DML-NMR

Delta-Machine Learning Nuclear Magnetic Resonance (DML-NMR) is a set of python scripts to rapidly predict isotropic chemical shieldings at the PBE0/6-311+G(2d,p) level of theory. DML-NMR relies on calculating an inexpensive chemical shielding using Gaussian09 (or newer) in combination with these python scripts.

This project was developed by Pablo Unzueta and Gregory Beran at UC Riverside. Visit our group website for our full publication list.

Installation

Dependencies

• tensorflow >= 2.0
• numpy >= 1.13.3
• Sklearn >= 0.23.2
• pandas >= 1.1.3
• torchani >= 2.2

DML-NMR python scripts are written for python3.7 or newer.

User Installation

Source code can be downloaded with the following command:

git clone https://github.com/pablo-unzueta/dml-nmr

Usage

Examples

Using DML-NMR is quite simple. The code simply acts on all Gaussian09 output (.log) files in a directory. All options are controlled in the example config.yml file. Once all options are set, simply type:

python main.py

This will produce .dml files for each corresponding .log file containing the ML PBE0/6-311+G(2d,p) shielding predictions in the directory specified by config.yml. In the .dml file, the first column is the prediction, and the second contains the standard deviation of the ensemble predictions. Please note that the only accepted atom types are C H N and O. The advantage of using an ensemble net allows one to examine the uncertainty between the individual members to assess the quality of the prediction. Please refer to our paper to examine the 95% confidence intervals per atom type.

Currently, we have trained nets for following density functionals:

• LDA
• PBE
• PBE0

And basis sets:

• STO-3G
• 6-31G

We suggest using PBE0/6-31G for the baseline calculation and have included the ensemble uncertainties below for this model per atom type. If the NMR calculations become a significant bottleneck, one can opt of the cheaper PBE/6-31G calculations which are roughly 30% cheaper as discussed in the paper.

Carbon Shielding Error 95% Confidence Interval

Stdev	PBE0/6-31G (ppm)	PBE/6-31G (ppm)
< 0.25	1.0	1.0
0.25 - 0.5	1.4	1.5
0.5 - 0.75	1.9	2.3
0.75 - 1.0	2.5	2.8
> 1.0	3.2	4.0

XYZ to AEV Conversion

The xyz to aev file conversion is carried out using the torchani python package. It is highly recommended to only carry out this process once since generating AEV's can be time consuming for a large quantity of files. Once the code has been ran once, please modify config.yml to the following:

reprocess_aev: False

Citation

Please cite if using this software:

@ARTICLE{Unzueta2021-rx,
  title    = "Predicting Density Functional {Theory-Quality} Nuclear Magnetic
              Resonance Chemical Shifts via {$\Delta$-Machine} Learning",
  author   = "Unzueta, Pablo A and Greenwell, Chandler S and Beran, Gregory J O",
  journal  = "J. Chem. Theory Comput.",
  volume   =  17,
  number   =  2,
  pages    = "826--840",
  month    =  feb,
  year     =  2021,
  language = "en",
  issn     = "1549-9618, 1549-9626",
  doi      = "10.1021/acs.jctc.0c00979"
}