Behavior Of Materials Research Articles

Layered Double Hydroxide Nanosheets Incorporated Hierarchical Hydrogen Bonding Polymer Networks for Transparent and Fire-Proof Ceramizable Coatings

In recent decades, annual urban fire incidents, including those involving ancient wooden buildings burned, transportation, and solar panels, have increased, leading to significant loss of human life and property. Addressing this issue without altering the surface morphology or interfering with optical behavior of flammable materials poses a substantial challenge. Herein, we present a transparent, low thickness, ceramifiable nanosystem coating composed of a highly adhesive base (poly(SSS1-co-HEMA1)), nanoscale layered double hydroxide sheets as ceramic precursors, and supramolecular melamine di-borate as an accelerator. We demonstrate that this hybrid coating can transform into a porous, fire-resistant protective layer with a highly thermostable vitreous phase upon exposure to flame/heat source. A nanosystem coating of just ~ 100 μm thickness can significantly increase the limiting oxygen index of wood (Pine) to 37.3%, dramatically reduce total heat release by 78.6%, and maintain low smoke toxicity (CITG = 0.016). Detailed molecular force analysis, combined with a comprehensive examination of the underlying flame-retardant mechanisms, underscores the effectiveness of this coating. This work offers a strategy for creating efficient, environmentally friendly coatings with fire safety applications across various industries.

Transferable machine learning approach for predicting electronic structures of charged defects

The study of electronic properties of charged defects plays a crucial role in advancing our understanding of how defects influence conductivity, magnetism, and optical behavior in various materials. However, despite its significance, research on large-scale defective systems has been hindered by the high computational cost associated with density functional theory (DFT). In this study, we propose HamGNN-Q, an E(3) equivariant graph neural network framework capable of accurately predicting DFT Hamiltonian matrices for diverse point defects with varying charges, utilizing a unified set of network weights. By incorporating background charge features into the element representation, HamGNN-Q facilitates a direct mapping from structural characteristics and background charges to the electronic Hamiltonian matrix of charged defect systems, obviating the need for DFT calculations. We showcase the model's high precision and transferability by evaluating its performance on GaAs systems encompassing diverse charged defect configurations. Furthermore, we predicted the wave function distribution of polarons induced by defects. We analyzed the node features through principal component analysis, providing physical insights for the interpretability of the HamGNN-Q model. Our approach provides a practical solution for accelerating electronic structure calculations of neutral and charged defects and advancing the design of materials with tailored electronic properties.

Using image processing techniques and multi-scale finite element models to predict the mechanical behavior of composite materials

Using image processing techniques and multi-scale finite element models to predict the mechanical behavior of composite materials

Monolayer MoS2 with S vacancy defects doped with Group V non-metallic elements (N, P, As): a first-principles study.

This study systematically investigated the effects of single S-atom vacancy defects and composite defects (vacancy combined with doping) on the properties of MoS2 using density functional theory. The results revealed that N-doped S-vacancy MoS2 has the smallest composite defect formation energy, indicating its highest stability. Doping maintained the direct band gap characteristic, with shifts in the valence band top. The Fermi level slightly shifted down in N- and P-doped systems, with N-doped MoS2 showing a larger increase in valence band top energy. Doping also significantly altered the density of states at the Fermi level and weakened the dielectric properties of MoS2. The maximum dielectric peaks of doped systems appeared near 2.7 eV with reduced intensities and red-shifted energies. Optical properties were significantly changed, with decreased reflectance, narrower reflectance spectra, and blue-shifted absorption spectra. These findings suggest that introducing composite defects can effectively reduce the forbidden bandwidth of MoS2, enhancing electrical conductivity. This research provides theoretical guidance for novel material design and offers insights into composite defect behavior in other two-dimensional materials. The Materials-Studio CASTEP module was used to calculate density functional theory (DFT). A plane wave ultrasoft pseudopotential is used to optimize the crystal structure, and the generalized gradient approximation (GGA) in the form of Perdew-Burke-Ernzerhof (PBE) is used to characterize the exchange correlation energy. After the convergence test, the truncation energy and dot settings were finally selected to be 450 eV and 3 × 3 × 1, respectively, the convergence accuracy was set to 1.0e-5eV/atom, and the convergence criterion for the interatomic interaction force was 0.02 eV/Å. The parameters were all at or better than the accuracy settings. The vacuum layer between the layers wasset to 18 Å to avoid interactions caused by the periodic calculation method.

