FreeBird.jl Tutorial

This is a tutorial to using the FreeBird.jl package. It covers the basic functionalities of the package, such as generating atomistic and lattice walkers, defining a potential energy function or Hamiltonian, and running a sampling simulation. For more detailed information, please refer to the documentation of the package. You can find the runnable version of this script in the scripts directory of the package.

Atomistic walkers and nested sampling

First, let's load the FreeBird.jl package:

using FreeBird

Now, let's create a few configurations of a simple atomistic system with six particles in a 3D box.

single_config = generate_initial_configs(1, 562.5, 6; particle_type=:H)

1-element Vector{AtomsBase.FastSystem{3, AtomsBase.PeriodicCell{3, Quantity{Float64, 𝐋, Unitful.FreeUnits{(Å,), 𝐋, nothing}}}, Quantity{Float64, 𝐋, Unitful.FreeUnits{(Å,), 𝐋, nothing}}, Quantity{Float64, 𝐌, Unitful.FreeUnits{(u,), 𝐌, nothing}}, AtomsBase.ChemicalSpecies}}:
 FastSystem(H₆, periodicity = FFF)

The function above has generated a single configuration, with 562.5 Å$^3$ volume per particle, and 6 particles of type H. Note that the particle_type keyword argument can be used to specify the type of particle, i.e., chemical element. By default, the type is set to :H. Use ?generate_initial_configs in the REPL to see the documentation of the function. Or see generate_initial_configs.

Let's inspect the generated configuration using the vew_structure function:

single_config[1] |> view_structure

          .------------------------------------.
         /|            H                       |
        / |                                    |
       /  |                                    |
      /   |                                    |
     /    H                                    |
    /     |                                    |
   /      |                                    |
  /       |           H                        |
 *        |                                    |
 |        |                 H                  |
 |        |                                    |
 |        |                H                   |
 |        |                          H         |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        .------------------------------------.
 |       /                                    /
 |      /                                    /
 |     /                                    /
 |    /                                    /
 |   /                                    /
 |  /                                    /
 | /                                    /
 |/                                    /
 *------------------------------------*

It's of a FastSystem type from AtomsBase. The dimensions of the box are 15 Å x 15 Å x 15 Å, following the volume per particle specified. The positions of the particles are randomly generated within the box.

Now, let's generate a few more configurations:

configs = generate_initial_configs(120, 562.5, 6)

120-element Vector{AtomsBase.FastSystem{3, AtomsBase.PeriodicCell{3, Quantity{Float64, 𝐋, Unitful.FreeUnits{(Å,), 𝐋, nothing}}}, Quantity{Float64, 𝐋, Unitful.FreeUnits{(Å,), 𝐋, nothing}}, Quantity{Float64, 𝐌, Unitful.FreeUnits{(u,), 𝐌, nothing}}, AtomsBase.ChemicalSpecies}}:
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 ⋮
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)
 FastSystem(H₆, periodicity = FFF)

The function above has generated 120 configurations, again, with 562.5 Å$^3$ volume per particle, and 6 particles of type H. These configurations will be served as the initial walkers for a sampling run, but first, we need to warp them into the AtomWalker type defined in FreeBird.jl.

walkers = AtomWalker.(generate_initial_configs(120, 562.5, 6));

Let's inquire the type of the walkers variable:

walkers |> typeof

Vector{AtomWalker{1}} (alias for Array{AtomWalker{1}, 1})

The walkers variable is of a Vector{AtomWalker{1}} type, which is a vector of AtomWalker{1} objects. The AtomWalker{1} type is a parametrized type, where the parameter is the number of components in the system. In this case, the system has only one component, consisting of 6 particles of type H.

To define how these particles interact with each other, we need to create a potential energy function. Let's use the Lennard-Jones potential:

lj = LJParameters(epsilon=0.1, sigma=2.5, cutoff=4.0)

LJParameters(0.1 eV, 2.5 Å, 4.0, -9.763240814208984e-5 eV)

The LJParameters type is a struct that holds the parameters of the Lennard-Jones potential. The epsilon and sigma fields are the energy and length scales of the potential, respectively. The cutoff field is the distance at which the potential is truncated. An energy shift is applied to the potential to ensure continuity at the cutoff distance, automatically in this case. See the documentation of the LJParameters type for more information and examples.

