COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS – Using data science and materials simulations to control the corkscrew magnetism of MnAu₂. – Glasbrenner

March 19, 2018 @ 4:30 pm – 5:45 pm
Exploratory Hall, Room 3301
Matthias Renz


James Glasbrenner, Assistant Professor
Department of Computational and Data Sciences
George Mason University

Using data science and materials simulations to control the corkscrew magnetism of MnAu₂

Monday, March 19, 4:30-5:45
Exploratory Hall, Room 3301


Materials occupy a foundational role in our society, from the silicon-based chips in our smartphones to the metals used to manufacture automobiles and construct buildings. The sheer variety in materials properties enables this wide range of use, and studying the atoms that bond together to form solids reveals the microscopic origin behind these properties. Remarkably, many properties can be traced to the behavior of and interaction between electrons, and computational simulations such as density functional theory calculations are used to study the features and macroscopic effects of this electronic structure. This computational approach can be further enhanced through recent advances in data science, which provide powerful tools and methods for analyzing and modeling data and for handling and storing large datasets.

In this talk, I will: 1) introduce the basic concepts of computational materials science and density functional theory in an accessible manner, and 2) present calculations on the material MnAu₂ where I use density functional theory and modeling to analyze its magnetic properties. The MnAu₂ structure is layered and its magnetic ground state forms a noncollinear corkscrew that rotates approximately 50° between neighboring manganese layers. Using the results of my calculations, I will explain the electronic origin of this corkscrew state and how to control its angle using external pressure and chemical substitution. In addition to discussing the electron physics, I will place a particular emphasis on the connection between data science and how modeling was used to analyze and interpret the density functional theory calculations. This will include a new, critical reexamination of my model fitting procedure using cross-validation and feature selection techniques, which will formally test the underlying assumptions I made in the original study.