Viscoelastic Memory Effects in Cyclic Thermomechanical Loading of Epoxy Polymer and Glass-Reinforced Composite: An Experimental Study and Modeling Under Variable Initial Stress and Cycle Durations

This article presents a study of the viscoelastic behavior of an epoxy polymer and a glass-reinforced composite based on it under cyclic thermomechanical loading. The goal is to model and explain the experimentally observed stress state formation, including the accumulation of residual stresses under various initial mechanical stress levels and heating/cooling cycle durations. An improved material model, implemented as a Python script, is used, allowing for the consideration of memory effects on thermomechanical loading depending on the level and nature (mechanical or thermal) of the initial stresses. A Python script was developed to determine the viscoelastic parameters (elastic modulus E1, elastic parameter E2, and viscosity) for the three-element Kelvin–Voigt model. These parameters were determined at different temperatures for both the polymer and the glass-reinforced composite used in the modeling. The accumulation of stresses under different ratios of mechanical and thermal stresses was also investigated. Experiments showed that high levels of residual stress could form in the pure epoxy polymer. The initial stress state significantly influences residual stress accumulation in the pure epoxy polymer. Low initial tensile stresses (0–1.5 MPa) resulted in substantial residual stress accumulation, exceeding the initial stresses by up to 2.7 times and reaching values of up to 2.1 MPa. Conversely, high initial stresses (around 4 MPa) suppressed residual stress accumulation due to the dominance of relaxation processes. This highlights the critical role of the initial loading conditions in predicting long-term material behavior. In the glass-reinforced plastic, the effect of residual stress accumulation was significantly weaker, possibly due to the reinforcement and high residual stiffness, even at elevated temperatures (the studies were conducted from 30 to 180 °C for the composite and from 30 to 90 °C for the polymer). The modeling results show satisfactory qualitative and quantitative agreement with the experimental data, offering a plausible explanation for the observed effects. The proposed approach and tools can be used to predict the stress–strain state of polymer composite structures operating under cyclic thermomechanical loads.

Flexural Strength, Fatigue Behavior, and Microhardness of Three-Dimensional (3D)-Printed Resin Material for Indirect Restorations: A Systematic Review

The purpose of this systematic review was to evaluate three mechanical properties of 3D-printed resins for indirect restorations according to published scientific evidence. This systematic review was conducted according to the PRISMA statement (preferred reporting elements for systematic reviews and meta-analyses). The search was performed by two investigators, (DD) and (VB), and a third (AC) resolved disagreements. Articles were searched in four digital databases: PubMed, EBSCO, Lilacs, and Science Direct, starting on 18 February 2024. As 3D-printing technology has shown significant advances in the last 5 years, the review was conducted with a publication year range between 2019 and 2024, in English language and included in vitro articles on the mechanical properties of flexural strength, fatigue behavior, and microhardness of 3D-printed materials for temporary or definitive restorations. MeSH terms and free terms were used for the titles and abstracts of each article. Finally, the QUIN tool was used to assess the risk of bias. In the main search, 227 articles were found, of which 20 duplicates were excluded, leaving 207 articles; of these, titles and abstracts were read, and 181 that did not meet the eligibility criteria were eliminated; of the remaining 26 articles, 1 article was eliminated for not presenting quantitative results. Regarding publication bias, 6 of the 25 articles had a low risk of bias, 18 had a medium risk of bias, and 1 had a high risk of bias. It may be concluded that 3D-printed resins have lower flexural strength, fatigue behavior, and microhardness than other resin types used for the fabrication of temporary and permanent restorations. The type of 3D printer and polymerization time could be factors that significantly affect the flexural strength, fatigue behavior and microhardness of 3D-printed resins. Based on existing evidence, it should be considered that additive technology has promising future prospects for temporary and permanent dental restorations.

Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities

Additive manufacturing (AM) or 3D printing technology creates a tangible object by adding successive layers of materials. Nowadays, 3D printing is used for developing both metal and non-metal products. In the advancement of 3D printing technology, material specimen design, modification, and testing become very simple, especially for non-metal materials, such as hyperelastic, thermoplastic, or rubber-like materials. However, proper material modeling and validation are required for the analysis of these types of materials. In this study, 3D printed poly lactic acid (PLA+) material behavior is analyzed numerically for validation in the counterpart of experimental analysis to evaluate their behavior in both cases. The specimen was designed in SolidWorks by following ASTM D638 dimension standards with proper infill densities and raster angle or infill orientation angle. These infill layer densities and angles of orientation play an important role in the mechanical behavior of the specimen. This paper aims to present a numerical validation of five infill densities (20%, 40%, 60%, 80%, and 100%) for a ±45-degree infill angle orientation by incorporating a nonlinear hyperelastic model. Results indicate that infill densities affect the mechanical behavior of PLA+ material. The result also suggested that neo-Hookean and Mooney–Rivlin are the best-fitted hyperelastic material models for these five separate linear infill densities. However, neo-Hookean is easier to analyze, as it has only one parameter and a new equation is developed in this study for determining the parameter for different infill densities.

Improving Combustion Analysis of Extruded Polystyrene via Custom Isolation Methodology

This study is dedicated to an in−depth analysis of the combustion characteristics of extruded polystyrene (XPS) as a building insulation material with the aim of accurately assessing its fire risk in the built environment. Innovatively, this research employed a cone calorimeter equipped with a self−designed insulating sample holder to conduct a systematic experimental study. Additionally, it performed a comprehensive analysis of the ignition characteristics, heat release rate, fire hazard, smoke release, and toxic gas emission of XPS materials. The experimental results revealed that the combustion behavior of XPS is influenced by multiple factors, including the content of flame retardants and external heat flux, which significantly affect the fire hazard of XPS. When the thermal radiation intensity escalates from 25 kW/m2 to 55 kW/m2, the peak heat release rate of XPS−B1 rises from 428 kW/m2 to 535 kW/m2, marking an increase of 25.00%. Conversely, the peak heat release rate of XPS−B2 surges from 348 kW/m2 to 579 kW/m2, reflecting a substantial increase of 66.38%. This research not only provides a solid theoretical foundation and detailed experimental data for the fire behavior of XPS materials but also holds significant practical importance for enhancing the fire safety of buildings. Overall, this research contributes to the scientific understanding of XPS insulation materials and supports the development of more effective fire prevention measures in construction.

Applications of density functional theory to corrosion and corrosion prevention of metals: A review

AbstractRecently, density functional theory (DFT) has been a powerful tool to model the corrosion behaviors of materials, provide insights into the corrosion mechanisms, predict the corrosion performance of materials, and design the corrosion‐resistant alloys and organic inhibitors. DFT enables corrosion scientist to fundamentally understand the corrosion behaviors and corrosion mechanisms of materials from the perspective of atomic and electronic structures, combining with the traditional and advanced experimental tests. This review briefly summarizes the main features of DFT calculations and present a comprehensive overview of their typical applications to corrosion and corrosion prevention of metals, involving potential‐pH diagrams, hydrogen evolution reaction, anodic dissolution, passivity and passivity breakdown, and organic inhibitor for metals. The paper also reviews the correlations between DFT‐computed descriptors and the micro/macro physiochemical parameters of corrosion. Despite the great progress achieved by DFT, there are still some challenges in addressing corrosion issues due to the lack of bridges between the DFT‐calculated electronic parameters and the macro corrosion performance of materials. The DFT modeling‐experiment‐engineering‐theory model will be a potential method to clarify and build the links.

Mechanical Properties and Constitutive Model of High-Mass-Fraction Pressed Tungsten Powder/Polytetrafluoroethylene-Based Composites

Heavy metal powders driven by explosions can enhance the near-field lethality of explosive warheads by forming a quasi-pressure field while reducing collateral damage at medium and long ranges. Incorporating polymers into high-content metal powders prevents powder sintering under explosive high pressure, enhancing dispersion uniformity and making them promising for controllable warhead applications. To describe the mechanical behavior of materials under impact loading, this paper investigates the dynamic and static mechanical properties and constitutive modeling of tungsten powder/polytetrafluoroethylene (PTFE) composites. Quasi-static compression tests and split Hopkinson pressure bar (SHPB) dynamic tests were conducted on composites with varying tungsten contents (0 wt%, 70 wt%, 80 wt%, and 90 wt%) and particle sizes (200 μm, 400 μm, and 600 μm), obtaining compressive stress–strain curves over a strain rate range of 0.001 to 3610 s−1. The compressive strength of the composites slightly decreased with increasing tungsten particle size but increased with higher tungsten content. Under quasi-static compression, the compressive strength of the composites with 70 wt% and 80 wt% tungsten was lower than that of pure PTFE. This was due to the bonding strength between the tungsten particles and the resin being weaker than the cohesion within the resin. Additionally, the random distribution of the tungsten particles in the matrix led to shear cracks propagating along the phase interfaces, reducing the compressive strength. The compressive strength of the composites with 90 wt% tungsten exceeded that of pure PTFE, as the packed arrangement of the tungsten particles increased the material strength through particle extrusion and friction during compression. Under dynamic impact, the compressive strength of the composites was higher than that of pure PTFE, primarily due to particle extrusion and friction effects. The composites exhibited significant strain rate sensitivity, with both the compressive strength and critical strain increasing quasi-linearly with the strain rate. Based on the experimental data, a damage-modified Zhu–Wang–Tang (ZWT) viscoelastic model was employed to fit the data, effectively characterizing the uniaxial compressive constitutive behavior of tungsten powder/PTFE composites.

