# Calculating NMR-spectra with Wolfram Language

Posted 1 year ago
3049 Views
|
4 Replies
|
10 Total Likes
|

# Introduction

Perhaps there are already Mma-Procedures to calculate NMR-spectra, but I did not do a literature research.

I post a notebook to calculate NMR-Spectra of simple spin I = 1/2 systems.

The notebook comes in two parts.

Part one uses a spin-product function approach, where the spin-product function a, a ,...b is used as e.g. phi [ 1, 1, ,...., -1 ]. The Hamiltonian is constructed according to different mT-values and the spectrum is calculated.

Part two uses the "brute force" approach where all operators are mapped unto matrices (Kronecker-Products of individual matrix-operators) in the total spin-space. So here products of operators are matrix-products (in part 1 they are functions of functions) .Having the Hamiltonian and its Eigensystem the spectrum is calculated.

Note that the number of lines of even small systems are growing rapidly. So it may well be that there is not enough memory to cope with a system you would like to consider.

If you omit giving numbers to frequencies and coupling-constants you may get pure theoretical results. That works fine for two spins, but already for three spins - although here you will still get the Hamiltonian - very large outputs are generated, especially in the Eigensystems, So you should avoid that.

It seems to be not too complicated to modify the approach in part two to include spins with I > 1/2.

And certainly it is well possible to modify the code as to get an iterative procedure to fit data to spectra.

I am aware that there are "professional" systems to do all this, but I just wanted to see how it could be done in Mathematica.

# Part 1

Number of Spins

nsp = 3;


Input of Parameters

freqs = {372.2, 364.4, 342, 6.083, 5.8};
JJ = ( {
{0, .91, 17.9, 1, 1, 1},
{0, 0, 11.75, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 0, 1, 1},
{0, 0, 0, 0, 0, 1},
{0, 0, 0, 0, 0, 0}
} );
Do[\[Nu][i] = freqs[[i]], {i, 1, nsp}];
Do[J[i, k] = JJ[[i, k]], {i, 1, nsp - 1}, {k, i + 1, nsp}];


Basevectors

base = Apply[\[CurlyPhi],
IntegerDigits[#, 2, nsp] + 1 & /@ Table[j, {j, 0, 2^nsp - 1}] /.
2 -> -1, {1}]


mT - Values

mT = Table[-nsp/2 + j, {j, 0, nsp}]


Number of lines in the spectrum ( = Sum[Binomial[nsp, k] Binomial[nsp, k + 1], {k, 0, nsp - 1}], because only transitions between states of different mT's give lines on non-zero intensity )

numberoflines = Binomial[2 nsp, nsp - 1]


15

Rule for scalar products

rscp = {\[CurlyPhi][x__]^2 -> 1, \[CurlyPhi][x__] \[CurlyPhi][y__] ->  0}


SpinOperators

cs[a_, j_] := Module[{t}, t = a; b = t[[j]]; t[[j]] = -b; t]
Ix[v_, j_] := v/2 /. \[CurlyPhi][x___] :> \[CurlyPhi] @@ cs[{x}, j]
Iy[v_, j_] :=
I v/2 /. \[CurlyPhi][x___] :> {x}[[j]] \[CurlyPhi] @@ cs[{x}, j]
Iz[v_, j_] := v/2 /. \[CurlyPhi][x___] :> {x}[[j]] \[CurlyPhi][x]


Example

{Ix[\[CurlyPhi][4], 1], Ix[\[CurlyPhi][-1], 1], Iy[\[CurlyPhi][5], 1],
Iy[\[CurlyPhi][-1], 1], Iz[\[CurlyPhi][6], 1],
Iz[\[CurlyPhi][-1], 1]}


