ml4chem.atomistic.models package¶
Submodules¶
ml4chem.atomistic.models.autoencoders module¶

class
ml4chem.atomistic.models.autoencoders.
Annealer
(warm_up=50, step=50, n_cycles=5)[source]¶ Bases:
object
Annealing class
Based on on https://arxiv.org/abs/1903.10145.
 Parameters
warm_up (int, optional) – Number of epochs that we let reconstruction to dominate VAE, by default 50
step (int, optional) – Number of steps to increase from 0 to 1, by default 50
n_cycles (int, optional) – The number of cycles we will repeat the annealing, by default 5

class
ml4chem.atomistic.models.autoencoders.
AutoEncoder
(hiddenlayers=None, activation='relu', one_for_all=False, **kwargs)[source]¶ Bases:
ml4chem.atomistic.models.base.DeepLearningModel
Fully connected atomic autoencoder
AutoEncoders are very interesting models where usually the input is reconstructed (input equals output). These models are able to learn data coding in an unsupervised manner. They are composed by an encoder that takes an input and concentrate (encodes) the information in a lower/larger dimensional space (aka latent space). Subsequently, a decoder takes the latent space and tries to reconstruct the input. It is been reported that when the output is not equal to the input, the model learns how to ‘translate’ input into output e.g. image coloring.
This module uses autoencoders for pipelines in chemistry.
 Parameters
hiddenlayers (dict) – Dictionary with encoder, and decoder layers in the Auto Encoder.
activation (str) – The activation function.
one_for_all (bool) – Use one autoencoder model for all atoms instead of a model per atom type as in the BehlerParrinello scheme. Default is False.
Notes
When defining the hiddenlayers keyword argument, input and output dimensions are automatically determined. For example, suppose you have an input data point with 10 dimensions and you want to autoencode with targets having 14 dimensions, a latent space with 4 dimensions and just one hidden layer with 5 nodes between inputlayer / latentlayer and latentlayer / outputlayer. Your hiddenlayers dictionary would look like this:
>>> hiddenlayers = {'encoder': (5, 4), 'decoder': (4, 5)}
That would generate an autoencoder with topology (10, 5, 4  4, 5, 14).

NAME
= 'AutoEncoder'¶

decode
(z, symbol=None)[source]¶ Decode latent vector, z
 Parameters
z (array) – Latent vector.
symbol (str, optional) – Chemical symbol. Default is None.
 Returns
Tensor with reconstruction.
 Return type
reconstruction

encode
(x, symbol=None)[source]¶ Encode input
 Parameters
x (array) – Input array.
symbol (str, optional) – Chemical symbol. Default is None.
 Returns
Latent vector.
 Return type
z

forward
(X)[source]¶ Forward propagation
This method takes an input and applies encoder and decoder layers.
 Parameters
X (list) – List of inputs either raw or in the feature space.
 Returns
outputs – Decoded latent vector.
 Return type
tensor

get_latent_space
(X, svm=False, purpose=None)[source]¶ Get latent space for training ML4Chem models
This method takes an input and use the encoder to return latent space in the structure needed for training ML4Chem models or visualization.
 Parameters
X (list) – List of inputs either raw or in the feature space.
svm (bool) – Whether or not these latent vectors are going to be used for kernel methods.
purpose (str) – The purpose for this latent space. This is just useful for the case where the latent space will be preprocessed (purpose=’preprocessing’).
 Returns
latent_space – Latent space with structure: {‘hash’: [(‘H’, [latent_vector]]}
 Return type
dict
Notes
The latent space saved with this function creates a dictionary that can operate with other parts of this package. Note that if you would need to get the latent space for an unseen structure then you will have to forward propagate and get the latent_space.

prepare_model
(input_dimension, output_dimension, data=None, purpose='training')[source]¶ Prepare the model
 Parameters
input_dimension (int) – Input’s dimension.
output_dimension (int) – Output’s dimension.
data (object) – Data object created from the handler.
purpose (str) – Purpose of this model: ‘training’, ‘inference’.

