Event
MSE Seminar: Dr. Susan Sinnott, Penn State University
Wednesday, November 8, 2023
3:30 p.m.
Room 2108 Chemical and Nuclear Engineering Building
Sherri Tatum
301 405 5240
statum12@umd.edu
Atomistic Simulations to Advance Surface Science
Abstract: Computational methods are useful tools in the investigation of atomic and molecular dynamics and associated mechanisms at surfaces and interfaces. Physics-based classical potentials are a class of computational method that is useful for use in classical atomistic simulations of systems made up of thousands to many billions of atoms. These potentials consist of parameterized functions that capture aspects of atomic and molecular interactions within these material systems.
The focus of this presentation is on the third-generation charge-optimized many body (COMB3) potential. COMB3 was developed to enable an atomic-scale description of systems that include combinations of metallic, ionic, and covalent bonding under the same framework. The framework enables the system to determine the charge state of an atom or ion and manifest the physically appropriate type(s) and strength of local bonding as a function of environment correctly and autonomously. The framework further includes a combination of atomic-specific, bond-specific, bond-angle-specific parameters; the former is the same regardless of material, and only new bond-specific and bond-angle-specific parameters are required to extend existing elements to new compounds.
This presentation will provide an overview of the COMB3 potential and illustrate its utility in the study of water-metal surface and nanoparticle interactions, the examination of carbon nanoparticle-metal surface interactions, and the mechanisms associated with the growth of metal films on metallic and oxide substrates.
Bio: Susan Sinnott, Ph.D is the Head and Professor of the Department of Materials Science and Engineering, as well as a Professor of the Department of Chemistry at Pennsylvania State University. She's also the Editor-in-Chief of the journal Computational Materials Science.