Hamiltonian - Matrixelements Subscript[H, i,j] for (sub-)base b

HH[b_, i_,  j_] := (Sum[\[Nu][m] b[[j]] Iz[b[[i]], m], {m, 1, nsp}] +
Sum[J[m, k] b[[
j]] (Ix[Ix[b[[i]], k], m] + Iy[Iy[b[[i]], k], m] +
Iz[Iz[b[[i]], k], m]), {m, 1, nsp - 1}, {k, m + 1, nsp}] //
Expand) /. rscp


Example

HH[base, 1, 1]


546.94

HH[base, 1, 2]


0

HH[base, 3, 5]


0.455

Spinfunctions according to mT - value

wf = Function[x, Select[base, Total[List @@ #] == 2 x &]] /@ mT


Hamilton - Submatrices

HSM[j_] := (nn = Length[wf[[j]]];
Table[HH[wf[[j]], m, n], {m, 1, nn}, {n, 1, nn}])
HSM[2];
% // MatrixForm


HSM[2] /. {\[Nu][10] -> -\[Nu], \[Nu][11] -> 0, \[Nu][12] -> \[Nu],
J[1, 2] -> J, J[1, 3] -> J, J[2, 3] -> J};
% // MatrixForm


Get Eigenstates [Congruent] all sets { freq, eigenvector } for different spin-states (mT values)

frevec[n_] := Module[{es},
es = Eigensystem[HSM[n]];
{#[[1]], #[[2]].wf[[n]]} & /@ Transpose[es]
]
eigenstates = frevec /@ Range[nsp + 1]


Operator for calculating relative intensities

IOP[x_] := Sum[Ix[x, n], {n, 1, nsp}]


Calculating a spectral line = difference of eigenvalues and intensity

line[a_, b_] := Module[{},
freq = Abs[a[[1]] - b[[1]]];
m2 = (Expand[a[[2]] IOP[b[[2]]]] /. rscp)^2;
{freq, Sqrt[m2]}]


Lorentzfunction

LF[x_, x0_, a_, h_] := Module[{},
If[h == 0, h = 1];
1/Sqrt[Pi] (a h/2)/(h^2/4 + Pi (x - x0)^2)]


Calculating the spectrum

spec = Table[0, {numberoflines}];
nL = 0;
Do[
lk = Length[eigenstates[[i]]];
lk1 = Length[eigenstates[[i + 1]]];
Do[
Do[
nL = nL + 1;
spec[[nL]] = line[eigenstates[[i, m]], eigenstates[[i + 1, n ]]],
{n, 1, lk1}
],
{m, 1, lk}
],
{i, 1, Length[eigenstates] - 1}];
normalizer = Max[Transpose[spec][[2]]];
bb = {.95 Min[Transpose[spec][[1]]], 1.05 Max[Transpose[spec][[1]]]};
spec = {#[[1]], #[[2]]/normalizer} & /@ spec;
spec
pl1 = ListPlot[spec, Filling -> Axis]


Show the spectrum with lines

pl2 = Plot[
Plus @@ (LF[x, #[[1]], #[[2]], 1.5] & /@ spec), {x, bb[[1]],
bb[[2]]}, PlotRange -> All, AxesOrigin -> {320, 0}]


For some physical reason spectra are recorded so that frequencies grow from right to left. So the plot is reversed and compared to the experimental spectrum ( see http://www.users.csbsju.edu/~frioux/nmr/Speclab4.htm ) which is given below the plot.

pl3 = Plot[
Plus @@ (LF[-x, #[[1]], #[[2]], 1.5] & /@ spec), {x, -bb[[2]], -bb[[
1]]}, PlotRange -> All]


# Part 2

nsp = 3;