class
ml4chem.atomistic.models.autoencoders.
VAE
(hiddenlayers=None, activation='relu', one_for_all=False, **kwargs)[source]¶ Bases:
ml4chem.atomistic.models.autoencoders.AutoEncoder
Variational Autoencoder (VAE)
This module uses variational autoencoders for pipelines in chemistry.
 Parameters
hiddenlayers (dict) – Dictionary with encoder, and decoder layers in the Auto Encoder.
activation (str) – The activation function.
variant (str) –
The following variants are supported:
”multivariate”: decoder outputs a distribution with mean and variance, we minimize the negative of the log likelihood plus the KLDivergence. Useful for continuous variables. Feature range [inf, inf].
”bernoulli”: decoder outputs a layer with sigmoid activation function, and we minimize crossentropy plus KLdiverence. Features must be in a range [0, 1].
”dcgan”: decoder outputs a single layer with tanh, and loss equals to KLDiverngence plus MSELoss. Useful for feature ranges [1, 1].
one_for_all (bool) – Use one autoencoder model for all atoms instead of a model per atom type as in the BehlerParrinello scheme. Default is False.
Notes
When defining the hiddenlayers keyword argument, input and output dimensions are automatically determined. For example, suppose you have an input data point with 10 dimensions and you want to autoencode with targets having 14 dimensions, a latent space with 4 dimensions and just one hidden layer with 5 nodes between inputlayer / latentlayer and latentlayer / outputlayer. Your hiddenlayers dictionary would look like this:
>>> hiddenlayers = {'encoder': (5, 4), 'decoder': (4, 5)}
That would generate an autoencoder with topology (10, 5, 4  4, 5, 14).

NAME
= 'VAE'¶

decode
(z, symbol=None)[source]¶ Decode latent vector, z
 Parameters
z (array) – Latent vector.
symbol (str, optional) – Chemical symbol. Default is None.
 Returns
Tensor with reconstruction.
 Return type
reconstruction
Notes
See page 11 “Kingma, D. P. & Welling, M. AutoEncoding Variational Bayes. (2013)”.

encode
(x, symbol=None)[source]¶ Encode input
 Parameters
x (array) – Input array.
symbol (str, optional) – Chemical symbol. Default is None.
 Returns
Mean and variance.
 Return type
mu, logvar

forward
(X)[source]¶ Forward propagation
This method takes an input and applies encoder and decoder layers.
 Parameters
X (list) – List of inputs either raw or in the feature space.
 Returns
Decoded latent vector.
 Return type
mu and logvar for two multivariate gaussian

get_latent_space
(X, svm=False, purpose=None)[source]¶ Get latent space for training ML4Chem models
This method takes an input and use the encoder to return latent space in the structure needed for training ML4Chem models or visualization.
 Parameters
X (list) – List of inputs either raw or in the feature space.
svm (bool) – Whether or not these latent vectors are going to be used for kernel methods.
purpose (str) – The purpose for this latent space. This is just useful for the case where the latent space will be preprocessed (purpose=’preprocessing’).
 Returns
latent_space – Latent space with structure: {‘hash’: [(‘H’, [latent_vector]]}
 Return type
dict
Notes
The latent space saved with this function creates a dictionary that can operate with other parts of this package. Note that if you would need to get the latent space for an unseen structure then you will have to forward propagate and get the latent_space.

reparameterize
(mu, logvar, purpose=None)[source]¶ Reparameterization trick
This trick samples the posterior (a latent vector) from a multivariate Gaussian probability distribution. At the same time it allows the model to be backwardpropagated.
 Parameters
mu (tensor) – Mean values of distribution.
logvar (tensor) – Logarithm of variance of distribution.
 Returns
A sample from the distribution.
 Return type
Sample vector