Shear-Induced Anisotropy Analysis of Rock-like Specimens Containing Different Inclination Angles of Non-Persistent Joints

Discontinuities in rock mass are usually considered to be important influencing factors for shear failure. As a type of granular material, the macroscopic mechanical behavior of rock masses is closely related to the anisotropy of the contact network. This paper uses the discrete element method (DEM) to simulate direct shear tests of specimens with different joint inclinations and analyzes the evolution of shear-induced fabric anisotropy and contact force anisotropy during the shear process. Three anisotropic tensors aijc, aijn and aijt are defined to characterize the anisotropic behavior of granular materials. The macroscopic mechanical behavior of the specimens is explained from the micromechanical level combined with the evolution laws of the microcracks and energy of the specimens. The research results indicate that, after the appearance of microcracks in the specimens, the joint inclination leads to changes in their macroscopic mechanical behavior such as peak shear stress, peak displacement and failure mode by affecting the development of the fabric and contact anisotropy of the specimens. Meanwhile, a decrease in fabric and contact anisotropy often indicates specimen failure.

Melting Temperature Hidden Behind Liquid-Liquid Phase Transition in Glycerol.

Liquid-liquid phase transitions play a pivotal role in various scientific disciplines and technological applications, ranging from biology to materials science and geophysics. Understanding the behavior of materials undergoing these transitions provides valuable insights into complex systems and their dynamic properties. This review explores the implications of liquid-liquid phase transitions, particularly focusing on the transition between low-density liquid (LDL) and high-density liquid (HDL) phases. We investigate the thermodynamic, structural, and mechanistic aspects of these transitions, emphasizing their relevance in diverse fields. The creation of dynamic heterogeneities and critical fluctuations during liquid-liquid phase transitions is discussed, highlighting their role in shaping the phase behavior and dynamics of complex fluids. Experimental observations, including the use of dielectric spectroscopy and nonlinear methods, shed light on the intricate nature of these transitions. Our findings suggest a connection between liquid-liquid phase transitions and critical phenomena, with implications for understanding the supercooled state and phase behavior of hydrogen-bonded liquids such as glycerol. Overall, this review underscores the importance of interdisciplinary approaches in unraveling the complexities of liquid-liquid phase behavior and addressing fundamental questions.

Using Neural Networks for Pavement Rolling Resistance

All pavements contribute to the rolling resistance of vehicles. For passenger cars, the pavement influence is limited to the surface properties, but heavy trucks are influenced by the deformation, the internal damping, and non-elastic behavior of the pavement materials involved. Previous studies have been addressing the pavement type, the material type and various stages of compaction. Recently, even the effects of curling slabs on rolling resistance was assessed. As sustainable pavements are now becoming a requirement from road authorities, it is important to have access to calculable parameters of energy losses during use, and not only from construction. The present paper addresses some of the input parameters needed to assess rolling resistance losses for pavements in general and rigid pavements in particular, by using neural network techniques. The results can be used for the decision-making in either bidding processes or strategic planning.

