Kwangnam Kim
About Kwangnam Kim
Kwangnam Kim is a Postdoctoral Researcher specializing in computational materials science at Lawrence Livermore National Laboratory. His research focuses on materials discovery through high-throughput screening and machine learning techniques, with a particular interest in energy storage applications.
Work at Lawrence Livermore National Laboratory
Kwangnam Kim has been employed as a Postdoctoral Researcher at Lawrence Livermore National Laboratory since 2020. His work focuses on computational materials science, specifically in the areas of thermodynamics, kinetics, and transport properties of materials. This role allows him to contribute to advanced research initiatives and collaborate with other experts in the field.
Education and Expertise
Kwangnam Kim holds a Doctor of Philosophy (PhD) in Mechanical Engineering from the University of Michigan, where he studied from 2015 to 2020. Prior to this, he earned a Master of Science (MS) in Mechanical Engineering from Seoul National University, completing his studies from 2010 to 2012. He also obtained a Bachelor of Science (BS) in Mechanical Engineering from Hanyang University, where he studied from 2005 to 2010. His educational background provides a strong foundation for his research in computational materials science.
Background
Kwangnam Kim's academic journey began at Hanyang University, where he completed his undergraduate studies in Mechanical Engineering. He then advanced his education with a Master's degree at Seoul National University. Following his Master's, he pursued a PhD at the University of Michigan, focusing on areas critical to materials science. Before his current role, he worked as a Junior Researcher at Mando Corp. in Korea from 2012 to 2015.
Research Focus and Interests
Kwangnam Kim specializes in computational materials science, with a particular emphasis on thermodynamics, kinetics, and transport properties of materials. His research interests include the development of materials and systems for energy storage applications. He employs high-throughput screening and machine learning techniques in his work, contributing to the discovery of new materials.