class
ml4chem.atomistic.models.autoencoders.
train
(inputs, targets, model=None, data=None, optimizer=(None, None), regularization=None, epochs=100, convergence=None, lossfxn=None, device='cpu', batch_size=None, lr_scheduler=None, **kwargs)[source]¶ Bases:
object
Train the model
 Parameters
inputs (dict) – Dictionary with hashed feature space.
targets (list) – The expected values that the model has to learn aka y.
model (object) – The NeuralNetwork class.
data (object) – Data object created from the handler.
optimizer (tuple) –
 The optimizer is a tuple with the structure:
>>> ('adam', {'lr': float, 'weight_decay'=float})
epochs (int) – Number of full training cycles.
regularization (float) – This is the L2 regularization. It is not the same as weight decay.
convergence (dict) – Instead of using epochs, users can set a convergence criterion.
lossfxn (obj) – A loss function object.
device (str) – Calculation can be run in the cpu or cuda (gpu).
batch_size (int) – Number of data points per batch to use for training. Default is None.
lr_scheduler (tuple) –
Tuple with structure: scheduler’s name and a dictionary with keyword arguments.
>>> lr_scheduler = ('ReduceLROnPlateau', {'mode': 'min', 'patience': 10})
anneal (bool) – Cyclical annealing based on https://arxiv.org/abs/1903.10145.
penalize_latent (bool) – Set to True if latent vectors are going to be penalized. Default is False.

classmethod
closure
(chunks, targets, model, lossfxn, device, inputs_chunk_vals=None, annealing=None, penalize_latent=False)[source]¶ Closure
This method clears previous gradients, iterates over chunks, accumulate the gradients, update model params, and return loss.

static
get_inputs_chunks
(chunks)[source]¶ Get inputs in chunks for EncoderMapLoss
 Returns
A list with inputs_chunk_vals.
 Return type
inputs_chunk_vals

classmethod
train_batches
(index, chunk, targets, model, lossfxn, device, inputs_chunk_vals, annealing, penalize_latent)[source]¶ A function that allows training per batches
 Parameters
index (int) – Index of batch.
chunk (tensor or list) – Tensor with input data points in batch with index.
targets (tensor or list) – The targets.
model (obj) – Pytorch model to perform forward() and get gradients.
lossfxn (obj) – A loss function object.
device (str) – Are we running cuda or cpu?
inputs_chunk_vals (tensor or list) – Inputs needed by EncoderMapLoss
 Returns
loss – The loss function of the batch.
 Return type
tensor
ml4chem.atomistic.models.gaussian_process module¶

class
ml4chem.atomistic.models.gaussian_process.
GaussianProcess
(sigma=1.0, kernel='rbf', scheduler='distributed', lamda=1e05, trainingimages=None, checkpoints=None, cholesky=True, weights_independent=True, forcetraining=False, nnpartition=None, sum_rule=True, batch_size=None, weights=None)[source]¶ Bases:
ml4chem.atomistic.models.kernelridge.KernelRidge
Gaussian Process Regression
This method is based on the KernelRidge regression class of ML4Chem.
 Parameters
sigma (float, list, or dict) –
Length scale of the Gaussian in the case of RBF, exponential, and laplacian kernels. Default is 1. (float) and it computes isotropic kernels. Pass a list if you would like to compute anisotropic kernels, or a dictionary if you want sigmas for each model.
Example:
>>> sigma={'energy': {'H': value, 'O': value}, 'forces': {'H': {0: value, 1: value, 2: value}, 'O': {0: value, 1: value, 2: value}}}
value can be a float or a list.
kernel (str) – Choose the kernel. Available kernels are: ‘linear’, ‘rbf’, ‘laplacian’, and ‘exponential’. Default is ‘rbf’.
lamda (float, or dictionary) –
Strength of the regularization. If you pass a dictionary then force and energy will have different regularization:
>>> lamda = {'energy': value, 'forces': value}
Dictionaries are only used when performing Cholesky factorization.
trainingimages (str) – Path to Trajectory file containing the images in the training set. This is useful for predicting new structures.
cholesky (bool) – Whether or not we are using Cholesky decomposition to determine the weights. This method returns an unique set of regression coefficients.
weights_independent (bool) – Whether or not the weights are going to be split for energy and forces.
forcetraining (bool) – Turn force training true.
nnpartition (str) – Use peratom energy partition from a neural network calculator. You have to set the path to .amp file. Useful for energy training with Cholesky factorization. Default is set to None.
scheduler (str) – The scheduler to be used with the dask backend.
sum_rule (bool) – Whether or not we sum of fingerprintprime elements over a given axis. This applies np.sum(fingerprint_list, axis=0).
batch_size (int) – Number of elements per batch in order to split computations. Useful when number of local chemical environments is too large.
weights (dict) – Dictionary of weights.
Notes
This regressor applies the atomic decomposition Ansatz (ADA). For more information check the Notes on the KernelRidge class.

