Materials Theory最新文献

We present a detailed analysis of the universal relationship between grain boundary (GB) free energy and GB self-diffusion coefficient derived by Borisov et al. (1964). This relationship was expressed by a simple equation that was used in many publications to predict GB energies on the basis of experimental diffusion data. Meanwhile, the physical assumptions and approximations underlying the Borisov model are poorly understood. As a result, the Borisov equation was often used outside its intended limits. Here, we re-derive the Borisov equation from ground up, identifying its underlying assumptions, correcting some errors and inconsistencies, and extending the original model to the case of impurity diffusion and diffusion by the interstitialcy and interstitial-dumbbell mechanisms. The meaning of the key assumption of the Borisov model, related to the free energy of the activated complex, is discussed, and ways to test this assumption are proposed.

The overarching goal of this work is to develop a predictive model of fracture resistance in a context of epistemic uncertainty due to analytically intractable mechanisms in metallic materials. The uncertainty associated with the spatial distribution of defects, such as voids, and its conflicting effects on setting ductility limits has hindered robust predictions for decades. An immediate objective is to explore the capabilities of carefully selected machine learning methods in uncovering fundamental evolution laws for the spatial distribution of defects, such as voids, under the finite deformations of ductile metals. The approach begins with a simple, yet rigorous classification scheme of void dispersions into ordered, random or clustered. Mathematical arguments are well established for deriving evolution equations of the spatial distribution of voids for both random and ordered distributions. Random dispersions possess the fundamental property of persistent isotropy. Ordered dispersions are characterized with maximum induced anisotropy. State of the art modeling frameworks assume one or the other (random versus ordered). However, real dispersions are clustered due to manufacturing methods. Knowledge of evolution laws for clustered dispersions is critically lacking and is analytically intractable. To address this challenge, machine learning methods are employed in conjunction with surrogate models to supply the data needed for training and testing of the models.

The rapid expansion of lithium-ion batteries (LIBs) in electric vehicles, portable electronics, and large-scale energy-storage systems has intensified concerns regarding critical material depletion, environmental impacts, and end-of-life battery management. Although conventional recycling technologies, including pyrometallurgical, hydrometallurgical, and direct recycling approaches, have enabled valuable resource recovery, their broader implementation remains challenged by heterogeneous battery chemistries and complex degradation. Artificial intelligence (AI) has emerged as a powerful tool to transform LIB recycling by enabling intelligent identification, automated sorting, predictive modelling, and adaptive process optimization. This mini-review highlights recent advances in AI-driven LIB recycling, focusing on the integration of machine learning, deep learning, and computer vision across the battery recycling value chain. The roles of AI in battery classification, recycling-route selection, material recovery prediction, and second-life assessment are critically discussed along with future perspectives toward developing autonomous and data-driven recycling ecosystems that integrate advanced materials science with artificial intelligence to accelerate the transition from conventional end-of-life battery management and recycling practices toward intelligent circular battery systems.

In this perspective, I discuss why I do atomistic simulations. Atomistic simulations have now been a key component of materials science research for the better part of a century. They provide a direct window into the individual motion of atoms, revealing dynamical behavior that is impossible to see any other way. The level of detail is immense. However, not all of that detail can matter at the meso- and macroscale where engineering models perform quite well in describing materials. In my view, the thrill of discovery, of seeing something unexpected for the first time, is one of the key reasons for performing atomistic simulations. And, while many novel results may not immediately translate to engineering-level models, in many cases they reveal new possibilities that change how we think about a problem. The novel and unexpected behavior revealed by these simulations are particularly important when considering materials out of equilibrium, where kinetic processes dictate the historical evolution of the material.

The evaluation of 3D fracture driving forces under finite strain regimes is crucial for damage tolerance assessments of critical engineering structures. This technical note compares the performance and limitations of two complementary methodologies integrated within updated Lagrangian corotational solvers: the Eshelby-based domain integral method (formally equivalent to the (G-theta) formulation) and a direct boundary kinematic extraction approach. We demonstrate that while the continuous Eshelby-based domain integral theoretically preserves path-independence, its discrete finite element implementation struggles to maintain a generic workflow across all strain regimes. Under large-rotation, small-strain conditions, reconstructing the deformation gradient from nodal displacements is vulnerable to single-precision floating-point cancellation. Conversely, under large-strain, crack-tip blunting regimes, the domain integral is severely corrupted by volumetric locking on linear elements, in contrast to standard small-strain J-integral approximations using these elements. These limitations are discussed alongside a direct surface kinematic extraction method regularized via a second-order Total Variation (ADMM-TV2) scheme. Operating strictly on boundary displacement jumps, this kinematic approach offers an efficient, low-cost alternative that avoids volumetric reconstruction pathologies. Rather than advocating for a single superior methodology, we highlight the complementarity of these approaches, showing how boundary kinematics can reliably support volumetric integration on highly irregular meshes.

