Umbrella

class mlptrain.sampling.umbrella.UmbrellaSampling(zeta_func: ReactionCoordinate, kappa: float, temp: float | None = None)

Bases: object

Umbrella sampling class for generating pulling simulation, running umbrella sampling windows and running WHAM or umbrella integration.

__init__(zeta_func: ReactionCoordinate, kappa: float, temp: float | None = None)

Umbrella sampling to predict free energy using an mlp under a harmonic bias:

ω = κ/2 (ζ(r) - ζ_ref)^2

where ω is the bias in a particular window, ζ a function that takes in nuclear positions (r) and returns a scalar and ζ_ref the reference value of the reaction coordinate in that particular window.

property beta: float

β = 1 / (k_B T)

free_energies(prob_dist) → ndarray

Free energies at each point along the profile, eqn. 8.6.5 in Tuckerman

classmethod from_folder(folder_name: str, temp: float) → UmbrellaSampling

Create an umbrella sampling instance from a folder containing the window data

Return type:

(mlptrain.sampling.umbrella.UmbrellaSampling)

classmethod from_folders(*args: str, temp: float) → UmbrellaSampling

Load a set of individual umbrella sampling simulations in to a single one

Return type:

(mlptrain.sampling.umbrella.UmbrellaSampling)

load(folder_name: str) → None

Load data from a set of saved windows

run_umbrella_sampling(traj: mlptrain.ConfigurationSet, mlp: MLPotential, temp: float, interval: int, dt: float, init_ref: float | None = None, final_ref: float | None = None, n_windows: int = 10, save_sep: bool = True, all_to_xyz: bool = False, **kwargs) → None

Run umbrella sampling across n_windows, fitting Gaussians to the sampled values of the reaction coordinate.

NOTE will leave a dangling plt.figure open

range f reaction coordinates

Parameters:

  • mlp – Machine learnt potential

  • temp – Temperature in K to initialise velocities and to run NVT MD. Must be positive

  • interval – (int) Interval between saving the geometry

  • dt – (float) Time-step in fs

  • init_ref – (float | None) Value of reaction coordinate in Å for first window

  • final_ref – (float | None) Value of reaction coordinate in Å for first window

  • n_windows – (int) Number of windows to run in the umbrella sampling

  • save_sep – (bool) If True saves trajectories of each window separately as .xyz files

  • all_to_xyz – (bool) If True all .traj trajectory files are saved as .xyz files (when using save_fs, save_ps, save_ns)

in some units

Keyword Arguments:

constraints – (List) List of ASE constraints to use in the dynamics e.g. [ase.constraints.Hookean(a1, a2, k, rt)]

save(folder_name: str = 'umbrella') → None

Save the windows in this US to a folder containing each window as .txt files within in

umbrella_integration(n_bins: int = 100) → Tuple[ndarray, ndarray]

Perform umbrella integration on the umbrella windows to un-bias the probability distribution. Such that the PMF becomes

\[dA/dq = Σ_i p_i(q) dA^u_i/ dq\]

where the sum runs over the windows. Also plot and save the resulting free energy.

returned

Returns:

Tuple containing the reaction coordinate

and values of the free energy

Return type:

(np.ndarray, np.ndarray)

wham(tol: float = 0.001, max_iterations: int = 100000, n_bins: int = 100) → Tuple[ndarray, ndarray]

Construct an unbiased distribution (on a grid) from a set of windows

Parameters:

n_bins – Number of bins to use in the histogram (minus one) and the number of reaction coordinate values plotted and returned

Returns:

Tuple containing the reaction coordinate

and values of the free energy

Return type:

(np.ndarray, np.ndarray)

property zeta_refs: ndarray

Array of ζ_ref for each window