NAME
= 'GaussianProcess'¶

get_potential_energy
(features, reference_space, purpose)[source]¶ Get potential energy with Kernel Ridge
 Parameters
features (dict) – Dictionary with hash and features.
reference_space (array) – Array with reference feature space.
purpose (str) – Purpose of this function: ‘training’, ‘inference’.
 Returns
Energy of a molecule and its respective variance.
 Return type
energy, variance

get_variance
(features, ks, reference_space, purpose)[source]¶ Compute predictive variance
 Parameters
features (dict) – Dictionary with data point to be predicted.
ks (array) – Variance between data point and reference space.
reference_space (list) – Reference space used to compute kernel.
purpose (str) – Purpose of this function: ‘training’, ‘inference’.
 Returns
Predictive variance.
 Return type
variance
ml4chem.atomistic.models.kernelridge module¶

class
ml4chem.atomistic.models.kernelridge.
KernelRidge
(sigma=1.0, kernel='rbf', scheduler='distributed', lamda=1e05, trainingimages=None, checkpoints=None, cholesky=True, weights_independent=True, forcetraining=False, nnpartition=None, sum_rule=True, batch_size=None, weights=None, **kwargs)[source]¶ Bases:
object
Kernel Ridge Regression
 Parameters
sigma (float, list, or dict) –
Length scale of the Gaussian in the case of RBF, exponential, and laplacian kernels. Default is 1. (float) and it computes isotropic kernels. Pass a list if you would like to compute anisotropic kernels, or a dictionary if you want sigmas for each model.
Example:
>>> sigma={'energy': {'H': value, 'O': value}, 'forces': {'H': {0: value, 1: value, 2: value}, 'O': {0: value, 1: value, 2: value}}}
value can be a float or a list.
kernel (str) – Choose the kernel. Available kernels are: ‘linear’, ‘rbf’, ‘laplacian’, and ‘exponential’. Default is ‘rbf’.
lamda (float, or dictionary) –
Strength of the regularization. If you pass a dictionary then force and energy will have different regularization:
>>> lamda = {'energy': value, 'forces': value}
Dictionaries are only used when performing Cholesky factorization.
trainingimages (str) – Path to Trajectory file containing the images in the training set. This is useful for predicting new structures.
cholesky (bool) – Whether or not we are using Cholesky decomposition to determine the weights. This method returns an unique set of regression coefficients.
weights_independent (bool) – Whether or not the weights are going to be split for energy and forces.
forcetraining (bool) – Turn force training true.
nnpartition (str) – Use peratom energy partition from a neural network calculator. You have to set the path to .amp file. Useful for energy training with Cholesky factorization. Default is set to None.
scheduler (str) – The scheduler to be used with the dask backend.
sum_rule (bool) – Whether or not we sum of fingerprintprime elements over a given axis. This applies np.sum(fingerprint_list, axis=0).
batch_size (int) – Number of elements per batch in order to split computations. Useful when number of local chemical environments is too large.
weights (dict) – Dictionary of weights.
Notes
In the case of training total energies, we need to apply either an atomic decomposition Ansatz (ADA) during training or an energy partition scheme to the training set. ADA can be achieved based on Ref. 1. For an explanation of what they do, see the Master thesis by Sonja Mathias.
http://wissrech.ins.unibonn.de/teaching/master/masterthesis_mathias_revised.pdf
ADA is the default way of training total energies in this KernelRidge class.
An energy partition scheme for total energies can be obtained from an artificial neural network or methods such as the interacting quantum atoms theory (IQA). I implemented the nnpartition mode for which users can provide the path to a NN calculator and we take the energies peratom from the function .calculate_atomic_energy(). The strategy would be to use train the NN with a very tight convergence criterion (1e6 RSME). Then, calling .calculate_atomic_energy() would give you the atomic energies for such set.
For forces is a different history because we do know the derivative of the energy with respect to atom positions (a peratom quantity). So we rely on the method in the algorithm shown by Rupp in Ref. 2.
References
1. Bartók, A. P. & Csányi, G. Gaussian approximation potentials: A brief tutorial introduction. Int. J. Quantum Chem. 115, 1051–1057 (2015). 2. Rupp, M. Machine learning for quantum mechanics in a nutshell. Int. J.
Quantum Chem. 115, 1058–1073 (2015).

