diff --git a/docs/src/fitting-eft.md b/docs/src/fitting-eft.md index 2fe1f1c..2185c7f 100644 --- a/docs/src/fitting-eft.md +++ b/docs/src/fitting-eft.md @@ -17,7 +17,7 @@ fdata = Dict("train" => rdata[1:n_train], ``` ## Specify the Friction Model -Next, we specify the matrix models that will make up our friction model. In this case we only specify the single matrix model `m_equ`, which being of the type `RWCMatrixModel` is based on a row-wise coupling. +Next, we specify the matrix models that will make up our friction model. In this case we only specify the single matrix model `m_equ`, which being of the type [RWCMatrixModel](@ref) is based on a row-wise coupling. Alternative for building a friction model with a pairwise coupling is provided in [the last of section of this working example](@ref fitting-pairwise-coupling). ```julia property = EuclideanMatrix() species_friction = [:H] @@ -119,4 +119,37 @@ The diffusion coefficient matrix $\Sigma$ can also be used to efficiently genera R = randf(fm,Σ) ``` +## [Friction Model with a Pairwise Coupling](@id fitting-pairwise-coupling) +Instead of using a row-wise coupling, we can also use a pairwise coupling to construct a friction model. The following code produces a friction model with a pairwise coupling: +```julia +property = EuclideanMatrix() +species_friction = [:H] +species_env = [:Cu] +m_equ = RWCMatrixModel(property, species_friction, species_env; + species_substrat = [:Cu], + rcut = 5.0, + maxorder = 2, + maxdeg = 5, +); + +m_equ = PWCMatrixModel(property, species_friction, species_env; + z2sym = NoZ2Sym(), + speciescoupling = SpeciesUnCoupled(), + species_substrat = [:Cu], + n_rep = 1, + maxorder=2, + maxdeg=5, + rcut= 5.0, + ); + +m_equ0 = OnsiteOnlyMatrixModel(property, species_friction, species_env; + species_substrat=[:Cu], + id=:equ0, + n_rep = 1, + rcut = rcut, + maxorder=2, + maxdeg=5 +); +fm= FrictionModel((mequ_off = m_equ, mequ_on=m_equ0)); +``` \ No newline at end of file