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Vikas Tiwari

Publications and source records attributed to Vikas Tiwari.

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Electrostatic-driven Self-assembly of Janus-like Monolayer-protected Metal Nanoclusters

The generation of controlled microstructures of functionalized nanoparticles has been one of the crucial challenges in nanoscience and nanotechnology. Efforts have been made to tune ligand charge states that can affect the aggregation propensity and modulate the self-assembled structures. In this work, we modelled zwitterionic Janus-like monolayer ligand-protected metal nanoclusters (J-MPCs) and studied their self-assembly using atomistic molecular dynamics and advanced enhanced sampling simulations. The oppositely-charged ligands functionalization on two hemispheres of a J-MPC elicits asymmetric solvation, primarily driven by distinctive hydrogen bonding patterns in the ligand-solvent interactions. Electrostatic interactions between the oppositely charged residues in J-MPCs guide the formation of one-dimensional and ring-like self-assembled superstructures with molecular dipoles oriented in specific patterns. The pertinent atomistic insights into the intermolecular interactions governing the self-assembled structures of zwitterionic J-MPCs obtained from this work can be used to design a general strategy to create tunable microstructures of charged MPCs.

cond-mat.soft

Collective Variables for Crystallization Simulations -- from Early Developments to Recent Advances

Crystallization is one of the most important physicochemical processes which has relevance in material science, biology, and the environment. Decades of experimental and theoretical efforts have been made to understand this fundamental symmetry-breaking transition. While experiments provide equilibrium structures and shapes of crystals, they are limited to unraveling how molecules aggregate to form crystal nuclei that subsequently transform into bulk crystals. Computer simulations, mainly molecular dynamics (MD), can provide such microscopic details during the early stage of a crystallization event. Crystallization is a rare event that takes place in timescales much longer than a typical equilibrium MD simulation can sample. This inadequate sampling of the MD method can be easily circumvented by the use of enhanced sampling (ES) simulations. An ES method enhances the fluctuations of a system's slow degrees of freedom, called collective variables (CVs), by applying a bias potential, and thereby transforms the system from one state to the other within a short timescale. The most crucial part of an ES method is to find suitable CVs which often needs intuition and several trial-and-error optimization steps. Over the years, a plethora of CVs has been developed and applied in the study of crystallization. In this review, we provide a brief overview of CVs that have been developed and used in ES simulations to study crystallization from melt or solution. These CVs can be categorized mainly into four types: (i) spherical particle-based, (ii) molecular template-based, (iii) physical property-based, and (iv) CVs obtained from dimensionality reduction techniques. We present the context-based evolution of CVs, discuss the current challenges, and propose future directions to further develop effective CVs for the study of crystallization of complex systems.

cond-mat.soft