NAME
= 'KernelRidge'¶

get_kernel_matrix
(feature_space, reference_features, purpose)[source]¶ Get kernel matrix delayed computations
 Parameters
features (dict, list) – Dictionary with hash and features, or a list.
reference_space (array) – Array with reference feature space.
purpose (str) – Purpose of this kernel matrix. Accepted arguments are ‘training’, and ‘inference’.
 Returns
List with kernel matrix values.
 Return type
kernel_matrix
Notes
This class method expects the feature_space to be an OrderedDict and reference_space but it turns out that for computing variances, it might be the case the feature_space is also a list.

get_lt
¶

get_potential_energy
(features, reference_space, purpose)[source]¶ Get potential energy with Kernel Ridge
 Parameters
features (dict) – Dictionary with hash and features.
reference_space (array) – Array with reference feature space.
purpose (str) – Purpose of this function: ‘training’, ‘inference’.
 Returns
Energy of a molecule.
 Return type
energy

get_sigma
(sigma, forcetraining=False)[source]¶ Function to build sigma
 Parameters
sigma (float, list or dict.) – This is user’s raw input for sigma.
forcetraining (bool) – Whether or not force training is set to true.
 Returns
_sigma – Universal sigma dictionary for KernelRidge.
 Return type
dict

prepare_model
(feature_space, reference_features, data=None, purpose='training')[source]¶ Prepare the Kernel Ridge Regression model
 Parameters
feature_space (dict) – A dictionary with hash, fingerprint structure.
reference_features (dict) – A dictionary with raveled tuples of symbol, atomic fingerprint.
data (object) – Data object created from the handler.
purpose (str) – Purpose of this model: ‘training’, ‘inference’.
Notes
This method builds the atomic kernel matrices and the LT vectors needed to apply the atomic decomposition Ansatz.
ml4chem.atomistic.models.loss module¶

ml4chem.atomistic.models.loss.
AtomicMSELoss
(outputs, targets, atoms_per_image, uncertainty=None)[source]¶ Default loss function
If user does not input loss function we provide meansquared error loss function.
 Parameters
outputs (tensor) – Outputs of the model.
targets (tensor) – Expected value of outputs.
atoms_per_image (tensor) – A tensor with the number of atoms per image.
uncertainty (tensor, optional) – A tensor of uncertainties that are used to penalize during the loss function evaluation.
 Returns
loss – The value of the loss function.
 Return type
tensor

