Data and Code repository for «Relaxation pathways and emergence of domains in square artificial spin ice»

Data and Code repository for 
«Relaxation pathways and emergence of domains in square artificial spin ice»
    by Matteo Menniti, Naëmi Leo, Pedro Vilalba-González, Matteo Pancaldi, and Paolo Vavassori

Figures 2,3 and 6 - results of kinetic Monte Carlo simulations

Data extracted from kMC runs:
    - for models = mean (mean-field model, MF), split (chiral-split barrier model, CB)
    - for periodicities a = 190 nm to 380 nm

Files labelled "model_a_300K_50x50_global.dat" store the global output for each kMC run, with columns
    - t_runx    continuous time, normalised to the single-particle frequency
    - m_runx    net magnetisation along the [11] direction
    - T3_runx    population of T3 vertices
    - T2_perp_runx    population of T2 vertices magnetised perpendicular to the intial [11] direction
    - T2_opp_runx    population of T2 vertices magnetised opposite to initial [11] direction
    Here "_runx" is the kMC run number with x from 0 through 20.

Files labelled "model_a_300K_50x50_verteximage.npz" contain representative spatially-resolved vertex states, encoded in a 50x50 matrix with numbers 1...4 representing T1...T4 vertices on the square grid. These are shown for run0 at times where the net magnetisation reaches 75%, 50%, 25% of its initial value:
    - for Fig. 2:    a = 200 nm, 240 nm, 280 nm
    - for Fig. 6:    a = 300 nm, 340 nm, 380 nm

The plotting of data for Figures 2 and 6, as well as Figure 3 is documented in the following files:
    - Figures_kMC.ipynb - ipython notebook
    - Figures_kMC.pdf - pdf print of the ipython notebook

Figure 4 - transition probabilities

The analytical derivation of the relative rates, based on the values given in Tab.1 of the manuscript, are documented in 
    - Figure_4.ipynb - ipython notebook
    - Figure_4.pdf - pdf print of the ipython notebook