We now can create a so-called liveset that will be used to store the walkers during the simulation. The lj potential will be used to attached and used to calculate the potential energy of the walkers.

ls = LJAtomWalkers(walkers, lj)

LJAtomWalkers(AtomWalker{1}, LJParameters):
[1] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.008190004538058977 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[2] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.00888230480833532 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[3] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.09811212994214642 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[4] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.012636818329797723 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[5] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.10034595112068169 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

⋮
Omitted 110 walkers
⋮

[116] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.016664251707252435 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[117] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.16464895589846623 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[118] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.03496761220835596 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[119] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : 117840.13709112834 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[120] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -0.044584544805109226 eV
    iter               : 0
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

LJParameters(0.1 eV, 2.5 Å, 4.0, -9.763240814208984e-5 eV)

Here, ls is a LJAtomWalkers type, and has the walkers and lj fields attached to it.

Now, time to set up a simulation. We will be using nested sampling, a Bayesian-inference inspired method, as an example here. First, we need to define the nested sampling parameters:

ns_params = NestedSamplingParameters(mc_steps=200, step_size=0.1)

NestedSamplingParameters(200, 0.01, 0.1, 1.0e-6, 1.0, (0.25, 0.75), 0, 100, 1234)

The NestedSamplingParameters type is a struct that holds the parameters of the nested sampling algorithm. The fields are as follows:

mc_steps::Int64: The number of total Monte Carlo moves to perform.
initial_step_size::Float64: The initial step size, which is the fallback step size if MC routine fails to accept a move.
step_size::Float64: The on-the-fly step size used in the sampling process.
step_size_lo::Float64: The lower bound of the step size.
step_size_up::Float64: The upper bound of the step size.
accept_range::Tuple{Float64, Float64}: The range of acceptance rates for adjusting the step size.
fail_count::Int64: The number of failed MC moves in a row.
allowed_fail_count::Int64: The maximum number of failed MC moves allowed before resetting the step size.
random_seed::Int64: The seed for the random number generator.

Speaking of the Monte Carlo moves, we need to define that too:

mc = MCRandomWalkClone()

MCRandomWalkClone([1, 2, 3])

The MCRandomWalkClone type is a type of Monte Carlo move that indicates a new walker is created by cloning an existing walker and then decorrelate the positions of the particles.

We also need to specify how we want to save the data and the output:

save = SaveEveryN(n_traj=10, n_snap=20_000)

SaveEveryN("output_df.csv", "output.traj.extxyz", "output.ls.extxyz", 10, 20000, 1)

The SaveEveryN type is a struct that holds the parameters of the saving routine.

Now, we are ready to run the nested sampling simulation:

energies, liveset, _ = nested_sampling(ls, ns_params, 20_000, mc, save)

(20000×2 DataFrame
   Row │ iter   emax
       │ Int64  Float64
───────┼──────────────────────
     1 │     1      1.59522e6
     2 │     2      2.24506e5
     3 │     3      1.1784e5
     4 │     4  11861.3
     5 │     5   6319.86
     6 │     6   2886.45
     7 │     7   1690.44
     8 │     8   1444.13
   ⋮   │   ⋮          ⋮
 19994 │ 19994     -1.26974
 19995 │ 19995     -1.26974
 19996 │ 19996     -1.26974
 19997 │ 19997     -1.26974
 19998 │ 19998     -1.26974
 19999 │ 19999     -1.26974
 20000 │ 20000     -1.26974
            19985 rows omitted, LJAtomWalkers(AtomWalker{1}, LJParameters):
[1] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417394642656 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[2] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.269741739464367 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[3] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417394643686 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[4] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.269741739464488 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[5] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417394645123 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

⋮
Omitted 110 walkers
⋮

[116] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417395083472 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[117] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417395133992 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[118] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417395151693 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[119] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.269741739517138 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