Input of Parameters

freqs = {372.2, 364.4, 342, 6.083, 5.8};
JJ = ( {
{0, .91, 17.9, 1, 1, 1},
{0, 0, 11.75, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 0, 1, 1},
{0, 0, 0, 0, 0, 1},
{0, 0, 0, 0, 0, 0}
} );
Do[\[Nu][i] = freqs[[i]], {i, 1, nsp}];
Do[J[i, k] = JJ[[i, k]], {i, 1, nsp - 1}, {k, i + 1, nsp}];


number of lines to be expected and dimension ot (total) spin - space (at least for spin 1/2 )

numberoflines = Binomial[2 nsp, nsp - 1]
dimspsp = 2^nsp


15

8

spin operators for spin I = 1/2

ix = ( { {0, 1}, {1, 0} } )/2;
iy = ( { {0, -I}, {I, 0} } )/2;
iz = ( {{1, 0}, {0, -1} } )/2;


this function constructs the spin operator of particle j as matrix in the spin space of n particles

Op[op_, n_, j_] := Module[{x, m},
x = Join[Table[{{1, 0}, {0, 1}}, {j - 1}], {op},
Table[{{1, 0}, {0, 1}}, {n - j}]];
m = SparseArray[KroneckerProduct[Sequence @@ x]]
]
oIx[j_] := Op[ix, nsp, j]
oIy[j_] := Op[iy, nsp, j]
oIz[j_] := Op[iz, nsp, j]


Hamiltonian

HH = \!$$\*UnderoverscriptBox[\(\[Sum]$$, $$i = 1$$, $$nsp$$]$$\[Nu][i] oIz[ i]$$\) + \!$$\*UnderoverscriptBox[\(\[Sum]$$, $$i = 1$$, $$nsp - 1$$]$$\*UnderoverscriptBox[\(\[Sum]$$, $$j = i + 1$$, $$nsp$$]J[i,
j] $$(oIx[i] . oIx[j] + oIy[i] . oIy[j] + oIz[i] . oIz[j])$$\)\)


SparseArray[SequenceForm["<", 32, ">"], {8, 8}]

Eigensystem for the Hamilton - Operator

est = Transpose[Eigensystem[HH]]


Intensity operator

IOP1 = \!$$\*UnderoverscriptBox[\(\[Sum]$$, $$j = 1$$, $$nsp$$]$$oIx[j]$$\)


SparseArray[SequenceForm["<", 24, ">"], {8, 8}]

line1[a_, b_] := Module[{},
freq = Abs[a[[1]] - b[[1]]];
m2 = (a[[2]].IOP1.b[[2]])^2;
{freq, Sqrt[m2]}]


Calculate spectrum, show it and display result from part 1

spec1 = Table[0, {Binomial[dimspsp, 2]}];
nL = 0;
Do[
Do[
nL = nL + 1;
spec1[[nL]] = line1[est[[u]], est[[v]]],
{u, v + 1, dimspsp}
],
{v, 1, dimspsp - 1}
]
spec1 = Select[spec1, #[[2]] > 0. &];
normalizer = Max[Transpose[spec1][[2]]];
bb = {.95 Min[Transpose[spec][[1]]], 1.05 Max[Transpose[spec][[1]]]};
spec1 = {#[[1]], #[[2]]/normalizer} & /@ spec1;
spec1
ListPlot[spec1, Filling -> Axis, FillingStyle -> Directive[Red, Thick]]
Show[pl1]


Plot the spectrum and compare with the result of the 1 st part

pl4 = Plot[
Plus @@ (LF[x, #[[1]], #[[2]], 1.5] & /@ spec1), {x, bb[[1]],
bb[[2]]}, PlotRange -> All, PlotStyle -> Red]
Show[pl2]


Attachments:
4 Replies
Sort By:
Posted 1 year ago
 - you have earned "Featured Contributor" badge, congratulations !This is a great post and it has been selected for the curated Staff Picks group. Your profile is now distinguished by a "Featured Contributor" badge and displayed on the "Featured Contributor" board.
Posted 1 year ago
 This is a great contribution!I've wanted to be able to do these calculations in WL for a while now, especially since LAOCOON3 was no longer available.Let me know if you would like to optimize chemical shifts and coupling constants to fit observed data. We should be able to import spectra from an instrument data file (either the raw FID or the transformed spectrum), and use FindMinimum or NMinimize.