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.