Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks.

This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties.

On high-cycle fatigue of 316L stents.

This paper deals with fatigue life prediction of 316L stainless steel cardiac stents. Stents are biomedical devices used to reopen narrowed vessels. Fatigue life is dominated by the cyclic loading due to the systolic and diastolic pressure and the design against premature mechanical failure is of extreme importance.

Diffusion and viscosity of liquid tin: Green-Kubo relationship-based calculations from molecular dynamics simulations.

Molecular dynamics (MD) simulations of liquid tin between its melting point and 1600 °C have been performed in order to interpret and discuss the ionic structure. The interactions between ions are described by a new accurate pair potential built within the pseudopotential formalism and the linear response theory. The calculated structure factor that reflects the main information on the local atomic order in liquids is compared to diffraction measurements.

Preparation, structural characterization, and thermomechanical properties of poly(methyl methacrylate)/organoclay nanocomposites by melt intercalation.

Poly(methyl methacrylate) (PMMA) based nanocomposites were synthesized by melt intercalation technique using organoclays (Cloisite 30B and Cloisite 20A) as fillers. X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to determine the dispersion and the morphology of the nanocomposites obtained. Thermomechanical tests including tensile test and dynamic mechanical analysis (DMA) were used to evaluate the Young's modulus, storage modulus and the glass transition temperature.

Prediction of the mechanical properties of hydroxyapatite/polymethyl methacrylate/carbon nanotubes nanocomposite.

In this work carbon nanotubes (CNTs) were used to increase the strength and toughness of the hydroxyapatite (HA) and consequently to reduce its brittleness. The combination of CNT, HA and polymethyl methacrylate (PMMA) has led to a new composite material, which has mechanical properties superior to those of conventional HA/PMMA for biomedical scaffold in tissue engineering. PMMA is a well known bone cement which is highly compatible with HA and also it can act as a functionalizing/linking material with HA.

Micromechanical modeling and characterization of the effective properties in starch-based nano-biocomposites.

The aim of this work was to predict the effective elastic properties of starch-based nano-biocomposites. Experiments (materials elaboration, morphological characterization and determination of mechanical properties) were conducted on both the pristine matrix (plasticized starch) and the matrix filled with montmorillonite nanoclay. Aggregated/intercalated and exfoliated nano-biocomposites were produced and mechanically tested under uniaxial tension to understand the effect of montmorillonite morphology/dispersion on the stiffness properties of starch-based nano-biocomposites.