First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures

The structural, mechanical, and thermodynamic properties of refractory metals Rh, Ir, W, Ta, Nb, Mo, Re, and Os have been systematically investigated by first-principles calculations based on density functional theory. Comparative studies reveal that Young's modulus (E = 636.42 GPa), shear modulus (G = 256.81 GPa), bulk modulus (B = 406.55 GPa), and microhardness (H = 44.69 GPa) of hexagonal Os are the highest, which reveals Os has the best overall mechanical properties. The body-centered cubic Nb has the smallest Young's modulus (E = 94.76 GPa), shear modulus (G = 33.62 GPa), bulk modulus (B = 174.50 GPa), and hardness (H = 2.04 GPa). Based on the ratio of bulk to shear modulus, it is judged that Rh, Ir, and Os are brittle materials (B/G < 1.75), and Nb, Ta, Mo, W, and Re exhibit ductile (B/G > 1.75). The elastic anisotropy has also been discussed by plotting both the 3D contours and the 2D planar projections of Young's modulus. For the face-centered cubic metals Rh and Ir and hexagonal close-packed metals Re and Os, the 3D contours of the Young's modulus are very similar, whereas body-centered cubic metals Ta, W, Nb, and Mo exhibit significant difference in elastic anisotropy. The thermodynamic calculations show that Debye temperature and minimum thermal conductivity decreases along Rh, Os, Mo, Ir, Re, W, Ta, Nb sequence. Furthermore, the results can be used as a general guidance for the design and development of high temperature refractory alloy system.

High Young's modulus of metallic biomaterials in comparison with that of bone affects harmfully on bone healing and remodeling. Therefore, it is important to decrease Young's modulus of the metallic biomaterials. In that case, porous materials are advantageous from the viewpoint of obtaining a lower Young's modulus, because Young's modulus decreases with increasing porosity of the porous materials. However, with an increase in porosity, the other mechanical properties start deteriorating simultaneously. This deterioration of the mechanical properties is probably derived from the stress concentration at the pores of the porous materials. In comparison with metallic biomaterials, polymers exhibit lower Young's moduli; thus a polymer filling is a likely option to improve the mechanical properties of porous materials by preventing the stress concentration at the pores without increasing the Young's modulus. Furthermore, certain polymers exhibit intrinsic biofunctionalities. Thus, polymer filling is expected to improve the mechanical properties and impart biofunctionalities to porous materials without an increase in its Young's modulus. In this study, the pores of porous pure titanium (pTi) with the porosity of 22-50% were filled with a medical polymer (polymethylmethacrylate: PMMA). The effects of PMMA filling on the tensile strength and Young's modulus of pTi were then investigated. As a result, it is found that the PMMA filling improves the tensile strength of pTi with the porosity over 40%, although not significantly affecting the Young's modulus of pTi.

The initial temperature has a considerable effect on aluminum polycrystals' physical stability and mechanical performance, with the possibility to optimize their mechanical properties for practical applications. Thus, using a molecular dynamics technique, the effect of temperature on the mechanical properties of aluminum polycrystals is studied. Stress-strain curves, ultimate strength, and Young's modulus were all measured at temperatures of 300, 350, 400, and 450 K. The findings from MD simulations show that the initial temperature significantly affects the physical stability and mechanical performance of designed aluminum polycrystals. The aluminum polycrystal experiences a numerical increase in ultimate strength and Young's modulus from 6640 to 74.072 to 7.055 and 79.226 GPa, respectively, when subjected to the optimal initial conditions of 350 K. With further increasing temperature to 450 K, ultimate strength and Young's modulus decrease to 6.461 and 74.413 GPa, respectively. The observed decrease in ultimate strength and Young's modulus of the aluminum polycrystal as the temperature increased from the optimal condition of 350 K–450 K can be attributed to the weakening of interatomic attraction forces at higher temperatures. This reduction in interatomic bonding strength resulted in decreased material stiffness and resistance to deformation, leading to lower ultimate strength and Young's modulus values. This study's novelty lies in its comprehensive assessment of the initial temperature's effects on the mechanical performance of aluminum polycrystals, providing valuable insights for practical applications and advancing beyond previous efforts in the literature.

