IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy

Will Gerrard, Lars A Bratholm, Martin J Packer, Adrian J Mulholland, David R Glowacki, Craig P Butts*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

63 Citations (Scopus)
179 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)508-515
Number of pages8
JournalChemical Science
Volume11
Issue number2
Early online date20 Nov 2020
DOIs
Publication statusPublished - 20 Nov 2020

Bibliographical note

This journal is © The Royal Society of Chemistry 2020.

Keywords

  • physics.chem-ph

Fingerprint

Dive into the research topics of 'IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy'. Together they form a unique fingerprint.

Cite this