Ohmic spin-boson nonequilibrium population dynamics

The spin-boson model is widely used as a model for condensed phase quantum dynamics. It is defined by a system-bath Hamiltonian where the system is a 2-state spin coupled to a bath of harmonic oscillators. This example shows how to perform nonequilibrium population dynamics with the spin-boson model using a bath characterised by the Ohmic spectral density. We will be using model B from the work of [22].

Our boson bath will have 100 oscillators, each with a mass of 1. Here, we also set up the model with the ohmic density and the parameters that match up with our reference ([22]). The ohmic density is given a cutoff frequency of 2.5 and a Kondo parameter of 0.09. The model is symmetric, with the energy bias between states equal to 0.0, and the coupling between states set to 1.

using NQCDynamics
N = 100
atoms = Atoms(fill(1, N))
β = 5
T = 1 / β
density = OhmicSpectralDensity(2.5, 0.09)
model = SpinBoson(density, N, 0.0, 1.0)

Initial conditions

For the initial conditions, we will sample directly from a Wigner distribution for the nuclear degrees of freedom. Since our nuclear degrees of freedom are harmonic, the Wigner distribution has an analytic form and we can use the distributions included in the package. The position and velocity variables we create here are Matrixs of Normal distributions, which are shaped to match the system size (1, N). Inside the DynamicalDistribution they will provide samples that match the size of the system. The initial electronic state is confined to 1 with PureState(1).

position = reshape([PositionHarmonicWigner(ω, β, 1) for ω in model.ωⱼ], 1, :)
velocity = reshape([VelocityHarmonicWigner(ω, β, 1) for ω in model.ωⱼ], 1, :)
distribution = DynamicalDistribution(velocity, position, (1, 100)) * PureState(1)

Dynamics

Now that we have a distribution from which we can sample our initial conditions, we can run ensembles of trajectories and calculate the population correlation functions. Let's compare the results obtained using FSSH and Ehrenfest.

fssh = Simulation{FSSH}(atoms, model)
ehrenfest = Simulation{Ehrenfest}(atoms, model)

saveat = 0:0.1:20
output = TimeCorrelationFunctions.PopulationCorrelationFunction(fssh, Diabatic())
ensemble_fssh = run_dynamics(fssh, (0.0, 20.0), distribution;
    saveat=saveat, trajectories=100, output, reduction=MeanReduction(), dt=0.1)
output = TimeCorrelationFunctions.PopulationCorrelationFunction(ehrenfest, Diabatic())
ensemble_ehrenfest = run_dynamics(ehrenfest, (0.0, 20.0), distribution;
    saveat=saveat, trajectories=100, output, reduction=MeanReduction(), dt=0.1)

[ Info: Sampling randomly from provided distribution.
[ Info: Performing 100 trajectories.
[ Info: Finished after 5.659343144 seconds.
[ Info: Sampling randomly from provided distribution.
[ Info: Performing 100 trajectories.
[ Info: Finished after 6.15761409 seconds.

Here, we can see the population difference between the two states.

using Plots
plot(saveat, [p[1,1] - p[1,2] for p in ensemble_fssh[:PopulationCorrelationFunction]], label="FSSH")
plot!(saveat, [p[1,1] - p[1,2] for p in ensemble_ehrenfest[:PopulationCorrelationFunction]], label="Ehrenfest")
xlabel!("Time /a.u.")
ylabel!("Population difference")

The exact result for this model, along with various mapping methods can be found in the work of [22]. We can see that even with just 100 trajectories, our Ehrenfest result closely matches theirs. The FSSH is quite clearly underconverged with only 100 trajectories due to the discontinuous nature of the individual trajectories. Feel free to try this for yourself and see what the converged FSSH result looks like!