Hong Sun
About Hong Sun
Hong Sun is a Postdoctoral Researcher at Lawrence Livermore National Laboratory, specializing in physics-informed machine learning and the application of AI techniques in materials design. He has a background in multi-scale molecular modeling and has previously worked at Purdue University as a Research and Teaching Assistant and Postdoctoral Associate.
Work at Lawrence Livermore National Laboratory
Hong Sun has been employed as a Postdoctoral Researcher at Lawrence Livermore National Laboratory since 2021. In this role, he focuses on applying advanced techniques in machine learning to enhance the design and discovery of materials. His work involves developing physics-informed machine learning models for atomic modeling and force field development. The laboratory is known for its cutting-edge research in various scientific fields, and Hong contributes to projects that leverage artificial intelligence in materials science.
Education and Expertise
Hong Sun holds a Ph.D. in Mechanical Engineering from Purdue University, where he studied from 2015 to 2020. He also earned a Bachelor's degree in Materials Science and Engineering from Xi'an Jiaotong University, completing his studies from 2012 to 2015. Additionally, he studied Computer Science at Xi'an Jiaotong University for one year in 2011. His educational background underpins his expertise in multi-scale molecular modeling, diffusion models, and geometric deep learning.
Background
Hong Sun began his academic career at Xi'an Jiaotong University, where he completed his initial studies in Computer Science and later pursued a Bachelor's degree in Materials Science and Engineering. He then moved to Purdue University, where he worked as a Research and Teaching Assistant from 2015 to 2020. Following this, he served as a Postdoctoral Associate at Purdue for eight months before joining Lawrence Livermore National Laboratory.
Achievements
During his tenure at Purdue University, Hong Sun gained significant experience in multi-scale molecular modeling of battery materials and (semi)conducting polymers. He developed skills in uncertainty quantification and neural architecture search, which are essential for his current research. His work emphasizes the application of AI techniques to accelerate material discovery, showcasing his contributions to the field of materials science.