Seismic Failure Assessment Using Energy Outputs of Finite Element Analysis: A Strategy for Complex Heritage Masonry Structures Modeled with Concrete Damaged Plasticity Material

The structural assessment of masonry construction often requires the use of nonlinear 2D and 3D finite element analysis. This work describes a strategy for using energy outputs from such analyses to accurately assess failure conditions precipitated by increasing lateral load. The methodology relies on the analogy between plastic strains and fracture that is inherent to the concrete damaged plasticity (CDP) macro-model used to represent the quasi-brittle behavior of masonry material. At critical conditions, energy imparted to a structure by loading can no longer be completely stored as elastic strain energy and must be dissipated. This occurs with fractures in masonry, which are represented with plastic strains when using CDP material. The development of plastic dissipation energy can therefore be used as a measure for understanding the progressive collapse of a structure, as we illustrate with the following three case studies analyzed using Abaqus/CAE Explicit: the massive earthen pyramid at Huaca de la Luna (Trujillo, Peru), the Roman pozzolanic concrete vault of Diocletian’s Frigidarium (Rome, Italy), and the mixed-material triumphal arch of the San Pedro Apóstol Church of Andahuaylillas (Peru). The method is verified by other measures of failure and has particular applicability for seismic analysis of complex masonry and earthen structures.

A review on fretting wear/fatigue behavior, protective measures, and application examples of typical alloy materials

As fastening structures are widely used in mechanical equipment and components, and fretting wear/fatigue often occurs in fastening structures. And fretting wear/fatigue significantly reduces the service lifetime of key components due to its small displacement, high concealment, and fatal damage. And the fretting wear/fatigue behavior of different alloys is increasingly concerned, and how to effectively protect against fretting wear/fatigue is also a key issue of concern for experts and scholars. In this paper, the theory of fretting tribology, the fretting wear/fatigue behavior of friction interfaces of typical alloys, the common protection measures against fretting wear/fatigue, and the application examples of fretting wear/fatigue are reviewed and analyzed. Firstly, the history of the development of the fretting wear phenomenon and fretting tribology is introduced, the causes of the fretting wear phenomenon are explained and mechanically analyzed, and the fretting maps of the running conditions and the fretting maps of the material response are presented to better analyze the fretting wear/fatigue phenomenon. Secondly, the running conditions of typical alloys under different displacement amplitudes and loads are systematically described, and the two measures of surface technology and extrusion reinforcement are systematically analyzed, which provides a good basis for the selection, design, and use of protective measures against failures caused by fretting wear/fatigue in the following key mechanical components. Finally, the common application examples and problems of typical alloys are lists.

Three‐dimensional characterization of particle morphology in natural gravel and blasted rock fragments using SfM‐MVS photogrammetry

AbstractThis study aims to develop a high‐precision and cost‐efficient method for the three‐dimensional reconstruction of large particles in natural gravel and blasted rock fragments, utilizing Structure from Motion (SfM) and Multi‐View Stereo (MVS) techniques. The proposed approach was applied to characterize the three‐dimensional morphology of rockfill dam materials at a real construction site. Particle shape was quantitatively analyzed using shape indices of sphericity, convexity, and angularity. The predominant morphology of natural gravel is characterized as slightly elongated and slightly flat, while rock fragments are slightly elongated and not flat. Probability density distributions of shape indices follow a skewed normal distribution: sphericity and convexity show leftward skewness, whereas angularity is right‐skewed. Skewness parameters of sphericity and angularity are consistent between natural gravel and blasted rock fragments, indicating comparable shape asymmetry. Convexity skewness is significantly higher in natural gravel compared to rock fragments, by approximately an order of magnitude. The relationship between size and particle shape shows that form ratios and associated shape descriptors change linearly with the logarithm of size; larger particles approach spherical or cubic forms. The innovative measurements contribute to the particle shape data set of rockfill dam materials, providing valuable insights into the three‐dimensional and statistical morphological characteristics of relatively large particles in natural gravel and blasted rock fragments. This approach enhances understanding of particle morphology's impact on the mechanical behavior of granular materials.

Roller Compacted Concrete for Road Base Layer with RAP and Steel Fibers: Viscous Properties and Description of Experimental Sites

A high-performance composite material consisting of reinforced compacted cement concrete including Reclaimed Asphalt Pavement (RAP) has been investigated. This composite material is used as a base layer in combination with an asphalt overlay for heavy traffic roads. Steel fibers are used to minimize crack opening. RAP is added to preserve raw material and to comply with sustainable development, especially in the case of in-situ treatments. Composites with different RAP and fiber content and cement content were studied both in laboratory and on site. The laboratory study is divided into two parts. First, classical standard tests for road concrete were carried out: compressive strength, compressive modulus and tensile splitting strength, with a view to the design of pavement structures to be used on site. Secondly, more comprehensive rheological tests, such as complex modulus and fatigue tests, were performed to obtain a better knowledge of material behavior. Then, on-site behavior was studied from two experimental sites. The presence of bitumen in cement-treated composites induces an increase of viscous properties, observed mainly on the phase angle. These composites respect the time-temperature superposition principle and can be fitted by a visco-elastic rheological model.

