Abstract
AbstractKohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol−1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol−1) on test data. Moreover, density-based Δ-learning (learning only the correction to a standard DFT calculation, termed Δ-DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ-DFT is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)2) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.
Funder
MSIP | Institute for Information and communications Technology Promotion
Deutsche Forschungsgemeinschaft
United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference116 articles.
1. Rupp, M., Tkatchenko, A., Müller, K.-R. & von Lilienfeld, O. A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).
2. Montavon, G. et al. Learning invariant representations of molecules for atomization energy prediction. Adv. Neural. Inf. Process. Syst. 25, 440–448 (2012).
3. Montavon, G. et al. Machine learning of molecular electronic properties in chemical compound space. N. J. Phys. 15, 095003 (2013).
4. Botu, V. & Ramprasad, R. Learning scheme to predict atomic forces and accelerate materials simulations. Phys. Rev. B 92, 094306 (2015).
5. Hansen, K. et al. Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 6, 2326–2331 (2015).
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