ml4chem.atomistic.models.loss.
EncoderMapLoss
(inputs, outputs, targets, latent, periodicity=inf, k_c=1.0, k_auto=1.0, k_sketch=1.0, sigma_h=4.5, a_h=12.0, b_h=6.0, sigma_l=1.0, a_l=2.0, b_l=6.0)[source]¶ Encodermap loss function
 Parameters
inputs (tensor) – Inputs of the model.
outputs (tensor) – Outputs of the model.
targets (tensor) – Expected value of outputs.
latent (tensor) – The latent space tensor.
periodicity (float) – Defines the distance between periodic walls for the inputs. For example 2pi for angular values in radians. All periodic data processed by EncoderMap must be wrapped to one periodic window. E.g. data with 2pi periodicity may contain values from pi to pi or from 0 to 2pi. Default is float(“inf”) – nonperiodic inputs.
k_auto (float) – Contribution of distance loss function to total loss.
k_sketch (float) – Contribution of sketch map loss function to total loss.
 Returns
loss – The value of the loss function.
 Return type
tensor
Notes
This loss function combines a distance measure between outputs and targets plus a sketchmap loss plus a regularization. See Eq. (5) from paper referenced above.
When passing it to the Autoencoder() class, the model basically becomes an atomcentered model with the encodermap variant.
There is something to note about regularization for this loss function. Autors of EncoderMap penalize both the weights using L2 regularization, and the magnitude of activation in the latent space layer. is added in the optimizer The L2 regularization is included using weight_decay in the optimizer of choice. The activation penalization is computed below.
References
This is the implementation of the encodermap loss function as proposed by:
Lemke, T., & Peter, C. (2019). EncoderMap: Dimensionality Reduction and Generation of Molecule Conformations. Journal of Chemical Theory and Computation, 15(2), 1209–1215. researcharticle.

ml4chem.atomistic.models.loss.
MSELoss
(outputs, targets)[source]¶ Meansquared error loss function
 Parameters
outputs (tensor) – Outputs of the model.
targets (tensor) – Expected value of outputs.
 Returns
loss – The value of the loss function.
 Return type
tensor

ml4chem.atomistic.models.loss.
SumSquaredDiff
(outputs, targets)[source]¶ Sum of squared differences loss function
 Parameters
outputs (tensor) – Outputs of the model.
targets (tensor) – Expected value of outputs.
 Returns
loss – The value of the loss function.
 Return type
tensor
Notes
In the literature it is mentioned that for realvalued autoencoders the reconstruction loss function is the sum of squared differences.

ml4chem.atomistic.models.loss.
VAELoss
(outputs=None, targets=None, mus_latent=None, logvars_latent=None, mus_decoder=None, logvars_decoder=None, annealing=None, variant=None, latent=None, input_dimension=None)[source]¶ Variational Autoencoder loss function
 Parameters
outputs (tensor) – Outputs of the model.
targets (tensor) – Expected value of outputs.
mus_latent (tensor) – Mean values of distribution.
logvars_latent (tensor) – Logarithm of the variance.
variant (str) –
The following variants are supported:  “multivariate”: decoder outputs a distribution with mean and
variance, we minimize the negative of the log likelihood plus the KLDivergence. Useful for continuous variables. Feature range [inf, inf].
”bernoulli”: decoder outputs a layer with sigmoid activation function, and we minimize crossentropy plus KLdiverence. Features must be in a range [0, 1].
”dcgan”: decoder outputs a single layer with tanh, and loss equals to KLDiverngence plus MSELoss. Useful for feature ranges [1, 1].
annealing (float) – Contribution of distance loss function to total loss.
latent (tensor, optional) – The latent space tensor.
input_dimension (int, optional) – Input’s dimension.
 Returns
loss – The value of the loss function.
 Return type
tensor

ml4chem.atomistic.models.loss.
get_distance
(i, j, periodicity)[source]¶ Get distance between two tensors
 Parameters
i (tensor) – A tensor.
j (tensor) – A tensor.
periodicity (float) – Defines the distance between periodic walls for the inputs.
 Returns
 Return type
tensor with distances.
Notes
Cases where periodicity is present are not yet supported.

