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.