combining_models.md

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Composing multiple models

Why?

For some non-adiabatic dynamics methods, we would like to calculate certain quantities using a number of different approximations or different machine learning model interfaces. However, a model for a potential energy surface does not and should not be able to provide e.g. an electronic friction tensor. Instead, we can use the modular nature of NQCModels.jl to combine different components to a non-adiabatic dynamics method into a single model that can be used to set up a Simulation

For example, in order to run [Molecular Dynamics with Electronic friction](@ref mdef-dynamics), we need to use a Model which supports the following methods:

• potential to determine the total energy
• derivative to determine interatomic forces
• friction to determine the electronic friction tensor

However, most models for potential energy surfaces only provide potential and derivative, whereas friction! is provided by an ElectronicFrictionProvider.

To combine different models together for a system, we can use Subsystems to define which atoms in the system a given model should be applied to, and a CompositeModel to combine the different models into one. If different Subsystems should experience different effective temperatures, a TemperatureSetting can be provided when initialising a Simulation.

Example: Combining an AdiabaticModel with ElectronicFrictionProviders

using NQCDynamics

atoms = vcat([:H, :H], [:Cu for _ in 1:54]) # create example surface structure
positions = rand(3,56) # Placeholder for atomic positions
pes_model = Subsystem(Free(3)) # PES model with potential and derivative
friction_model = Subsystem(RandomFriction(3)) # Friction model supporting friction! only

println("PES model: \n", pes_model, "\nFriction model:", "friction_model")

complete_model = CompositeModel(pes_model, friction_model)

As shown above, we have combined the RandomFriction provider with a model potential to give a total model which can be used in a simulation.

println("Potential: ", NQCModels.potential(complete_model, positions))
println("Derivative: ", NQCModels.derivative(complete_model, positions))
println("Friction: ", NQCModels.friction(complete_model, positions))