Core modulesΒΆ

These are the building blocks which are used to construct the CVs.

NN

FeedForward(layers[, activation, dropout, ...])

Define a feedforward neural network given the list of layers.

Loss

MSELoss(*args, **kwargs)

(Weighted) Mean Square Error

ELBOGaussiansLoss(*args, **kwargs)

ELBO loss function assuming the latent and reconstruction distributions are Gaussian.

FisherDiscriminantLoss(n_states[, lda_mode, ...])

Fisher's discriminant ratio.

AutocorrelationLoss([reduce_mode, invert_sign])

(Weighted) autocorrelation loss.

ReduceEigenvaluesLoss([mode, n_eig, invert_sign])

Calculate a monotonic function f(x) of the eigenvalues, by default the sum.

TDALoss(n_states, target_centers, target_sigmas)

Compute a loss function as the distance from a simple Gaussian target distribution.

CommittorLoss(atomic_masses, alpha[, cell, ...])

Compute a loss function based on Kolmogorov's variational principle for the determination of the committor function

GeneratorLoss(r, eta, friction, alpha[, ...])

Computes the loss function to learn a representation for the resolvent of the infinitesimal generator

SmartDerivatives([setup_device, force_all_atoms])

Utils to compute efficently (time and memory wise) the derivatives of the model output wrt the positions used to compute the input descriptors.

Stats

Stats(*args, **kwargs)

Base stats class.

PCA(in_features[, out_features])

Principal Component Analysis class.

LDA(in_features, n_states[, mode])

Fisher's discriminant class.

TICA(in_features[, out_features])

Time-lagged independent component analysis base class.

Transform

Transform(in_features, out_features)

Base transform class.

Transform.descriptors

PairwiseDistances(n_atoms, PBC, cell[, ...])

Non duplicated pairwise distances for a set of atoms from their positions

TorsionalAngle(indices, n_atoms, mode, PBC, cell)

Torsional angle defined by a set of 4 atoms from their positions

CoordinationNumbers(group_A, group_B, ...[, ...])

Coordination number between the elements of two groups of atoms from their positions

EigsAdjMat(mode, cutoff, n_atoms, PBC, cell)

Eigenvalues of the adjacency matrix for a set of atoms from their positions

MultipleDescriptors(descriptors_list, n_atoms)

Wrapper class to combine multiple descriptor transform objects acting on the same set of atomic positions

Transform.tools

Normalization(in_features[, mean, range, ...])

Normalizing block, used for computing standardized inputs/outputs.

ContinuousHistogram(in_features, min, max, bins)

Compute continuous histogram using Gaussian kernels

SwitchingFunctions(in_features, name, cutoff)

Common switching functions