ml4chem.atomistic.models.loss.
get_pairwise_distances
(positions, squared=False)[source]¶ Get pairwise distances of a matrix
 Parameters
positions (tensor) – Tensor with positions.
squared (bool, optional) – Whether or not the squared of pairwise distances are computed, by default False.
 Returns
Pairwise distances.
 Return type
distances

ml4chem.atomistic.models.loss.
sigmoid
(r, sigma, a, b)[source]¶ Sigmoid function
 Parameters
r (array) – Pairwise distances.
sigma (float) – Location of the inflection point.
a (float) – Rate in which sigmoid approaches 0 or 1.
b (float) – Rate in which sigmoid approaches 0 or 1.
 Returns
sigmoid – Value of the sigmoid function.
 Return type
float
ml4chem.atomistic.models.merger module¶

class
ml4chem.atomistic.models.merger.
ModelMerger
(models)[source]¶ Bases:
torch.nn.modules.module.Module
Model Merger
A class that can merge models and train them simultaneously. Models are executed sequentially. It is assumed that outputs of model1 are the inputs of model2. This behavior can be modified by adding extra_funcs to call external functions.
 Parameters
models (list) –
 A list of models.
>>> models = [list of models]

NAME
= 'Merged'¶

autoencoders
= ['AutoEncoder', 'VAE']¶

closure
(index, model, independent_loss, name=None)[source]¶ Closure
This method clears previous gradients, iterates over batches, accumulates the gradients, reduces the gradients, update model params, and finally returns loss and outputs_.
 Parameters
index (int) – Index of model.
model (obj) – Model object.
independent_loss (bool) – Whether or not models’ weight are optimized independently.
name (str, optional) – Model class’s name, by default None.
 Returns
A tuple with loss function magnitudes and tensor with outputs.
 Return type
loss, outputs

forward
(X, models)[source]¶ Forward propagation
 Parameters
X (list) – List of models’ inputs.
models (list) – List of model objects.
 Returns
A list with the forward propagation evaluation.
 Return type
outputs

train
(inputs, targets, data=None, optimizer=(None, None), epochs=100, regularization=None, convergence=None, lossfxn=None, device='cpu', batch_size=None, lr_scheduler=None, independent_loss=True, loss_weights=None)[source]¶ Train the models
 Parameters
inputs (dict) – Dictionary with hashed feature space.
targets (list) – The expected values that the model has to learn aka y.
model (object) – The NeuralNetwork class.
data (object) – Data object created from the handler.
optimizer (tuple) –
 The optimizer is a tuple with the structure:
>>> ('adam', {'lr': float, 'weight_decay'=float})
epochs (int) – Number of full training cycles.
regularization (float) – This is the L2 regularization. It is not the same as weight decay.
convergence (dict) –
 Instead of using epochs, users can set a convergence criterion.
>>> convergence = {"rmse": [0.04, 0.02]}
lossfxn (obj) – A loss function object.
device (str) – Calculation can be run in the cpu or cuda (gpu).
batch_size (int) – Number of data points per batch to use for training. Default is None.
lr_scheduler (tuple) –
Tuple with structure: scheduler’s name and a dictionary with keyword arguments.
>>> lr_scheduler = ('ReduceLROnPlateau', {'mode': 'min', 'patience': 10})
independent_loss (bool) – Whether or not models’ weight are optimized independently.
loss_weights (list) – How much the loss of model(i) contributes to the total loss.
ml4chem.atomistic.models.neuralnetwork module¶

class
ml4chem.atomistic.models.neuralnetwork.
NeuralNetwork
(hiddenlayers=(3, 3), activation='relu', **kwargs)[source]¶ Bases:
ml4chem.atomistic.models.base.DeepLearningModel
Atomcentered Neural Network Regression with Pytorch
This model is based on Ref. 1 by Behler and Parrinello.
 Parameters
hiddenlayers (tuple) – Structure of hidden layers in the neural network.
activation (str) – Activation functions. Supported “tanh”, “relu”, or “celu”.
References
Behler, J. & Parrinello, M. Generalized NeuralNetwork Representation of HighDimensional PotentialEnergy Surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Khorshidi, A. & Peterson, A. A. Amp : A modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 207, 310–324 (2016).

