# ANMR¶

ANMR requires the files:

• coord, turbomole data group

• anmr_nucinfo (human readable), written by crest

• information on which nuclei can interchange in your molecule (e.g. the hydrogen atoms in a methlygroup)

• = information on chemical and magnetical equivalent atoms

• anmr_rotamer (machine readable), written by crest

• information on the rotamers detected by crest

• anmr_enso, written by enso

• information on the contributing conformers, the Boltzmann weight and all contributions to free energy

• .anmrrc, written by enso

• anmr searches for the .anmrrc file first in the folder of execution and if not found in the home directory of the user

• The file contains the reference-shieldings of e.g TMS to convert the calculated shielding to shifts

• the folders with the property calculations of the conformers

• e.g. CONF1/NMR

Example .anmrrc file:

7 8 XH acid atoms
ENSO qm= TM mf= 300 lw= 1.0  J= on S= on
TMS[chcl3] pbe0[COSMO]/def2-TZVP//pbeh-3c[DCOSMO-RS]/def2-mSVP
1  31.786    0.0    1
6  189.674   0.0    0
9  182.57    0.0    0
15 291.9     0.0    0


The first line in .anmrrc informs anmr to ignore all protons bound to nitrogen (N=7) and oxygen (O=8). If you want to calculate protons bound to oxygen or nitrogen, simply remove the corresponding number, but leave the rest of the line intact! The next line starting with ENSO informs ANMR that the property calculation was performed by TM = TURBOMOLE (or ORCA = ORCA). The mf= 300 informs ANMR that the magnetic frequency of the NMR spectrometer is set to 300 MHz. The lw (linewidth for plotting) is 1.0 and J (couplings) and S (shieldings) are to be evaluated. If S= off then ANMR will terminate after calculating and averaging the shifts of the molecule under consideration. The next line explains how the reference shieldings are calculated: in this case the reference molecule is tetramethylsilane in chloroform and the shielding is calculated with PBE0/def2-TZVP + COSMO on PBEh-3c + DCOSMO-RS geometries.

The following lines contain the data on [atomic number] [calculated shielding valule of the reference molecule] [experimental shift] [active or not].

The lines show the reference shieldings for hydrogen (1), carbon (6) fluor (9) and phosphorus (15). The third number within the last four lines is 0.0 and can be used to adjust the shift of the reference (e.g. to the experimental shift). The last number in the last four lines can either be 1 or 0 and this switches the ‘element on or off’ for the spectrum calculation.

Example anmr_enso file:

ONOFF NMR CONF BW      Energy     Gsolv    RRHO
1     1   1    0.10042 -354.38939 -0.00899 0.22109
1     2   2    0.32452 -354.39034 -0.00899 0.22093
1     3   3    0.57506 -354.39287 -0.00902 0.22295


The file anmr_enso is written by the enso program and contains information on the conformers, which folder they are in, the Boltzmann weight, energy, solvation and thermostatistical contribution to free energy. The first number in the three last lines indicates to ANMR if the conformer is to be considered (1) or not (0). If one conformer is not considered (or more) anmr program internally recalculates the Boltzmann weights based on the free energies from the anmr_enso file.

Usage of anmr:

First of all: the spin problem is of $$2^{N}$$ complexity! Depending on the size of the maximalspinsystem (mss) the program might use a lot of RAM! If this is the case, run anmr with a decreased spinsystem size:

anmr -mss 12 > anmr.out 2> anmr.error &


anmr will then write a file called anmr.dat (which is quiet large). The file contains the information ppm vs intesity. This file can then be plotted with any plotting tool or our ‘nmrplot.py’.

To reduce the large size of the file you can remove entries which are close to zero with either this awk or python code:

head -1 anmr.dat > newanmr.dat
awk '($2 > 0.001){print$0}' anmr.dat >> newanmr.dat
tail -1 anmr.dat >> newanmr.dat

import numpy as np
data = np.genfromtxt('anmr.dat')
threshold = 0.001
data2 = data[np.logical_not(data[:,1] < threshold)]
data2 = np.insert(data2, 0, (data[0][0], threshold), axis=0)
data2 = np.insert(data2, len(data2), (data[-1][0], threshold), axis=0)
np.savetxt('newanmr.dat', data2, fmt='%2.5e' )