[120] AtomWalker{1}(
    configuration      : FastSystem(H₆, periodicity = FFF)
    energy             : -1.2697417394818498 eV
    iter               : 20000
    list_num_par       : [6]
    frozen             : Bool[0]
    energy_frozen_part : 0.0 eV)

LJParameters(0.1 eV, 2.5 Å, 4.0, -9.763240814208984e-5 eV)
, NestedSamplingParameters(200, 0.01, 6.58359649027311e-6, 1.0e-6, 1.0, (0.25, 0.75), 0, 100, 1234))

The results of the simulation are stored in the energies and liveset variables. The energies variable is a DataFrame that contains the energies of the walkers at each iteration. The liveset variable is the final liveset after the simulation. Let's see how the walkers look like after the simulation:

liveset.walkers[1].configuration |> view_structure

          .------------------------------------.
         /|                                    |
        / |                                    |
       /  |                                    |
      /   |                                    |
     /    |                                    |
    /     |                                    |
   /      |                                    |
  /       |                                    |
 *        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                                    |
 |        |                H                   |
 |        .----------------H-----H-------------.
 |       /                                    /
 |      /              H     H               /
 |     /                     H              /
 |    /                                    /
 |   /                                    /
 |  /                                    /
 | /                                    /
 |/                                    /
 *------------------------------------*

They should be in a more ordered state, in this case, a cluster, than the initial gaseous state.

Calculating heat capacity with `AnalysisTools` module

The AnalysisTools module provides functions to calculate the heat capacity of the system. First, we calculate the ω factors, which account for the fractions of phase-space volume sampled during each nested sampling iteration, defined as:

\[\omega_i = \frac{1}{N+1} \left(\frac{N}{N+1}\right)^i\]

where $N$ is the number of walkers and $i$ is the iteration number.

ωi = ωᵢ(energies.iter, 120);

Let's shift the energies to be greater than or equal to zero, making the calculation of the heat capacity more stable.

Ei = energies.emax .- minimum(energies.emax);

Specify the temperatures that we are interested in, in units of Kelvin.

Ts = collect(1:0.1:1000);

Define the Boltzmann constant in units of eV/K.

kb = 8.617333262e-5 # eV/K

8.617333262e-5

Calculate the inverse temperatures

β = 1 ./(kb.*Ts);

Define the degrees of freedom, which is 3×6 for the 6-particle system.

dof = 18

Calculate the heat capacities as a function of temperature using the cv function,

\[C_V(\beta) = \frac{\mathrm{dof} \cdot k_B}{2} + k_B \beta^2 \left(\frac{\sum_i \omega_i E_i^2 \exp(-E_i \beta)}{Z(\beta)} - U(\beta)^2\right)\]

cvs = cv(energies, β, dof, 120);

Let's plot the heat capacity as a function of temperature

using Plots
plot(Ts, cvs./kb, xlabel="Temperature (K)", ylabel="Heat Capacity (\$k_B\$)", label="LJ\$_6\$")

The plot should show the heat capacity as a function of temperature for the 6-particle Lennard-Jones system, with a main peak around 400 K, representing the phase transition, and some fluctuations at low temperatures, and tailing off to zero at high temperatures.

That's it! You have successfully run a nested sampling simulation using the FreeBird.jl package.

Lattice walkers and exact enumeration

Another feature of FreeBird.jl is the ability to work with lattice systems. The lattice systems are defined by the MLattice which is a parametrized type.

MLattice{C,G}(
    lattice_vectors::Matrix{Float64},
    basis::Vector{Tuple{Float64, Float64, Float64}},
    supercell_dimensions::Tuple{Int64, Int64, Int64},
    periodicity::Tuple{Bool, Bool, Bool},
    components::Vector{Vector{Bool}},
    adsorptions::Vector{Bool},
    cutoff_radii::Vector{Float64},
) where {C,G}

The C parameter is the number of components in the system, and the G parameter defines the geometry of the lattice.

