IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy. Issue 2 (28th November 2019)

• Record Type: Journal Article
• Authors: Gerrard, Will; Bratholm, Lars A.; Packer, Martin J.; Mulholland, Adrian J.; Glowacki, David R.; Butts, Craig P.
• Is Part Of: Chemical science. Volume 11:Issue 2(2020)
• Page Start: 508
• Page End: 515
• Publication Date: 2019-11-28
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c9sc03854j
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours. Abstract : The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) machine learning system provides an efficient and accurate method for the prediction of NMR parameters from 3-dimensional molecular structures. Here we demonstrate that machine learning predictions of NMR parameters, trained on quantum chemical computed values, can be as accurate as, but computationally much more efficient (tens of milliseconds per molecular structure) than, quantum chemical calculations (hours/days per molecular structure) starting from the same 3-dimensional structure. Training the machine learning system on quantum chemical predictions, rather than experimental data, circumvents the need for the existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and stereoisomerism.