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			<titleStmt><title level='a'>Quantum chemical accuracy from density functional approximations via machine learning</title></titleStmt>
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				<publisher></publisher>
				<date>12/01/2020</date>
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				<bibl> 
					<idno type="par_id">10222055</idno>
					<idno type="doi">10.1038/s41467-020-19093-1</idno>
					<title level='j'>Nature Communications</title>
<idno>2041-1723</idno>
<biblScope unit="volume">11</biblScope>
<biblScope unit="issue">1</biblScope>					

					<author>Mihail Bogojeski</author><author>Leslie Vogt-Maranto</author><author>Mark E. Tuckerman</author><author>Klaus-Robert Müller</author><author>Kieron Burke</author>
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			<abstract><ab><![CDATA[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 (C              6              H              4              (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.]]></ab></abstract>
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