diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index a7bd753..456d3bb 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-13T15:55:27","documenter_version":"1.2.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-14T02:14:09","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/dev/development/index.html b/dev/development/index.html index 7a57f1b..7a4e337 100644 --- a/dev/development/index.html +++ b/dev/development/index.html @@ -1,2 +1,2 @@ -Development · ISOKANN
GitHub

Development

clone the repository with

git clone git@github.com:axsk/ISOKANN.jl.git

and create your own branch with

git checkout -b mynewcoolbranch.

Install julia, preferrably with juliaup (at least version 1.9). We recommend using Revise for a smoother dev experience.

Dont forget to activate the project with ]activate . and instantiate the dependencies with ]instantiate. You should then be able to run the tests with ]test or start using ISOKANN.

+Development · ISOKANN
GitHub

Development

clone the repository with

git clone git@github.com:axsk/ISOKANN.jl.git

and create your own branch with

git checkout -b mynewcoolbranch.

Install julia, preferrably with juliaup (at least version 1.9). We recommend using Revise for a smoother dev experience.

Dont forget to activate the project with ]activate . and instantiate the dependencies with ]instantiate. You should then be able to run the tests with ]test or start using ISOKANN.

diff --git a/dev/index.html b/dev/index.html index bc8a7a3..8cff610 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -ISOKANN.jl · ISOKANN
GitHub

ISOKANN.jl

Documentation for ISOKANN.jl

ISOKANN.IsoRun — Type
struct IsoRun{T}

The IsoRun struct represents a configuration for running the Isomolly algorithm.

Fields

  • nd::Integer: Number of outer data subsampling steps.
  • nx::Integer: Size of subdata set.
  • np::Integer: Number of power iterations with the same subdata.
  • nl::Integer: Number of weight updates with the same power iteration step.
  • nres::Integer: Resample new data every n outer steps.
  • ny::Integer: Number of new points to sample.
  • nk::Integer: Number of Koopman points to sample.
  • nxmax::Integer: Maximal number of x data points.
  • sim: Simulation object.
  • model: Model object.
  • opt: Optimization algorithm.
  • data::T: Data object.
  • losses: Vector to store loss values.
  • loggers::Vector: Vector of loggers.
source
ISOKANN.MollyLangevin — Type
struct MollyLangevin{S<:Molly.System} <: IsoSimulation

The MollyLangevin struct represents a Langevin dynamics simulation for the Molly package. It contains the system as well as the integration parameters.

Fields

  • sys::S: The system to be simulated.
  • temp::Float64: The temperature of the simulation in Kelvin. Default is 298.0 K.
  • gamma::Float64: The friction coefficient for the Langevin dynamics. Default is 1.0.
  • dt::Float64: The time step size in picoseconds. Default is 2e-3 ps.
  • T::Float64: The total simulation time in picoseconds. Default is 2e-1 ps.
  • n_threads::Int: The number of threads for force computations. Default is 1.
source
ISOKANN.propagate — Function
propagate(ms::MollyLangevin, x0::AbstractMatrix, ny)

Burst simulation of the MollyLangeving system ms. Propagates ny samples for each initial position provided in the columns of x0.

Arguments

  • ms::MollyLangevin: The MollyLangevin solver object.
  • x0::AbstractMatrix: The initial positions matrix.
  • ny: The number of trajectories per initial condition.

Returns

  • ys: A 3-dimensional array of size (dim(ms), nx, ny) containing the propagated solutions.
source
ISOKANN.pairnet — Function

Flux neural network model with layers fully connected layers using the corresponding simulations features as first layers

source
ISOKANN.pairnet — Function

Fully connected neural network with layers layers from n to nout dimensions. features allows to pass a featurizer as preprocessor, activation determines the activation function for each but the last layer lastactivation can be used to modify the last layers activation function

source
ISOKANN.propagate — Method
propagate(ms::MollyLangevin, x0::AbstractMatrix, ny)

Burst simulation of the MollyLangeving system ms. Propagates ny samples for each initial position provided in the columns of x0.

Arguments

  • ms::MollyLangevin: The MollyLangevin solver object.
  • x0::AbstractMatrix: The initial positions matrix.
  • ny: The number of trajectories per initial condition.

