Machine learning as a solution to the electronic structure problem

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 deep-learning based ML methodologies that can predict fundamental DFT outputs such as the charge density, wave functions and corresponding energy levels [1]. Here, we explore the applicability of this latter methodology using convolutional and recurrent neural networks to learn and predict the electronic charge density and the density of states of carbon, for a large variety of allotropes spanning from metallic to insulating behavior. Further improvements to the speed, accuracy and versatility of this DFT-emulation methodology will also be presented.

[1] A. Chandrasekaran, D. Kamal, R. Batra, C. Kim, L. Chen, and R. Ramprasad, Npj Comput. Mater. 5, 22 (2019)