Submitted Abstract
In the last decades, there have been significant efforts to develop theoretical methods to calculate positron binding energies accurately [1,2]. Nevertheless, most of them are extremely computationally expensive, limited for small polar molecules. From a density functional theory perspective, there are just a few reports of positron-electron correlation functionals.In this Ph.D. project, we aim to attain a robust understanding of positrons interacting with small and large molecular systems, starting from first principles of quantum mechanics. This will require developing a hierarchical methodological based on a systematic inclusion of electron-electron and electron-positron correlation effects. Our systematic strategy can be roughly summarized as:- Development of asymptotic long-range model potentials to describe the quantum-mechanical correlation between positrons and electrons in simple molecules and solids, [3]- Development of short-range positronic/electronic density functionals in analytical or machine-learning form, [4]- Unification of semi-local functionals and long-range model potentials for positrons interacting with large molecules.Consequently, this method will be incorporated with a machine learning model to enable the direct construction of molecular force fields [5], necessary to perform molecular dynamics simulations of positron interacting with molecular systems. The development of this methodology will allow the study of complex systems of physical and chemical interest.References:[1] G. F. Gribakin, J. A. Young, and C. M. Surko. Reviews of Modern Physics, 82(3):2557, 2010.[2] A. Reyes, F. Moncada, and J. Charry. International Journal of Quantum Chemistry, 119(2):1, 2019.[3] J. Hermann, R. A. DiStasio, and A. Tkatchenko. Chemical Reviews, 117(6):4714, 2017[4] J. C. Snyder, M. Rupp, K. Hansen, K. Robert Müller, and K. Burke. Physical Review Letters, 108(25):1, 2012[5] S. Chmiela, K. R. Müller, K. T. Schütt, A. Tkatchenko, and F. Arbabzadah. Nature Communications, 8:13890, 2017.