MSE Virtual Seminar Series: Yaroslava G. Yingling, North Carolina State University


Optimization of structure and properties of ligand functionalized nanoparticles using molecular modeling and machine learning

Dr. Yaroslava G. Yingling, North Carolina State University

Dr. Yaroslava Yingling

Ligand-functionalized metal nanoparticles (NPs) are actively used in many applications, such as drug delivery, biosensing, catalysis, supramolecular chemistry and solar energy. The nature of the ligand determines the atomic structure, solubility, stability, electrochemical properties and functionality of NPs. Despite extensive research efforts in understanding the correlation between nanoparticle structure and resultant composite material properties, there has been no systematic studies focusing on the design of ligand chemistry toward optimization of a specific material property mainly due to complexity of underlying processes. Current modeling and simulation techniques are primarily limited to coarse-grained methods, which lack proper resolution for understanding these processes at the atomic level. Here I will discuss the development of methodologies for all-atom investigation of ligand functionalized metal and magnetic nanoparticles, which permit the detailed studies of the nanoparticles alignment and explicit interactions with the surrounding environment from the effect of ligand chemistry on self-assembly to interactions with other molecules, such as DNA and RNA, and the investigation of chain and ring shapes formed by cubic and spherical Fe3O4 nanoparticles under the influence of an external magnetic field. We then use a combination of high-throughput molecular dynamics simulations and data available from the literature and experiment to train machine learning algorithms to speed up the search for optimal ligand chemistry and optimization of nanoparticle properties.

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