thermal management.

In the current study, the mathematical analogy between the Johnson–Mehl–Avrami-Kolmogorov equation and the equations describing density, the effective cross-sectional area and porosity evolution in compaction of metallic powders is presented. In addition, the density evolution in the early stages due to particle arrangement and elastic deformation are considered. The study provides an insight to the average (mean field) evolution of the regions occupied by the particles by demonstrating the mathematical analogy to growth and impingement of phase regions in solid state phase transformations. In a sense, the growth and impingement of the cross-sectional areas of particles can be interpreted as one phase (the solid particle) growing into another phase (the pore region). In addition to presenting the analogy and the interpretation, a slight geometrically based fine-tuning to the electrical resistivity equation is developed, which shows improvement in fitting when comparing to previously presented equation.

Ductile fracture under bending is governed by interactions among length scales associated with the imposed deformation field, material microstructure, and defects. Here, we use dual-phase advanced high-strength steels as a model system to examine these interactions through large-scale, microstructure-based finite element calculations. The dual-phase microstructure is modeled explicitly in the near-surface region of bend specimens. The ferrite/martensite feature size and specimen thickness are systematically varied to examine structural–microstructural length-scale interactions. Non-metallic inclusions are introduced to examine defect–microstructural length-scale interactions. The results show that microstructural refinement delays strain localization and crack nucleation, improving bendability for a fixed structural length scale. However, the extent of improvement depends on sheet thickness. The calculations further show that inclusions become especially detrimental when their size exceeds the microstructural length scale. Thus, bendability is governed not by a single material or geometric length scale, but by the coupled effects of specimen geometry, microstructure, and defect length scales. The findings have implications for designing high-strength multiphase sheet materials, where microstructural refinement is often pursued to increase strength and enable thickness reduction.

Using density functional theory, this analysis investigated the multifunctional properties of halide double perovskite compounds Rb₂AgXCl₆ (X = Ti, Sb, and In). While elastic constants, Pugh's ratio, and Poisson's ratio showed mechanical robustness and ductility, geometrical relaxation confirmed their cubic phase stability. Band gaps between 1.84 and 2.18 eV were found by analyzing the electronic band structure, placing them in the visible spectrum. Absorption spectra, dielectric response, conductivity, reflectance, and refractive index were among the optoelectronic characteristics that showed strong absorption, low reflectance, and minimal energy dissipation. This optical profile highlights their intrinsic suitability as active absorber layers within photovoltaic cells and photodetectors, as benchmarked against established perovskite standards. The direct-bandgap indium analogue emerges as highly promising for light-emitting applications. Additionally, effective p-type conduction and promising energy conversion efficiency were highlighted by the thermoelectric transport parameters, which were assessed using the Seebeck coefficient, the electronic power factor, and the figure of merit. In conclusion, the results show that Rb₂AgXCl₆ compounds combine adjustable optoelectronic characteristics, structural integrity, and potential thermoelectric performance, making them attractive options for advanced optoelectronic technologies and sustainable energy solutions.

The Variational Crack Element Method (VCEM) (Ghoniem 2026a) is a constrained energy-minimization framework that represents cracks through distributed Burgers-density fields, preserving the exact crack-tip singular structure while enabling scalable computation for interacting, branching, and kinked geometries. We establish the numerical fidelity of VCEM through systematic comparison with analytical solutions: straight cracks under mode-I, mode-II, and mixed-mode biaxial loading; circular-arc cracks; and kinked and branched configurations for which complex-variable solutions are available. Stress intensity factors, crack-tip fields, and displacement discontinuities are reproduced with high accuracy across all benchmarks. The method is then applied to the diametrically compressed (Brazilian) disk, a finite-domain problem with strongly non-uniform stress fields. Centerline cracks under force control reproduce the classical splitting–arrest sequence as the tips enter the compressive contact zones; inclined cracks reorient toward the maximum-opening direction with kink angles in quantitative agreement with experiment. Finally, simulations of evolving multi-crack networks demonstrate that fracture under force control can be interpreted as a connectivity phase transition, in which the applied load is the control parameter and the crack-network connectivity is the order parameter, driving the disk from an intact to a fragmented state through unstable growth, junction formation, and branching.

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

This article provides a perspective on a novel parametrically upscaled constitutive and damage modeling (PUCM/PUCDM) platform for multiscale modeling of fatigue and failure in metallic and composite structures. It integrates physics and thermodynamics-based modeling with machine learning, integrated computational materials engineering (ICME), temporal acceleration, and uncertainty quantification in a unified platform development. The platform enables uncertainty-quantified efficient scale bridging with explicit representation of lower-scale descriptors (RAMPs) in higher-scale response functions. The PUCM/PUCDMs are constitutive relations that can be readily incorporated in UMAT-type user windows of commercial finite element software for easy translation to a broader community. This is a significant benefit, which can provide the user community with a pathway for highly efficient computations without compromising the fidelity of very complex mechanisms at multiple scales. The embedding of machine learning-generated functions of RAMPs in the constitutive parameters is a unique advantage. It naturally renders them amenable to location-specific material design by coupling with design algorithms. Additionally, through strategic integration with emerging deep learning methods, the platform creates virtual damage sensing digital twins that interface with actual surface sensor measurements for efficient prediction of full-field (both surface and subsurface) temporally evolving deformation and damage variables. This facilitates robust structural health monitoring capabilities.