Materials science through electronic structure and atomistic modelling
Abstract
The Applied Physics group at LTU is a part of the Division of Materials Science, and we study the properties materials and their interfaces using electronic structure calculations, atomistic modelling and machine learning. The materials development in the information society is constantly moving in the direction of thinner layers and nano-structured materials, and measurements at smaller scales, in what is termed nanotechnology. In this regime information on the atomic scale is desirable and necessary.
We use SNIC compute projects (NAISS 2023/3-31) to model material properties on two levels of theory: electronic structure theory and atomistic theory, such as molecular mechanics molecular dynamics. The majority of our data will be input and output data from simulations employing either of these two levels of theory. We expect to generate large amount of data, both in terms of the number of files and the total size, which we analyze using advanced techniques. To study the binding of materials, whether physical binding or chemical bonding, we employ electron localization function (ELF) analysis. [1-5] We analyze relevant energy barriers of different processes such as reaction, diffusion and dissociation using the nudged elastic band method (NEB) and investigate the distribution of charges in materials via Bader analysis. [4,6] From our MD simulations we anticipate a large volume and number of required output files for the system analysis. By using the reactive force field (ReaxFF), we can accurately evaluate the formation and dissociation of bonds between specific atomic species. Additionally, by studying the output of formed/dissociated molecular species at each time step, we can gather data on the kinetics of the chemical reactions [7] within the system. One of our most data driven research projects is the creation of machine learning force fields (MLFFs). [8,9] These are trained on data generated from electronic structure theory calculations and can be used to model materials at high accuracy but low computational cost.
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8. D. Hedman, T. Rothe, G. Johansson, F. Sandin, J. A. Larsson, Y. Miyamoto, Carbon Trends 3 (2021) 100027.
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