This GitHub repository contains the dataset accompanying the following manuscript:
- R. Atwi, Y. Chen, K.S. Han, V. Murugesan, N.N. Rajput. "An automated framework for predicting NMR chemical shifts of liquid solutions". (2021).
The dataset includes the Nuclear Magnetic Resonance (NMR) tensors and chemical shifts computed using our automated computational framework that combines density functional theory (DFT) with classical molecular dynamics (MD) simulations as implemented in the MISPR high-throughput infrastructure.
The lactam data corresponds to different conformers of penam β-lactams in chloroform solvent. The electrolyte data corresponds to solvation structures for: (1) a magnesium bis(trifluoromethanesulfonyl)imide Mg(TFSI)2 in dimethoxyethane (DME) solvent and (2) LiTFSI in DME which are common electrolyte system for Mg- and Li-based batteries, respectively. Solvation structures were extracted from MD simulations at 25 °C, 1 atm, and a salt:solvent ratio of 1:18.
The benchmarking study on the electrolyte system includes almost 1,000 DFT calculations. It involves combinations of the following functionals, basis sets, and implicit solvation models:
| Functional | Basis Set | Solvation Model |
|---|---|---|
| B3LYP | 6-31+G* | PCM |
| M06-2X | 6-311++G** | SMD |
| PBE1PBE | def2-TZVP | |
| wB97X |
If you have any questions, you can reach the author at the following e-mail: rasha.atwi@stonybrook.edu