Abstract: The properties of materials are highly dependent on their structures, which include morphologies, grain boundaries, phases, atomic structures, Etc. In particular, the atomic structures determine the limit of their properties, which are the unique characteristics of each material. However, predicting physical properties and understanding their origins demand accurate electronic structure and atomic structure calculations, which require a lot of computational resources. Over the past few decades, computational power has been tremendously improved, enabling atomistic simulations on a reasonable length and time scale to answer such questions. Accordingly, high-throughput calculations, data mining, and machine-learning(ML) solutions have been becoming mainstream in materials research.
In this talk, we introduce our effort to understand the atomic structure and materials property relationship to practical application in the next-generation battery and semiconductor materials using atomistic simulation tools such as; ab initio Density Functional Theory, Classical Molecular Dynamics, and ML algorithms. First, we present newly developed active materials for next-generation rechargeable battery applications, which include novel cathode and electrolyte materials. And then, we exhibit the structure-property relationship to describe dielectric properties using the ML algorithm and intuition of fundamental physics. These examples will sufficiently show atomistic simulation’s practical application to materials research.
Biographical Sketch: Dr. Shin is a Sr. Staff Engineer and Project Leader in the Advanced Materials Lab at Samsung Semiconductor Inc (SSI). He studies theoretical and computational materials science through computational modeling, simulation, and Artificial-intelligence driven materials discoveries for energy harvesting, conversion, and storage materials.
Dr. Shin received his Ph.D. in Materials Science and Engineering at Boston University in 2012 and held a Chemistry Postdoc Fellow position in the Energy Storage and Distributed Resources Division at Lawrence Berkeley National Laboratory. He worked as Research Engineer at Samsung Research America before joining SSI.