Abstract

An essential component of materials research is the use of simulations based on density functional theory (DFT), which imposes severe limitations on the size of the system under study. A promising development in recent years is the use of machine learning (ML) methodologies to train surrogate models with DFT data to predict quantum-accurate results for larger systems. Many successful ML models have been created to predict higher-level DFT results such as the total potential energy and atomic forces, and initial steps have been taken to create machine-learning based ML methodologies that can predict fundamental DFT outputs such as the charge density, wave functions and corresponding energy levels. In this talk, I will present our latest results using deep learning neural networks to learn and predict the electronic structure of a large variety of carbon allotropes, and its extension to hydrocarbons.

B. G. del Rio, C. Kuenneth, H. D. Tran, and R. Ramprasad, J. Phys. Chem. A 124 , 9496-9502 (2020).