Yan Wang: Atomistic modeling and deep-neural-network molecular dynamics
Title
Atomistic modeling and deep-neural-network molecular dynamics
Mentor
Department
Biosketch
Yan Wang, Ph.D., joined the Department of Mechanical Engineering at the University of Nevada, Reno as an assistant professor in 2016. He received his Ph.D. in mechanical engineering from Purdue University, West Lafayette, in 2016. Prior to his study at Purdue, he received a B.Sc. degree in measurement and control from the Department of Precision Instruments at Tsinghua University, Beijing, in 2010. He also received a bachelor's degree in economics from Tsinghua University, Beijing, in 2010. He is the recipient of the prestigious ACS PRF Doctoral New Investigator Award (2019) and the NSF CAREER Award (2021).
Project overview
The Pack Research Experience Program (PREP) student would conduct research on one of the two projects listed below.
Project 1: Atomistic modeling of thermal transport in two-dimensional materials towards enhanced thermal management of next-generation electronics
The PREP student will receive training in molecular dynamics (MD) simulations, a powerful computational technique that directly models the motion of atoms and molecules to study physical phenomena at the nanoscale. The project focuses on investigating thermal transport in two-dimensional (2D) materials. The student will also gain experience with MATLAB and/or Python for constructing atomic structures and analyzing simulation data. Under the guidance of a Ph.D. student and the faculty mentor, the student will develop atomic models of 2D materials such as graphene and ReSâ‚‚, and perform simulations of their thermal transport behavior under conditions of high pressure and applied tensile/compressive strain. The PREP student will perform literature review of thermal transport properties of 2D materials. The outcomes of this research will contribute to advancing thermal management strategies for next-generation semiconductor devices built on emerging 2D materials.
Project 2: Deep-neural-network molecular dynamics investigation of thermal transport across solid-liquid interface
The PREP student will receive training in deep-neural-network molecular dynamics (DNN-MD) simulations, an advanced computational technique that leverages GPU acceleration to directly model the motion of atoms and molecules with high accuracy. The project focuses on investigating thermal transport across solid–liquid interfaces, such as those between graphene and water. The student will also gain experience with MATLAB and/or Python for building atomic structures and analyzing simulation data. Working under the mentorship of a Ph.D. student and the faculty advisor, the student will construct atomic models of solid–liquid systems and perform simulations of interfacial thermal transport under ultrafast laser irradiation. The outcomes of this research will provide fundamental insights for advancing laser processing technologies, including the use of graphene flakes dispersed in water and solar-driven water vaporization or desalination.
Pack Research Experience Program information and application