NMR shift prediction from small data quantities

04/06/2023
by   Herman Rull, et al.
0

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model which is able to achieve good results with comparatively low amounts of data. We show this by predicting 19F and 13C NMR chemical shifts of small molecules in specific solvents.

READ FULL TEXT
research
06/07/2019

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations

Recent studies illustrate how machine learning (ML) can be used to bypas...
research
10/07/2021

Predicting Chemical Hazard across Taxa through Machine Learning

We apply machine learning methods to predict chemical hazards focusing o...
research
08/01/2018

Towards Machine Learning on data from Professional Cyclists

Professional sports are developing towards increasingly scientific train...
research
12/15/2021

Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods

Predicting the success of startup companies is of great importance for b...
research
05/18/2020

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure

Probability density function (PDF) based turbulent combustion modelling ...
research
10/12/2021

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning

Stereoselective reactions (both chemical and enzymatic reactions) have b...
research
09/09/2022

SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients

The availability of property data is one of the major bottlenecks in the...

Please sign up or login with your details

Forgot password? Click here to reset