Will Gerrard - Academia.edu (original) (raw)
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Papers by Will Gerrard
Chemical Science
The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) ma- chi... more The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) ma- chine learning system provides an efficient and accurate method for the prediction of NMR pa- rameters from...
Chemical Science, 2020
machine learning system provides an efficient and accurate method for the prediction of NMR param... more 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.
Chemical Science
The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) ma- chi... more The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) ma- chine learning system provides an efficient and accurate method for the prediction of NMR pa- rameters from...
Chemical Science, 2020
machine learning system provides an efficient and accurate method for the prediction of NMR param... more 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.