Gradient/sum wrappers

These classes are used to build more complicated models.

class mlcg.nn.gradients.GradientsOut(model, targets='forces')

Gradient wrapper for models.

Parameters:

targets (str) – The gradient targets to produce from a model output. These can be any of the gradient properties referenced in mlcg.data._keys. At the moment only forces are implemented.

Example

To predict forces from an energy model, one would supply a model that predicts a scalar atom property (an energy) and specify the FORCE_KEY in the targets.

class mlcg.nn.gradients.SumOut(models, targets=None)

Property pooling wrapper for models

Parameters:

• models (ModuleDict) – Dictionary of predictors models keyed by their name attribute

• targets (List[str]) – List of prediction targets that will be pooled

Example

To combine SchNet force predictions with prior interactions:

import torch
from mlcg.nn import (StandardSchNet, HarmonicBonds, HarmonicAngles,
                     GradientsOut, SumOut, CosineCutoff,
                     GaussianBasis)
from mlcg.data._keys import FORCE_KEY, ENERGY_KEY

bond_terms = GradientsOut(HarmonicBonds(bond_stats), FORCE_KEY)
angle_terms = GradientsOut(HarmonicAngles(angle_stats), FORCE_KEY)
cutoff = CosineCutoff()
rbf = GaussianBasis(cutoff)
energy_network = StandardSchNet(cutoff, rbf, [128])
force_network = GradientsOut(energy_model, FORCE_KEY)

models = torch.nn.ModuleDict{
             "bonds": bond_terms,
             "angles": angle_terms,
             "SchNet": force_network
         }
full_model = SumOut(models, targets=[ENERGY_KEY, FORCE_KEY])