Spectroscopic investigation of two xanthane dyes and design of a FRET based pesticide sensor

Layer-by-Layer (LbL) technique is the simplest and inexpensive method for preparartion of nano-dimensional thin films for tailoring material behavior having wide range of applications including sensors. Here, spectroscopic behavior of two laser dyes Acriflavine (Acf) and Rhodamine B (RhB) assembled onto LbL films have been investigated. It has been observed that both Acf and RhB form stable LbL films. Polyanion polyacrylic acid (PAA) was used to incorporate the Acf or RhB onto the LbL films. Adsorption of Acf and RhB onto PAA were completed within 45 min and 30 min respectively. During LbL film, material loss occurred in case of Acf. It has been demonstrated that such material loss can be minimized by incorporating clay laponite onto the LbL films. Temperature and pH dependant studies indicate that Acf and RhB assembled onto LbL films can be used to design temperature as well as pH sensors. Fluorescence Resonance Energy Transfer (FRET) between Acf and RhB has also been investigated. Interestingly, it has been demonstrated that the energy transfer efficiency can be manipulated using spacer molecules within the Acf and RhB LbL films. Laponite clay can be used to enhance the FRET efficiency, whereas stearic acid (SA) can be used to lower the efficiency. FRET efficiency linearly changes upon exposure of a pesticide pretilachlor at varying concentration. This study indicated that with proper calibration, proposed sensing system can be used to design FRET based pesticide sensor with detection limit of 0.22 ppm.

Classification Framework for Assessing Anthropogenic Sedimentary Facies

As the mass of human-made materials now surpasses that of Earth’s total dry biomass, there is a critical need for sedimentologists to account for anthropogenic materials when analyzing depositional environments. To address this, a classification scheme is presented that extends traditional sedimentological models to encompass the diversity of modern sediments and their complex dynamics. Through relying on established sedimentological principles, the framework incorporates bed-scale, grain-scale, and sedimentary structure descriptors, as well as a methodology for describing the composition of a deposit and flexible nomenclature. It is designed to be integrative and adaptable, permitting the incorporation of additional descriptions as necessary, and is applicable across depositional settings that are dominated by either natural or anthropogenic processes. The scheme offers a practical, systematic approach to categorize and analyze the textures and structures of anthropogenic sediments, facilitating the reconstruction of modern and geologically recent environments. This unifying starting point enables more detailed predictions of material behavior and their environmental impacts, with implications for recycling, reuse, and management strategies. Furthermore, standardized nomenclature will enhance the capacity for data comparability across field sites, facilitating further understanding of our environment. Applications of this classification scheme include many interdisciplinary possibilities, overlapping with archeology, environmental monitoring, and engineering. By adopting this classification, sedimentologists can forge a deeper understanding of the stratigraphic record of the Anthropocene, contribute to developing more comprehensive strategies to manage Earth’s changing landscapes, and better understand our future geological record.

ODE-DSN: A Surrogate Model for Dynamic Stiffness in Microscopic RVE Problems under Non-Uniform Time-step Strain Inputs

Abstract In multiscale finite element methods, solving macroscopic problems typically requires addressing computationally expensive microscopic Representative Volume Element (RVE) problems. To reduce this computational burden, a data-driven approach using Artificial Neural Networks (ANNs) has been employed to pre-train the strain-stress relationship of the microscopic RVE, bypassing the need for full micro-scale calculations. Existing research has also explored the use of Recurrent Neural Networks (RNNs) to handle history-dependent materials. Building on this approach, this paper introduces a novel Ordinary Differential Equation-Dynamic Stiffness Network (ODE-DSN) model to capture the dynamic stiffness of time-dependent materials and compute stress. The stiffness-based framework enhances the model’s physical consistency and interpretability, while the Ordinary Differential Equation (ODE) neural network effectively manages non-uniform time sampling in strain inputs. Examples demonstrate that the model accurately learns material behavior with limited data (around 560 random strain-stress sequences) and effectively handles non-uniform time steps. This method addresses the challenge of handling strain inputs with non-uniform time steps while offering potential advantages in computational efficiency and resource utilization.