NAME
= 'PytorchPotentials'¶

class
ml4chem.atomistic.models.neuralnetwork.
train
(inputs, targets, model=None, data=None, optimizer=(None, None), regularization=None, epochs=100, convergence=None, lossfxn=None, device='cpu', batch_size=None, lr_scheduler=None, uncertainty=None, checkpoint=None, test=None)[source]¶ Bases:
ml4chem.atomistic.models.base.DeepLearningTrainer
Train the model
 Parameters
inputs (dict) – Dictionary with hashed feature space.
targets (list) – The expected values that the model has to learn aka y.
model (object) – The NeuralNetwork class.
data (object) – Data object created from the handler.
optimizer (tuple) –
 The optimizer is a tuple with the structure:
>>> ('adam', {'lr': float, 'weight_decay'=float})
epochs (int) – Number of full training cycles.
regularization (float) – This is the L2 regularization. It is not the same as weight decay.
convergence (dict) – Instead of using epochs, users can set a convergence criterion. Supported keys are “training” and “test”.
lossfxn (obj) – A loss function object.
device (str) – Calculation can be run in the cpu or cuda (gpu).
batch_size (int) – Number of data points per batch to use for training. Default is None.
lr_scheduler (tuple) –
Tuple with structure: scheduler’s name and a dictionary with keyword arguments.
>>> lr_scheduler = ('ReduceLROnPlateau', {'mode': 'min', 'patience': 10})
uncertainty (list) – A list of uncertainties that are used to penalize during the loss function evaluation.
checkpoint (dict) –
Set checkpoints. Dictionary with following structure:
>>> checkpoint = {"label": label, "checkpoint": 100, "path": ""}
label refers to the name used to save the checkpoint, checkpoint is a integer or 1 for saving all epochs, and the path is where the checkpoint is stored. Default is None and no checkpoint is saved.
test (dict) –
A dictionary used to compute the error over a validation/test set during training procedures.
>>> test = {"features": test_space, "targets": test_targets, "data": data_test}
The keys,values of the dictionary are:
”data”: a Data object.
”targets”: test set targets.
”features”: a feature space obtained using features.calculate().

classmethod
closure
(chunks, targets, uncertainty, model, lossfxn, atoms_per_image, device)[source]¶ Closure
This class method clears previous gradients, iterates over batches, accumulates the gradients, reduces the gradients, update model params, and finally returns loss and outputs_.
 Parameters
Cls (object) – Class object.
chunks (tensor or list) – Tensor with input data points in batch with index.
targets (tensor or list) – The targets.
uncertainty (list) – A list of uncertainties that are used to penalize during the loss function evaluation.
model (obj) – Pytorch model to perform forward() and get gradients.
lossfxn (obj) – A loss function object.
atoms_per_image (list) – Atoms per image because we are doing atomcentered methods.
device (str) – Are we running cuda or cpu?

classmethod
train_batches
(index, chunk, targets, uncertainty, model, lossfxn, atoms_per_image, device)[source]¶ A function that allows training per batches
 Parameters
index (int) – Index of batch.
chunk (tensor or list) – Tensor with input data points in batch with index.
targets (tensor or list) – The targets.
model (obj) – Pytorch model to perform forward() and get gradients.
uncertainty (list) – A list of uncertainties that are used to penalize during the loss function evaluation.
lossfxn (obj) – A loss function object.
atoms_per_image (list) – Atoms per image because we are doing atomcentered methods.
device (str) – Are we running cuda or cpu?
 Returns
loss – The loss function of the batch.
 Return type
tensor
ml4chem.atomistic.models.rt module¶
ml4chem.atomistic.models.se3net module¶

class
ml4chem.atomistic.models.se3net.
AvgSpacial
[source]¶ Bases:
torch.nn.modules.module.Module

forward
(inp)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.


class
ml4chem.atomistic.models.se3net.
SE3Net
(num_classes, size, activation='relu')[source]¶ Bases:
torch.nn.modules.module.Module
Rotational equivariant neural network
 Parameters
num_classes (int) –
size (int) –
activation (str) –

forward
(inputs, difference_mat)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.