(Invited) Thermal Transport in Multi-component Nanoparticle Superstructures

Abstract

Engineering nanoscale structures and interfaces through self-assembly of quantum dots and nanoparticles is a powerful approach in material design and processing. Nanoscale building blocks, with a variety of shapes, sizes, and compositions, can self-assemble into different structures with desirable electronic, photonic, and phononic properties. Recent advances in synthesis and characterization of complex nano-scale units has provided a large library of multi-component building blocks that can significantly expand the current scope of colloidal self-assembly. However, the complexity of these building blocks makes it significantly harder to predict the resulting self-assembled structures and their transport properties. Therefore, developing predictive tools based on computational and machine learning approaches becomes a necessity for successful implementation of new material design procedures.In this work, we utilize a combination of Molecular Dynamics simulations and Machine Learning-based approaches to investigate the self-assembly process of multi-component nanoparticles. We establish a computational framework to help identify feasible multi-component superstructures, and investigate various designs as a function of shape and size of the building blocks. Additionally, our approach transforms the high-dimensional energy landscapes of nanoparticles into low-dimensional parameter spaces by using Manifold Learning techniques. This illustration of the phase space compresses the full energy profile of the system into a single snapshot that contains important information regarding the assembly process and allows the comparison of the behavior of constructs with different geometric features in the same abstract space. This approach also facilitates the discovery of intuitive self-assembly pathways based on the energy footprint of nanoparticles.Furthermore, we develop a computational framework to predict transport properties of the identified multi-component superstructures. Band structure calculations are performed to capture the influence of key parameters such as building block size/shape on the photonic and phononic properties of various structural designs. Finally, we present a Genetic Algorithm (GA)-based platform to implement an inverse material-design process where superstructures with desired transport properties are obtained from the available building blocks. Our results provide new insight into designing multi-component nano-scale structures and will help pave the way for developing powerful tools for material discovery and identification.