Berlin 2024 – scientific programme
Parts | Days | Selection | Search | Updates | Downloads | Help
O: Fachverband Oberflächenphysik
O 82: Electronic Structure Theory I
O 82.3: Talk
Thursday, March 21, 2024, 11:00–11:15, MA 043
Surrogate models for the electron density and related scalar fields — Joseph Abbott1, •Raymond Chong1, Alan Lewis2, and Michele Ceriotti1 — 1Laboratory of Computational Science and Modeling (COSMO), IMX, École Polytechnique Fédérale de Lausanne, Switzerland — 2University of York, United Kingdom
The electron density is a central quantity in electronic structure calculations and a fundamental property of molecules and materials, allowing access to in principle any ground state electronic property. Density-functional theory (DFT) is an ab initio method that calculates the electron density of a system by solving self- consistently the Kohn-Sham equations. However, the cubic scaling in system size makes calculations for large systems intractable. As a complementary approach, machine learning (ML) surrogate models are being developed that directly predict the self-consistent electron density at a fraction of the cost of DFT, and with more favourable linear scaling. One framework in particular focuses on learning the coefficients of basis functions fitted to the real-space density using a local equivariant ML model.
An end-to-end pipeline for the training and prediction of the the electron density is presented here. Built on top of a modular and scalable software stack, the 'bypassing' of the Kohn-Sham equations to access the density of larger systems becomes increasingly possible. Furthermore, the framework can be applied to scalar field related to the electron density, opening the door to interesting applications such as the ML-driven imaging of scanning tunneling microscopy (STM).
Keywords: machine-learning; local-density-of-states; electron-density