Returns

  • ys: A 3-dimensional array of size (dim(ms), nx, ny) containing the propagated solutions.
source
ISOKANN.reactionpath — Method

compute the reactionpath for the simulation sim starting in x0 along the gradient of the function provided by chi

Optional arguments: extrapolate: walk beyond the interval limits orth: factor for the force orthogonal to the chi gradient solver: the ODE solver to use dt: the timestep size kwargs...: Keyword arguments passed to the solve method

source
+ISOKANN.jl · ISOKANN
GitHub

ISOKANN.jl

Documentation for ISOKANN.jl

ISOKANN.IsoRun — Type
struct IsoRun{T}

The IsoRun struct represents a configuration for running the Isomolly algorithm.

Fields

  • nd::Integer: Number of outer data subsampling steps.
  • nx::Integer: Size of subdata set.
  • np::Integer: Number of power iterations with the same subdata.
  • nl::Integer: Number of weight updates with the same power iteration step.
  • nres::Integer: Resample new data every n outer steps.
  • ny::Integer: Number of new points to sample.
  • nk::Integer: Number of Koopman points to sample.
  • nxmax::Integer: Maximal number of x data points.
  • sim: Simulation object.
  • model: Model object.
  • opt: Optimization algorithm.
  • data::T: Data object.
  • losses: Vector to store loss values.
  • loggers::Vector: Vector of loggers.
source
ISOKANN.MollyLangevin — Type
struct MollyLangevin{S<:Molly.System} <: IsoSimulation

The MollyLangevin struct represents a Langevin dynamics simulation for the Molly package. It contains the system as well as the integration parameters.

Fields

  • sys::S: The system to be simulated.
  • temp::Float64: The temperature of the simulation in Kelvin. Default is 298.0 K.
  • gamma::Float64: The friction coefficient for the Langevin dynamics. Default is 1.0.
  • dt::Float64: The time step size in picoseconds. Default is 2e-3 ps.
  • T::Float64: The total simulation time in picoseconds. Default is 2e-1 ps.
  • n_threads::Int: The number of threads for force computations. Default is 1.
source
ISOKANN.propagate — Function
propagate(ms::MollyLangevin, x0::AbstractMatrix, ny)

Burst simulation of the MollyLangeving system ms. Propagates ny samples for each initial position provided in the columns of x0.

Arguments

  • ms::MollyLangevin: The MollyLangevin solver object.
  • x0::AbstractMatrix: The initial positions matrix.
  • ny: The number of trajectories per initial condition.

Returns

  • ys: A 3-dimensional array of size (dim(ms), nx, ny) containing the propagated solutions.
source
ISOKANN.pairnet — Function

Fully connected neural network with layers layers from n to nout dimensions. features allows to pass a featurizer as preprocessor, activation determines the activation function for each but the last layer lastactivation can be used to modify the last layers activation function

source
ISOKANN.pairnet — Function

Flux neural network model with layers fully connected layers using the corresponding simulations features as first layers

source
ISOKANN.propagate — Method
propagate(ms::MollyLangevin, x0::AbstractMatrix, ny)

Burst simulation of the MollyLangeving system ms. Propagates ny samples for each initial position provided in the columns of x0.

Arguments

  • ms::MollyLangevin: The MollyLangevin solver object.
  • x0::AbstractMatrix: The initial positions matrix.
  • ny: The number of trajectories per initial condition.

Returns

  • ys: A 3-dimensional array of size (dim(ms), nx, ny) containing the propagated solutions.
source
ISOKANN.reactionpath — Method

compute the reactionpath for the simulation sim starting in x0 along the gradient of the function provided by chi

Optional arguments: extrapolate: walk beyond the interval limits orth: factor for the force orthogonal to the chi gradient solver: the ODE solver to use dt: the timestep size kwargs...: Keyword arguments passed to the solve method

source
diff --git a/dev/search_index.js b/dev/search_index.js index 0ed80db..ca4d48f 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"development/#Development","page":"Development","title":"Development","text":"","category":"section"},{"location":"development/","page":"Development","title":"Development","text":"clone the repository with","category":"page"},{"location":"development/","page":"Development","title":"Development","text":"git clone git@github.com:axsk/ISOKANN.jl.git","category":"page"},{"location":"development/","page":"Development","title":"Development","text":"and create your own branch with","category":"page"},{"location":"development/","page":"Development","title":"Development","text":"git checkout -b mynewcoolbranch.","category":"page"},{"location":"development/","page":"Development","title":"Development","text":"Install julia, preferrably with juliaup (at least version 1.9). 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