10.1021/acs.jctc.7b00677.s001
Joshua
D. Hartman
Joshua
D.
Hartman
Ashwin Balaji
Ashwin
Balaji
Gregory J. O. Beran
Gregory
J. O. Beran
Improved Electrostatic Embedding for Fragment-Based
Chemical Shift Calculations in Molecular Crystals
American Chemical Society
2017
point charges
embedding treatment
13 C
Electrostatic Embedding
15 N ones
cluster-based 1 H
17 O chemical shifts
γ- polymorph
embedding environment
14 N
embedding model results
Fragment-Based Chemical Shift Calculations
17 O chemical shift predictions
SCRMP-embedded NMR chemical shift predictions
crystal property calculations
ion method
lattice polarization
chemical shielding tensors
gauge-including projector
GIPAW
performance
PBE 0
Molecular Crystals Fragment-based methods
Such methods
oxygen chemical shifts
density functionals
Madelung field effects
2017-11-14 21:43:54
Journal contribution
https://acs.figshare.com/articles/journal_contribution/Improved_Electrostatic_Embedding_for_Fragment-Based_Chemical_Shift_Calculations_in_Molecular_Crystals/5601595
Fragment-based
methods predict nuclear magnetic resonance (NMR)
chemical shielding tensors in molecular crystals with high accuracy
and computational efficiency. Such methods typically employ electrostatic
embedding to mimic the crystalline environment, and the quality of
the results can be sensitive to the embedding treatment. To improve
the quality of this embedding environment for fragment-based molecular
crystal property calculations, we borrow ideas from the embedded ion
method to incorporate self-consistently polarized Madelung field effects.
The self-consistent reproduction of the Madelung potential (SCRMP)
model developed here constructs an array of point charges that incorporates
self-consistent lattice polarization and which reproduces the Madelung
potential at all atomic sites involved in the quantum mechanical region
of the system. The performance of fragment- and cluster-based <sup>1</sup>H, <sup>13</sup>C, <sup>14</sup>N, and <sup>17</sup>O chemical
shift predictions using SCRMP and density functionals like PBE and
PBE0 are assessed. The improved embedding model results in substantial
improvements in the predicted <sup>17</sup>O chemical shifts and modest
improvements in the <sup>15</sup>N ones. Finally, the performance
of the model is demonstrated by examining the assignment of the two
oxygen chemical shifts in the challenging γ-polymorph of glycine.
Overall, the SCRMP-embedded NMR chemical shift predictions are on
par with or more accurate than those obtained with the widely used
gauge-including projector augmented wave (GIPAW) model.