ShiftML was developed jointly by the Ceriotti and Emsley groups at the EPFL. ShiftML uses a machine learning framework to predict chemical shifts in solids which is based on capturing the local environments of individual atoms. ShiftML has been trained on 2000 structures taken from the Cambridge Structural Database (CSD), chosen to be as diverse as possible. On a separate test set of 500 randomly selected structures it predicts chemical shifts of molecular solids to an accuracy with respect to GIPAW DFT of (RMSE) 0.49 ppm for 1H, 4.3 ppm for 13C, 13.3 ppm for 15N, and 17.7 ppm for 17O and with an R2 of 0.97 for 1H, 0.99 for 13C, 0.99 for 15N, and 0.99 for 17O.
Please read and cite the following: Paruzzo, F. M., Hofstetter, A., Musil, F., De, S., Ceriotti, M., & Emsley, L. (2018). Chemical shifts in molecular solids by machine learning. Nature Communications, 9(1), 4501.
This is the first release of ShiftML, with limited functionality. This version predicts chemical shifts of structures containing only 1H, 13C, 15N and 17O.
It has only been tested for input structures in which all atomic coordinates are fully relaxed with DFT.
Version 1.02 Beta introduces sparsified kernels, which reduces the calculation time and memory requirement while maintining similar accuracy.
ShiftML takes as an input a crystal structure (in a number of different formats), and
ShiftML has so far been tested for DFT-optimized crystal structures of molecular solids containing 1H, 13C, 15N and 17O atoms. Paruzzo, et al..
Version 1.02 Beta implements a sparsified multi-scale-kernel version, which reduces the calculation time and memory requirement while maintining similar accuracy.
17.09.2018: Version 1.03 Beta
27.06.2018: Version 1.02 Beta
02.05.2018: Version 1.0 Beta
If you use this tool, please cite the following work:
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