Now, let's create a simple square lattice system with single component:

ml = MLattice{1,SquareLattice}(components=[[1,2,3,4]])

SLattice{SquareLattice}
    lattice_vectors      : [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]
    positions            : 16 grid points
    supercell_dimensions : (4, 4, 1)
    basis                : [(0.0, 0.0, 0.0)]
    periodicity          : (true, true, false)
    cutoff radii         : 2 nearest neighbors cutoffs [1.1, 1.5]
    occupations          : 
      ● ● ● ● 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
    adsorptions          : full adsorption

When you run the above code, the outer constructor of MLattice will be called. Many of the arguments are optional and have default values. The components argument is a vector of vectors that defines the components of the system. The components=[[1,2,3,4]] argument specifies that the system has a single component, and the first four sites are occupied.

MLattice{C,SquareLattice}(; lattice_constant::Float64=1.0,
    basis::Vector{Tuple{Float64,Float64,Float64}}=[(0.0, 0.0, 0.0)],
    supercell_dimensions::Tuple{Int64,Int64,Int64}=(4, 4, 1),
    periodicity::Tuple{Bool,Bool,Bool}=(true, true, false),
    cutoff_radii::Vector{Float64}=[1.1, 1.5],
    components::Union{Vector{Vector{Int64}},Vector{Vector{Bool}},Symbol}=:equal,
    adsorptions::Union{Vector{Int},Symbol}=:full)

You may notice that the above code returns a SLattice type. The SLattice type is simply an alias for the MLattice{1,G}, where G is the geometry of the lattice and the number of components is fixed to 1. You can also directly call the SLattice, it will give the same result:

sl = SLattice{SquareLattice}(components=[[1,2,3,4]])

SLattice{SquareLattice}
    lattice_vectors      : [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]
    positions            : 16 grid points
    supercell_dimensions : (4, 4, 1)
    basis                : [(0.0, 0.0, 0.0)]
    periodicity          : (true, true, false)
    cutoff radii         : 2 nearest neighbors cutoffs [1.1, 1.5]
    occupations          : 
      ● ● ● ● 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
    adsorptions          : full adsorption

Now, let's define a Hamiltonian for the lattice system:

ham = GenericLatticeHamiltonian(-0.04, [-0.01, -0.0025], u"eV")

GenericLatticeHamiltonian{2,Quantity{Float64, 𝐋^2 𝐌 𝐓^-2, Unitful.FreeUnits{(eV,), 𝐋^2 𝐌 𝐓^-2, nothing}}}:
    on_site_interaction:      -0.04 eV
    nth_neighbor_interactions: [-0.01, -0.0025] eV

The GenericLatticeHamiltonian type is a struct that holds the parameters of the Hamiltonian. The first argument is the on-site energy, and the second argument is the list of n-th nearest-neighbors energy. The third argument is the unit of the energy.

To run exact enumeration, we only need a initial walker/lattice configuration, and the Hamiltonian. Let's run the exact enumeration:

df, ls = exact_enumeration(sl, ham)

(1820×2 DataFrame
  Row │ energy      config
      │ Quantity…   Array…
──────┼───────────────────────────────────────────────
    1 │    -0.2 eV  Vector{Bool}[[1, 1, 1, 1, 0, 0, …
    2 │ -0.1925 eV  Vector{Bool}[[1, 1, 1, 0, 1, 0, …
    3 │  -0.195 eV  Vector{Bool}[[1, 1, 1, 0, 0, 1, …
    4 │ -0.1925 eV  Vector{Bool}[[1, 1, 1, 0, 0, 0, …
    5 │  -0.185 eV  Vector{Bool}[[1, 1, 1, 0, 0, 0, …
    6 │   -0.18 eV  Vector{Bool}[[1, 1, 1, 0, 0, 0, …
    7 │   -0.18 eV  Vector{Bool}[[1, 1, 1, 0, 0, 0, …
    8 │   -0.18 eV  Vector{Bool}[[1, 1, 1, 0, 0, 0, …
  ⋮   │     ⋮                       ⋮
 1814 │ -0.1925 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1815 │  -0.195 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1816 │  -0.185 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1817 │ -0.1925 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1818 │  -0.195 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1819 │ -0.1925 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
 1820 │    -0.2 eV  Vector{Bool}[[0, 0, 0, 0, 0, 0, …
                                     1805 rows omitted, LatticeGasWalkers(LatticeWalker{1}, GenericLatticeHamiltonian{2, Quantity{Float64, 𝐋^2 𝐌 𝐓^-2, Unitful.FreeUnits{(eV,), 𝐋^2 𝐌 𝐓^-2, nothing}}}):
    lattice_vectors:      [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]
    supercell_dimensions: (4, 4, 1)
    periodicity:          (true, true, false)
    basis:                [(0.0, 0.0, 0.0)]
[1] energy = -0.2 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[2] energy = -0.1925 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[3] energy = -0.195 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[4] energy = -0.1925 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[5] energy = -0.185 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
⋮
Omitted 1810 walkers
⋮

