The Paper “Quantum chemical accuracy from density functional approximations via machine learning” by Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Müller and Kieron Burke was published in Nature Communications (2020)11:5223. In this paper, the authors from the Machine Learning group at TU Berlin, New York University and University of California leveraged Machine Learning to calculate coupled-cluster energies from DFT densities, reaching much better quantum chemical accuracy on test data than achieved with previous available methods. Moreover, their approach significantly reduced the amount of training data required.
THE PAPER IN DETAIL:
Authors:
Mihail Bogojeski (TU Berlin), Leslie Vogt-Maranto (New York University), Mark E. Tuckerman (New York University), Klaus-Robert Müller (TU Berlin, Korea University, MPI), Kieron Burke (University of California)
Abstract:
Kohn-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.
Publication:
NATURE COMMUNICATIONS | (2020)11:5223 | https://doi.org/10.1038/s41467-020-19093-1
Authors:
Mihail Bogojeski (TU Berlin), Leslie Vogt-Maranto (New York University), Mark E. Tuckerman (New York University), Klaus-Robert Müller (TU Berlin, Korea University, MPI), Kieron Burke (University of California)
Abstract:
Kohn-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.
Publication:
NATURE COMMUNICATIONS | (2020)11:5223 | https://doi.org/10.1038/s41467-020-19093-1