In this study, Cu₂ZnSn(Se_1-xS_x)₄ (CZTSSe) semiconductors are investigated as a composition-dependent first-principles screening platform for pollutant adsorption and photocatalytic-related electronic properties relevant to cement-industry emissions. Density functional theory (DFT) calculations were performed for four compositions: Cu₂ZnSnSe₄, Cu₂ZnSn(Se_0.75S_0.25)₄, Cu₂ZnSn(Se_0.50S_0.50)₄, and Cu₂ZnSn(Se_0.25S_0.75)₄. Structural, electronic, density-of-states (DOS/PDOS), optical, effective-mass, mechanical, and adsorption properties were evaluated using a consistent computational setup. The calculated PBE band gap decreases from 1.515 eV for Cu₂ZnSnSe₄ to 1.281 eV for Cu₂ZnSn(Se_0.25S_0.75)₄, indicating that Se/S substitution can tune the electronic structure and associated optical response. The calculated adsorption energies show stronger relative interactions for CO₂ and SO₂ than for CO within the adopted adsorption model, with the strongest CO₂ and SO₂ interactions obtained for Cu₂ZnSnSe₄. The effective masses are used only as descriptors of band curvature, while the elastic calculations are interpreted in terms of the theoretical mechanical stability of the idealized structures. These results provide composition-dependent computational trends that can guide subsequent experimental and mechanistic studies. The calculations do not by themselves establish adsorption capacity, pollutant-removal efficiency, photocatalytic efficiency, carrier mobility, regeneration behavior, or long-term industrial stability.

In this study, the bulk and surface properties of Pb(-)Tl and In(-)Tl liquid alloys are investigated at 773 K and 723 K, respectively. Because these properties are difficult and expensive to determine experimentally at high temperatures, theoretical models are employed. For each alloy, the self-association model (SAM) is first used to obtain good agreement between the computed and experimental values of thermodynamic properties, such as the Gibbs free energy of mixing (G_M), the entropy of mixing (S_M), the heat of mixing (H_M) and chemical activity. Subsequently, other properties are calculated using Darken’s diffusion equation, a semi-empirical model (Moelwyn-Hughes model) and a statistical mechanical method based on the concept of a layered structure near the interface. The results show the existence of weak hetero-coordination and weak homo-coordination in the bulk of Pb(-)Tl and In(-)Tl alloys, respectively. Also, more thallium atoms are present at the surface of In(-)Tl alloy compared to Pb(-)Tl alloy. Pb(-)Tl alloy exhibits a tendency to ideal mixing at the surface, while In(-)Tl alloy exhibits homo-coordination at the surface for all bulk concentrations of thallium.

In this work, we present a comprehensive kinetic analysis of ammonia desorption from a series of titanium silicalite-1 (TS-1) samples, including both commercial (ZD-07031) and laboratory-synthesized materials with varying titanium content and textural properties. The classical Polanyi–Wigner model, despite yielding high correlation coefficients (R² ≥ 0.98), fails to provide convergent kinetic parameters across different samples, with pre-exponential factors varying by orders of magnitude. To overcome this limitation, we employed the Schroeder–Eyring equation, which explicitly accounts for the temperature dependence of the pre-exponential factor through the thermodynamic parameters of activation – enthalpy (Δ_rH^0≠) and entropy (Δ_rS^0≠). This modification not only improves the consistency of the fitted parameters but also reduces the number of kinetic curves required to describe the experimental data. Kinetics analysis reveals that ammonia desorption from TS-1 can be adequately described by a two-site model with characteristic thermodynamic parameters: for type I sites, Δ_rS^0≠ = 5.03 ± 0.19 J·(K·mol)⁻¹ and Δ_rH^0≠ = 100.7 ± 0.7 kJ·mol⁻¹; for type II sites, Δ_rS^0≠ = 59 ± 3 J·(K·mol)⁻¹ and Δ_rH^0≠ = 124.5 ± 0.8 kJ·mol⁻¹. These values are consistent across all samples, despite variations in titanium content and specific surface area (438–518 m²/g). The observed third-order kinetics is rationalized by a collision-mediated desorption mechanism: adsorbed ammonia molecules diffuse along the surface in a fluid-like state within the micropores; triple molecular collisions can concentrate sufficient energy to overcome the desorption barrier. This interpretation suggests that for weakly acidic zeolites such as TS-1, ammonia TPD primarily probes the volume-filling properties of the porous structure rather than surface acidity per se. The proposed kinetic modeling approach provides a physically meaningful framework for interpreting TPD-NH₃ data, enabling the extraction of consistent thermodynamic parameters that are independent of experimental conditions. This work opens the possibility of standardizing TPD methodology for comparative analysis of zeolitic materials.

Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness. Active learning (AL) provides a principled framework for constructing efficient and accurate IPs, yet most strategies reduce parameter uncertainty without explicitly accounting for the specific material properties being predicted. The information-matching (IM) approach addresses this limitation by requiring that the selected training data provide at least as much parameter space information as needed to achieve prescribed uncertainty targets for selected quantities of interest (QoIs). Here, we apply IM to develop bespoke IPs specifically tailored for predicting plastic strength in metals. Due to the high computational cost of simulating plastic strength, we employ an indirect IM strategy that targets inexpensive intermediate QoIs that correlate with strength. The IM method enables precise parameter constraints with minimal training data, yielding precise predictions for both the intermediate QoIs and plastic strength. Yet, model error remains a key limitation, and a post hoc uncertainty inflation correction provides a viable means to mitigate this limitation. These findings illustrate both the promise and limits of uncertainty-aware AL for predicting complex material properties.