Difference between revisions of "Rlx scalar.m"
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| − | {{DISPLAYTITLE:rlx_scalar.m}} | + | {{DISPLAYTITLE:rlx_scalar.m}} __NOTOC__ |
| − | + | Computes a scalar-relaxation superoperator using Redfield theory for cases where the system has a static background Hamiltonian and a perturbation multiplied by a scalar stochastic function, such as scalar hyperfine relaxation; the function is called by the Spinach relaxation theory module and may also be invoked directly. | |
==Syntax== | ==Syntax== | ||
| − | |||
| − | = | + | R=rlx_scalar(spin_system,H0,H1,tau_c_array) |
| − | |||
| − | == | + | ==Parameters== |
H0 - background Hamiltonian | H0 - background Hamiltonian | ||
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==See also== | ==See also== | ||
| − | [[relaxation.m]], [[expmint.m]], [[lindbladian.m]] | + | [[relaxation.m]], [[expmint.m]], [[lindbladian.m]], [[rlx_t1_t2.m]], [[adelim.m]], [[blinv.m]], [[blprod.m]], [[corrfun.m]], [[magpump.m]], [[ngce.m]], [[rlx_split.m]], [[sec2kite.m]], [[spden.m]], [[Kernel_utilities]] |
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''Version 2.1, authors: [[Ilya Kuprov]]'' | ''Version 2.1, authors: [[Ilya Kuprov]]'' | ||
Latest revision as of 19:41, 6 June 2026
Computes a scalar-relaxation superoperator using Redfield theory for cases where the system has a static background Hamiltonian and a perturbation multiplied by a scalar stochastic function, such as scalar hyperfine relaxation; the function is called by the Spinach relaxation theory module and may also be invoked directly.
Syntax
R=rlx_scalar(spin_system,H0,H1,tau_c_array)
Parameters
H0 - background Hamiltonian
H1 - the stochastically modulated interaction operator
multiplied by its root mean square modulation depth
tau_c - the correlation time of the stochastic modulation
Outputs
R - relaxation superoperator as a negative definite matrix
Notes
If H1(t) has a non-zero average value, it must be subtracted out and put into H0.
See also
relaxation.m, expmint.m, lindbladian.m, rlx_t1_t2.m, adelim.m, blinv.m, blprod.m, corrfun.m, magpump.m, ngce.m, rlx_split.m, sec2kite.m, spden.m, Kernel_utilities
Version 2.1, authors: Ilya Kuprov
