Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN explored by a deep-learning interatomic potential.

Penta-NiN, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The DLP's accuracy is verified, showing strong agreement with AIMD results.

Lattice thermal conductivity and Young's modulus of XN (X = Be, Mg and Pt) 2D materials using machine learning interatomic potentials.

The newly synthesized BeN monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN, MgN, and PtN, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed and utilized in classical molecular dynamics (MD) simulations.

Estimation of drug and tumor properties using novel hybrid meta-heuristic methods.

One of the major drawbacks in mathematical modeling of the drug delivery in living species is application of a common value for a specific property such as diffusion coefficient of drug in tissue, while this property is unique for each person or species. Therefore, knowledge on the species-specific values of these properties can improve the process of drug delivery and treatment. Inverse problem methods can achieve these unique properties for each specimen.