[1816] energy = -0.185 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0]
[1817] energy = -0.1925 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1]
[1818] energy = -0.195 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1]
[1819] energy = -0.1925 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1]
[1820] energy = -0.2 eV, iter = 0
    occupations:
      components:
        component 1:
          Bool[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]

GenericLatticeHamiltonian{2,Quantity{Float64, 𝐋^2 𝐌 𝐓^-2, Unitful.FreeUnits{(eV,), 𝐋^2 𝐌 𝐓^-2, nothing}}}:
    on_site_interaction:      -0.04 eV
    nth_neighbor_interactions: [-0.01, -0.0025] eV

)

The results of the exact enumeration are stored in the df and ls variables. The df variable is a DataFrame that contains the list of energies, as well as the configurations. The ls variable is the final liveset that contains all possible configurations of the lattice system. Let's see how the first configuration looks like:

ls.walkers[1].configuration

SLattice{SquareLattice}
    lattice_vectors      : [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]
    positions            : 16 grid points
    supercell_dimensions : (4, 4, 1)
    basis                : [(0.0, 0.0, 0.0)]
    periodicity          : (true, true, false)
    cutoff radii         : 2 nearest neighbors cutoffs [1.1, 1.5]
    occupations          : 
      ● ● ● ● 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
    adsorptions          : full adsorption

It's the initial configuration of the lattice system. Let's see how the last configuration looks like:

ls.walkers[end].configuration

SLattice{SquareLattice}
    lattice_vectors      : [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]
    positions            : 16 grid points
    supercell_dimensions : (4, 4, 1)
    basis                : [(0.0, 0.0, 0.0)]
    periodicity          : (true, true, false)
    cutoff radii         : 2 nearest neighbors cutoffs [1.1, 1.5]
    occupations          : 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
      ○ ○ ○ ○ 
      ● ● ● ● 
    adsorptions          : full adsorption

Be warned that the exact enumeration can be computationally expensive for large systems.

Calculating heat capacity

Since we enumerated all possible configurations of the lattice system, we can calculate the partition function, then heat capacity directly.

Let's calculate the heat capacity for the lattice system: Define the temperatures that we are interested in, in units of Kelvin.

Ts = collect(1:0.1:500);

Define the Boltzmann constant in units of eV/K.

kb = 8.617333262e-5 # eV/K

8.617333262e-5

Convert them to inverse temperatures

βs = 1 ./(kb.*Ts);

Extract the energies from the DataFrame, keeping the values only

es = [e.val for e in df.energy];

Since this is not a nested sampling run, each configuration carries the same weight:

ω_1 = ones(length(df.energy));

And for a lattice, the degrees of freedom is 0:

dof = 0

Now we can use a scaler version of the cv function to calculate the heat capacity:

cvs = [cv(β, ω_1, es, dof) for β in βs];

Let's plot the heat capacity as a function of temperature

using Plots
plot(Ts, cvs./kb, xlabel="Temperature (K)", ylabel="Heat Capacity (\$k_B\$)", label="Square Lattice")

You should expect to see a single peak in the heat capacity curve around 40 K, and tailing off to zero at high temperatures.

That's it! You have successfully run an exact enumeration simulation using the FreeBird.jl package.

This page was generated using Literate.jl.

FreeBird.jl Tutorial

Atomistic walkers and nested sampling

Calculating heat capacity with AnalysisTools module

Lattice walkers and exact enumeration

Calculating heat capacity

Calculating heat capacity with `AnalysisTools` module