A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids
Manuel Cordova, Edgar A. Engel, Artur Stefaniuk, Federico M. Paruzzo, Albert Hofstetter, Michele Ceriotti, Lyndon Emsley
Abstract
H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.
Topics & Concepts
Chemical shiftBottleneckDensity functional theoryBenchmark (surveying)MoleculeMaterials scienceChemistryBiological systemComputational chemistryComputer sciencePhysical chemistryOrganic chemistryEmbedded systemBiologyGeodesyGeographyAdvanced NMR Techniques and ApplicationsSolid-state spectroscopy and crystallographyX-ray Diffraction in Crystallography