A deep neural network method is developed to learn the density functional theory (DFT) Hamiltonian as a function of atomic structure. This approach provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to investigate large-scale materials, such as twisted van der Waals materials.
Your institute does not have access to this article
Access options
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
$29.99
monthly
Subscribe to Journal
Get full journal access for 1 year
$99.00
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Buy article
Get time limited or full article access on ReadCube.
$32.00
All prices are NET prices.
References
Jones, R. O. Density functional theory: Its origins, rise to prominence, and future. Rev. Mod. Phys. 87, 897 (2015). A review article that presents the development and important applications of DFT.
Butler, K. T. et al. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). A review article that presents recent progress in machine learning for computational materials science and chemistry.
Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021). A review article that presents applications of machine learning methods in the prediction of total energy to perform efficient molecular dynamics simulations.
Carr, S. et al. Electronic-structure methods for twisted moiré layers. Nat. Rev. Mater. 5, 748–763 (2020). A review article that summarizes and compares different theoretical methods used to study twisted materials.
Zhang, L. et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018). An article that introduces a deep-learning approach to predicting the many-body potential.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Li, H. et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00265-6 (2022).
Rights and permissions
About this article
Cite this article
Improving the efficiency of ab initio electronic-structure calculations by deep learning. Nat Comput Sci 2, 418–419 (2022). https://doi.org/10.1038/s43588-022-00270-9
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-022-00270-9
