Molecular dynamics study on structural and atomic evolution between Au and Ni nanoparticles through coalescence

Insight into the effect of ibuprofen on the permeability of the membrane: a molecular dynamic simulation study

Studying interactions between drugs and cell membranes is of great interest to designing novel drugs, optimizing drug delivery, and discerning drug mechanism action. In this study, we investigated the physical properties of the bilayer membrane model of POPC upon interaction with ibuprofen (IBU) using molecular dynamics simulations. The area per lipid (APL) was calculated to describe the effect of ibuprofen on the packing properties of the lipid bilayer. The APL was 0.58 nm2 and 0.63 nm2 for the membrane in low and high IBU respectively, and 0.57 nm2 for the membrane without IBU. Our finding showed that the mean square deviation (MSD) increased with increased ibuprofen content. In addition, the order parameter for the hydrocarbon chain of lipids increased with increased ibuprofen content. There was an increment in the transfer free energy after the head group region while it was maximum in the hydrophobic core for hydrogen peroxide (H2O2) (∼6.2 kcal.mol-1) and H2O (∼3.4 kcal.mol-1) which then decreased to respective values of (∼4.6 kcal.mol-1), and (∼2.3 kcal.mol-1) at the center of the bilayer in the presence of IBU. It seems that in the presence of ibuprofen, the free energy profile of the permeability of water and H2O2 significantly decreased. These findings show that ibuprofen significantly influences the physical properties of the bilayer by decreasing the packing and intermolecular interaction in the hydrocarbon chain region and increasing the water permeability of the bilayer. These results may provide insights into the local cytotoxic side effects of ibuprofen and its underlying molecular mechanisms.Communicated by Ramaswamy H. Sarma.

Development of a Machine Learning Potential to Study the Structure and Thermodynamics of Nickel Nanoclusters

Machine learning (ML) potentials such as the Gaussian approximation potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity, and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large-scale MD simulations using the GAP model analyzed with data-driven models like graph neural networks reveal intriguing insights into the size-dependent phase behavior and thermomechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models, which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.

Computational understanding of the coalescence of metallic nanoparticles: a mini review

Metallic nanoparticles exhibit extraordinary properties that differ from those of bulk materials due to their large surface area to volume ratios. Coalescence of metallic nanoparticles has a huge impact on their properties. Remarkable progress has been made by using computational methods for understanding nanoparticle coalescence. This work aims to provide a mini review on the state-of-the-art modelling and simulation of nanoparticle coalescence. First, we will discuss the outstanding performances and coalescence behaviors of metallic nanoparticles, and list some challenges in the coalescence of metallic nanoparticles. Next, we will introduce the applications of molecular dynamics and the Monte Carlo method in nanoparticle coalescence. Furthermore, we will discuss the coalescence kinetics and mechanisms of metal nanoparticles with the same element and different elements, alloy nanoparticles and metal oxide nanoparticles. Finally, we will present our perspective and conclusion.

Analysis of Dynamical Peculiarities in Nanoalloys at Subsystems Level: Dynamical Degrees of Freedom, Temperature Differences, and the Chameleon Effect

A novel analysis of the dynamical behavior of nanoalloy systems, as represented by model Ni/Al 13-atom clusters, over a broad range of energies that cover the stage-wise transition of the systems from their solid-like to liquid-like state is presented. Conceptually, the analysis is rooted in partitioning the systems into judiciously chosen subsystems and characterizing the latter in terms of subsystem-specific dynamical descriptors that include dynamical degrees of freedom, root-mean-square bond-length fluctuation, and element-specific subsystem temperature. The analysis reveals a host of intriguing new peculiarities in the dynamical behavior of the Ni/Al 13-mers, among which are what we call the chameleon effect and the difference in the temperatures of the Ni and Al subsystems at high energies, a difference that strongly depends on the cluster composition and also changes with energy. These do not have an analog in pure Ni13 and Al13 and are explained in terms of the coupled effects of the difference between the masses of the Ni and Al atoms (the mass effect) and of the difference in the anharmonicity of the overall interaction potential as experienced by the Ni and Al subsystems of the clusters (the potential effect).