Stable and Accurate Orbital-Free DFT Powered by Machine Learning
This work addresses the accuracy gap in orbital-free DFT for computational chemistry, enabling more efficient calculations in large molecular systems, though it is incremental in applying machine learning to a known bottleneck.
The researchers tackled the challenge of approximating the exact energy functional in orbital-free density functional theory (DFT) by using rotationally equivariant atomistic machine learning, achieving chemical accuracy in energies for QM9 organic molecules relative to Kohn-Sham references and converging to meaningful electron densities.
Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to the density. While decades of theoretical work have produced increasingly faithful approximations to this elusive exact energy functional, their accuracy is still insufficient for many applications, making it reasonable to try and learn it empirically. Using rotationally equivariant atomistic machine learning, we obtain for the first time a density functional that, when applied to the organic molecules in QM9, yields energies with chemical accuracy relative to the Kohn-Sham reference while also converging to meaningful electron densities. Augmenting the training data with densities obtained from perturbed potentials proved key to these advances. This work demonstrates that machine learning can play a crucial role in narrowing the gap between theory and the practical realization of Hohenberg and Kohn's vision, paving the way for more efficient calculations in large molecular systems.