MD Simulations of Nanoparticle Self-Assembly: from Aggregation to Morphological Evolution

EasyChair Preprint 12159

Abstract

Molecular dynamics (MD) simulations have emerged as a powerful tool for investigating the self-assembly behavior of nanoparticles, offering invaluable insights into their aggregation kinetics and morphological evolution at the atomic scale. In this study, we present a comprehensive overview of recent advancements in MD simulations applied to nanoparticle self-assembly phenomena. We discuss the fundamental principles underlying MD simulations and their application in studying various aspects of nanoparticle assembly, including nucleation, growth, and coalescence processes. Furthermore, we highlight the role of key parameters such as nanoparticle size, shape, and surface chemistry in governing self-assembly pathways and final morphologies. Additionally, we explore the influence of external factors such as solvent properties, temperature, and pressure on nanoparticle assembly dynamics. By integrating experimental observations with computational predictions, MD simulations offer a deeper understanding of the underlying mechanisms driving nanoparticle self-assembly, thereby facilitating the rational design of advanced materials with tailored properties for diverse applications ranging from catalysis to drug delivery.

Keyphrases: Biomolecules, energy landscapes, molecular dynamics simulations

@booklet{EasyChair:12159,
  author    = {Jane Elsa and Selim Basit},
  title     = {MD Simulations of Nanoparticle Self-Assembly: from Aggregation to Morphological Evolution},
  howpublished = {EasyChair Preprint 12159},
  year      = {EasyChair, 2024}}