Pushing the frontiers of density functionals by solving the fractional electron problem

By James Kirkpatrick, Brendan McMorrow, David H. P. Turban, Alexander L. Gaunt, J. Spencer, Alexander Matthews, A. Obika, L. Thiry, Meire Fortunato, D. Pfau and 7 more. Science, 2021

325 citations recorded by the source corpus.

Description Improving DFT with deep learning In the past 30 years, density functional theory (DFT) has emerged as the most widely used electronic structure method to predict the properties of various systems in chemistry, biology, and materials science. Despite a long history of successes, state-of-the-art DFT functionals have crucial limitations. In particular, significant systematic errors are observed for charge densities involving mobile charges and spins. Kirkpatrick et al. developed a framework to train a deep neural network on accurate chemical data and fractional electron constraints (see the Perspective by Perdew). The resulting functional outperforms traditional functionals on thorough benchmarks for main-group atoms and molecules. The present work offers a solution to a long-standing critical problem in DFT and demonstrates the success of combining DFT with the modern machine-learning methodology. —YS Deep learning solves a long-standing critical limitation of density functional theory in describing fractional charges and spins. Density functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise from the violation of mathematical properties of the exact functional. We overcame this fundamental limitation by training a neural network on molecular data and on fictitious systems with fractional charge and spin. The resulting functional, DM21 (DeepMind 21), correctly describes typical examples of artificial charge delocalization and strong correlation and performs better than traditional functionals on thorough benchmarks for main-group atoms and molecules. DM21 accurately models complex systems such as hydrogen chains, charged DNA base pairs, and diradical transition states. More crucially for the field, because our methodology relies on data and constraints, which are continually improving, it represents a viable pathway toward the exact universal functional.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf73d19779cfc09ac930db4808c0c9c042aaf60c