Mechanical properties of yttria-stabilized zirconia: A study by ReaxFF molecular dynamics simulations

Molecular dynamics study on axial elastic modulus of carbon nanoropes

Mechanical and electrical properties of functionalized graphene nanoribbon: A study of reactive molecular dynamic simulation and density functional tight-binding theory

The spatial organisation of mechanical cues is increasingly recognised as a key regulator of tissue development and disease, yet in vitro systems capable of replicating such environments remain limited. Fibroblast migration and the fibroblast-to-myofibroblast transition, which affect scar formation, are examples of mechanisms affected by mechanical cues. We report a photolithographic platform that enables precise, spatial patterning of the Young's modulus of gelatin methacryloyl (GelMA) hydrogels, using a ruthenium/sodium persulfate (SPS) photoinitiation system. By altering light intensity, we achieved Young's modulus tunability between 4 kPa and 46 kPa in non-patterned gels. By displaying a binary light intensity pattern over the gel, the Young's modulus could be switched from 10 kPa to 45 kPa, representing healthy and fibrotic Young's moduli, over a distance of ∼20 μm (2.3MPa·mm⁻¹). This system could also generate relatively linear Young's modulus gradients that are more physiologically relevant, ∼10 kPa⋅mm-1. Non-patterned and binary-patterned gels confirmed that increasing the Young's modulus drives the fibroblast-to-myofibroblast transition, observed by significantly greater cell volumes and α-smooth muscle actin (α-SMA) stress fibre formation, from encapsulated cardiac fibroblasts after 7 days. In gradient-patterned gels, fibroblasts exhibited a progressive, modulus-dependent increase in both cell volume and α-SMA stress fibre formation, alongside expressing a durotactic response, moving from healthy to fibrotic environments, and also aligning themselves between ±45 degrees to the Young's modulus gradient. Beyond cardiac fibrosis, this versatile platform enables rapid generation of biologically relevant mechanical landscapes for diverse mechanobiological applications, bridging the gap between simplified, mechanically uniform models and the heterogeneous microenvironments of native tissues. STATEMENT OF SIGNIFICANCE: This study developed a platform for spatially controlling the Young's modulus of GelMA using a digital light projection system. Using this system, a change in Young's modulus from 10 kPa to 45 kPa was achieved with a resolution of 20 µm. Natural Young's modulus gradients observed in tissues (∼10 kPa·mm⁻¹) can also be achieved. The fibroblast-to-myofibroblast transition was controlled spatially in the patterned gels, evidenced by changes in cell volume and α-SMA fibrillation. Fibroblasts demonstrated alignment and migration to the Young's modulus gradient. Our photopatterning system demonstrates ease of pattern/gradient definition with adaptability to a diverse range of systems, highlighting the impact of mechanical properties on cardiac scar formation, offering avenues for studying these effects.

When blending rubbers into polymers, different rubber distribution status and fraction due to different mechanical property. In this research, effective mechanical properties of rubber-toughened polymers with four blending fraction in six kinds of particle distribution status are simulated numerically by using finite element method. Rubber particle distribution model include four 2D models and two 3D models. Typical effective mechanical properties such as yield stress, Young's modulus, Poisson's ratio and stress-strain curve of each status are obtained. The Results show that all models Young's modulus and Poisson's ratio decrease with rubber particle volume fraction increasing. Young's modulus and Poisson's ratio of three-dimensional body-centered cubic and face-centered cubic models are in a close magnitude range, it means rubber particle volume fraction has less effect on 2D models and two 3D models. As we all known, Matrix yielding, crazing and interface debond. All play an important role in the toughening process of rubber-toughened polymers. So in this paper we also study on toughening mechanism using same models. Our simulation takes use of stress concentration factor, yield ratio and interface elements' strain difference which is related with matrix yielding, crazing and interface debond to study the toughening mechanism. Simulation shows that the maximum stress concentration factor increases with particle volume fraction. The shear yielding occurs first at the equator of rubber particle, and then yield region expands from the equator to the pole of the particle with loads increasing.

Effect of compressive prestress on the correlation between thermal conductivity and Young's modulus of carbon materials, fine-grained isotropic graphite IG-430U and a felt type C/C composite CX-2002U, was examined. Young's modulus and thermal conductivity decreased due to compressive prestress. The change in thermal conductivity of carbon materials was related to that of Young's modulus via sound velocity. The decrease in thermal conductivity of IG-430U graphite due to prestress was mostly correlated with that in Young's modulus. Also, decrease in thermal conductivity of C/C composite showed the similar tendency as the case for the graphite.

Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data. In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements. The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.

Development of an architecture-property model for triply periodic minimal surface structures and validation using material extrusion additive manufacturing with polyetheretherketone (PEEK)

Investigation of mechanical properties of bedded shale by nanoindentation tests: A case study on Lower Silurian Longmaxi Formation of Youyang area in southeast Chongqing, China

Effect of various defects on mechanical and electronic properties of zinc-oxide graphene-like structure: A DFT study

Atomic insights into the tensile behavior of carbon nanotube with different geometrical characteristics embedded in nickel matrix

Porosity effect on the thermal and mechanical properties of U-50Zr alloy: A molecular dynamics study