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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust \u2018local\u2019 machine learning (ML) strategy capturing the effect of the neighborhood on an atom\u2019s \u2018near-sighted\u2019 property\u2014chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first-principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model, trained on 100k samples, accurately predicts isotropic shielding of 50k \u2018hold-out\u2019 atoms with a mean error of less than 1.9\u2009ppm. For the rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a \u0394-ML strategy, we quench the error below 1.4\u2009ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10\u201317 heavy atoms and drugs.<\/jats:p>","DOI":"10.1088\/2632-2153\/abe347","type":"journal-article","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T13:40:05Z","timestamp":1612532405000},"page":"035010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Revving up <sup>13<\/sup>C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules"],"prefix":"10.1088","volume":"2","author":[{"given":"Amit","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabyasachi","family":"Chakraborty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7288-9238","authenticated-orcid":false,"given":"Raghunathan","family":"Ramakrishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"mlstabe347bib1","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1021\/cr960017t","volume":"99","author":"Helgaker","year":"1999","journal-title":"Chem. 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