Projects per year
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.
|Number of pages||8|
|Early online date||20 Nov 2020|
|Publication status||Published - 20 Nov 2020|
Bibliographical noteThis journal is © The Royal Society of Chemistry 2020.
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- 2 Finished
CCP-BioSim: Biomolecular Simulation at the Life Sciences Interface
1/07/15 → 30/04/21
Reactive Scattering Dynamics at the Gas-Liquid Interface: Bridging the Gap between the Gas-Phase and Solution
Glowacki, D. R.
30/06/15 → 29/06/21
HPC (High Performance Computing) Facility
Sadaf R Alam (Manager), Steven A Chapman (Manager), Polly E Eccleston (Other), Simon H Atack (Other) & D A G Williams (Manager)
Professor Craig P Butts
- School of Chemistry - Professor of Structural and Mechanistic Chemistry
- Supramolecular and Mechanistic Chemistry
- Spectroscopy and Dynamics
Person: Academic , Member