Accelerated discovery of nanomaterials using molecular simulation

Abstract

Next-generation nanotechnology demands new materials and devices that are highly efficient, multifunctional, cost-effective and environmentally friendly. The need to accelerate the discovery of new materials therefore becomes more pressing than ever. In this regard, among widely regarded fabrication techniques are self-assembly and directed assembly, which have attracted increasing interest as they are applicable to a wide range of materials ranging from liquid crystals to semiconductors to polymers and biomolecules. The fundamental challenges to these bottom up techniques are to design the assembling building blocks, to tailor their interactions and to engineering the assembly pathways towards desirable structures. We will demonstrate how molecular simulation, particularly Molecular Dynamics and Monte Carlo methods, has been a powerful tool for tackling these fundamental challenges. We will review through selected examples the insights from simulation that help explain the roles of the shape of the building blocks and their interactions in determining the morphology of the assembled structures. We will discuss testable predictions from simulation that serve to motivate future experimental studies. Aided by data mining techniques and computing capacities, the cooperative efforts between computational and experimental investigations open new horizons for accelerating the discovery of new materials and devices. We will address the theoretical background of self-assembly studies; simulation methods and data analysis tools commonly used in this highly multidisciplinary research area.