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mlcolvar documentation
mlcolvar documentation

Contents:

  • Installation
  • Documentation
    • Collective variables
      • mlcolvar.cvs.BaseCV
      • mlcolvar.cvs.AutoEncoderCV
      • mlcolvar.cvs.VariationalAutoEncoderCV
      • mlcolvar.cvs.DeepLDA
      • mlcolvar.cvs.DeepTDA
      • mlcolvar.cvs.RegressionCV
      • mlcolvar.cvs.DeepTICA
      • mlcolvar.cvs.MultiTaskCV
      • mlcolvar.cvs.Committor
    • Core modules
      • mlcolvar.core.nn.FeedForward
      • mlcolvar.core.nn.graph.BaseGNN
      • mlcolvar.core.nn.graph.SchNetModel
      • mlcolvar.core.nn.graph.GVPModel
      • mlcolvar.core.loss.MSELoss
      • mlcolvar.core.loss.ELBOGaussiansLoss
      • mlcolvar.core.loss.FisherDiscriminantLoss
      • mlcolvar.core.loss.AutocorrelationLoss
      • mlcolvar.core.loss.ReduceEigenvaluesLoss
      • mlcolvar.core.loss.TDALoss
      • mlcolvar.core.loss.CommittorLoss
      • mlcolvar.core.loss.GeneratorLoss
      • mlcolvar.core.loss.SmartDerivatives
      • mlcolvar.core.stats.Stats
      • mlcolvar.core.stats.PCA
      • mlcolvar.core.stats.LDA
      • mlcolvar.core.stats.TICA
      • mlcolvar.core.transform.Transform
      • mlcolvar.core.transform.descriptors.PairwiseDistances
      • mlcolvar.core.transform.descriptors.TorsionalAngles
      • mlcolvar.core.transform.descriptors.CoordinationNumbers
      • mlcolvar.core.transform.descriptors.EigsAdjMat
      • mlcolvar.core.transform.descriptors.MultipleDescriptors
      • mlcolvar.core.transform.tools.Normalization
      • mlcolvar.core.transform.tools.ContinuousHistogram
      • mlcolvar.core.transform.tools.SwitchingFunctions
    • Data
      • mlcolvar.data.DictDataset
      • mlcolvar.data.DictLoader
      • mlcolvar.data.DictModule
      • mlcolvar.data.graph.AtomicNumberTable
      • mlcolvar.data.graph.Configuration
      • mlcolvar.data.graph.get_neighborhood
      • mlcolvar.data.graph.create_dataset_from_configurations
    • Utils
      • mlcolvar.io.load_dataframe
      • mlcolvar.io.create_dataset_from_files
      • mlcolvar.utils.timelagged.create_timelagged_dataset
      • mlcolvar.utils.fes.compute_fes
      • mlcolvar.utils.trainer.MetricsCallback
      • mlcolvar.utils.plot.plot_metrics
      • mlcolvar.utils.plot.plot_features_distribution
    • Explain
      • mlcolvar.explain.sensitivity.sensitivity_analysis
      • mlcolvar.explain.sensitivity.plot_sensitivity
      • mlcolvar.explain.graph_sensitivity.graph_node_sensitivity
      • mlcolvar.explain.lasso.lasso_classification
      • mlcolvar.explain.lasso.lasso_regression
      • mlcolvar.explain.lasso.plot_lasso_classification
      • mlcolvar.explain.lasso.plot_lasso_regression
  • Tutorials
    • Getting Started
      • Training your first collective variable
      • Creating datasets
      • Customize training
    • Methods for CVs optimization
      • AutoEncoding collective variables
      • Deep-LDA: Neural network-based discriminant CVs
      • Deep-TDA: Deep Targeted Discriminant Analysis
      • Deep-TICA: Neural network basis functions for TICA
      • Learning the committor
    • Customizing CVs
      • Multi-task framework
      • Implementing a new CV from scratch
      • Implementing new CVs via subclassing
      • Pre/post processing
      • Transform module of mlcolvar
      • Using mlcolvar with graph neural networks (GNNs)
      • Advanced GNNs functionalities: truncated graphs and long range interactions
    • Interpreting CVs
      • Sensitivity analysis
      • Sparse linear models
    • Utils
      • Utils: Compute and plot free energy surface and free energy differences
  • Examples
    • Modified Muller-Brown
      • Unsupervised setting: state discovery
      • Supervised setting: CVs as classifiers
      • Time-lagged setting: improving CVs
      • Multitask learning: a semi-supervised application
    • DeepLDA: Alanine dipeptide and aldol reaction
    • DeepTICA: Alanine dipeptide from multithermal simulation
    • TPI-DeepTDA: Chignolin mini-protein
    • Stateinterpreter (characterizing DeepTICA states)
    • Learning the committor for Alanine with distances as inputs
    • Learning the committor with graph neural networks (GNNs)
    • Learning the committor for Tropolone intramolecular proton-transfer with distances as inputs
  • PLUMED module
  • Contributing
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  • Utils: Compute and plot free energy surface and free energy differences
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