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Table of contents

  • 1. General
    • 1.1. What is ParAMS?
    • 1.2. What’s new in ParAMS 2022?
    • 1.3. Theory of Parameter Fitting: A Lennard-Jones Example
    • 1.4. General Application
  • 2. Tutorials
    • 2.1. Introduction to parametrization
      • 2.1.1. Training data
        • 2.1.1.1. Where does training data come from?
        • 2.1.1.2. What items can I include in my training set?
        • 2.1.1.3. What types of jobs should I run?
        • 2.1.1.4. What should I consider when designing my training set?
      • 2.1.2. Loss function
        • 2.1.2.1. How do I balance my training set?
        • 2.1.2.2. How do I minimize the loss function?
      • 2.1.3. Model parameters
      • 2.1.4. Where to now?
    • 2.2. Getting Started: Lennard-Jones Potential for Argon
      • 2.2.1. Lennard-Jones Parameters, Engine, and Interface
      • 2.2.2. Files
      • 2.2.3. ParAMS input
        • 2.2.3.1. Parameter interface (parameter_interface.yaml)
        • 2.2.3.2. Job Collection (job_collection.yaml)
        • 2.2.3.3. Training Set (training_set.yaml)
        • 2.2.3.4. ParAMS settings (params.conf.py)
      • 2.2.4. Run the example
      • 2.2.5. Parametrization results
        • 2.2.5.1. The best parameter values
        • 2.2.5.2. Correlation plots
        • 2.2.5.3. Error plots
        • 2.2.5.4. Parameter plots
        • 2.2.5.5. Editing and Saving Plots
        • 2.2.5.6. Predicted values
        • 2.2.5.7. Loss contributions
        • 2.2.5.8. Summary statistics
        • 2.2.5.9. All output files
        • 2.2.5.10. summary.txt
      • 2.2.6. Close the ParAMS GUI
      • 2.2.7. Appendix: Creation of the input files
      • 2.2.8. Next steps
    • 2.3. Import training data (GUI)
      • 2.3.1. Choose preferred units
      • 2.3.2. Reference calculation #1: Geometry optimization of a water molecule
      • 2.3.3. Charges and forces
      • 2.3.4. Bonds and angles
      • 2.3.5. PES scans: bond scan, angle scan, or volume scan
      • 2.3.6. Energies
      • 2.3.7. Import a molecular dynamics trajectory
        • 2.3.7.1. Reference calculation #2: MD simulation of liquid Ar
        • 2.3.7.2. MD with reference method
        • 2.3.7.3. MD with fast method followed by Replay with reference method
      • 2.3.8. Import a structure and settings from AMSinput
      • 2.3.9. Import VASP or Quantum ESPRESSO calculations
    • 2.4. Import training data (Python)
      • 2.4.1. Run a reference job
      • 2.4.2. Water molecule reference data
      • 2.4.3. Initialize the ResultsImporter
      • 2.4.4. Add a singlepoint calculation on the optimized geometry
      • 2.4.5. Add a geometry optimization job extracting the bond length and bond angle
      • 2.4.6. Add a trajectory
      • 2.4.7. Add a reaction energy
      • 2.4.8. Training set, validation set, and other data sets
      • 2.4.9. Exit PLAMS
      • 2.4.10. Save to disk
      • 2.4.11. More ResultsImporters
    • 2.5. ReaxFF: Gaseous H₂O
      • 2.5.1. Calculate the reference data
      • 2.5.2. Import the reference data into ParAMS
      • 2.5.3. Set the parameters to optimize
      • 2.5.4. Run the ReaxFF parametrization
      • 2.5.5. Visualize the results
      • 2.5.6. ffield.ff: The force field file
      • 2.5.7. Summary of the 3 different ways
      • 2.5.8. Next steps with ReaxFF parametrization
    • 2.6. ReaxFF: Adsorption on ZnS(110)
      • 2.6.1. Mix force fields from the AMS ReaxFF library
      • 2.6.2. Choose the parameters to optimize
      • 2.6.3. ZnS training and validation set
      • 2.6.4. Run the ZnS ReaxFF parametrization
      • 2.6.5. ZnS parametrization results
        • 2.6.5.1. ZnS energies
        • 2.6.5.2. ZnS volume scans
        • 2.6.5.3. H₂S bond and angle scans
        • 2.6.5.4. ZnS optimized distances
        • 2.6.5.5. ZnS forces on distorted structures
    • 2.7. ReaxFF: Convert old training sets to ParAMS format
      • 2.7.1. Convert geo (and control) to job_collection.yaml
      • 2.7.2. Convert trainset.in to training_set.yaml
      • 2.7.3. Convert ffield (and params) to parameter_interface.yaml
      • 2.7.4. Using convert.py
    • 2.8. ReaxFF: Training set for cobalt
      • 2.8.1. Weighting of individual entries
      • 2.8.2. General energies, Cluster models and the Co₂ dimer
      • 2.8.3. Description of crystalline phases
      • 2.8.4. Description of Co-surfaces
      • 2.8.5. Adatoms
      • 2.8.6. Vacancies and defects
      • 2.8.7. Elastic strain moduli
    • 2.9. GFN1-xTB: Lithium fluoride
      • 2.9.1. Energy-volume scan reference calculation for LiF
      • 2.9.2. Import the energy-volume scan into ParAMS
      • 2.9.3. Import experimental formation enthalpy into ParAMS
      • 2.9.4. Import experimental F₂ bond length
      • 2.9.5. Job-dependent engine settings (k-space)
      • 2.9.6. Set parameters to optimize and their ranges
      • 2.9.7. Set the optimizer settings
      • 2.9.8. Run the xTB parametrization
      • 2.9.9. Results of the xTB parametrization
    • 2.10. Training and validation sets
      • 2.10.1. Explicit validation set (validation_set.yaml)
        • 2.10.1.1. Validation set settings
        • 2.10.1.2. Run the optimization
        • 2.10.1.3. Training and validation set results
      • 2.10.2. Scripting: Random split of a dataset into training and validation sets
    • 2.11. Restarting (continuing) an optimization
      • 2.11.1. Restarting with the CMAOptimizer
        • 2.11.1.1. Run the first 100 evaluations
        • 2.11.1.2. Continue for another 100 iterations with a restart file
        • 2.11.1.3. Compare to an uninterrupted run of 200 iterations
      • 2.11.2. Restart with other optimizers (e.g. Nelder-Mead from Scipy)
    • 2.12. Calculate reference values with ParAMS
      • 2.12.1. Prerequisites
      • 2.12.2. The input files
        • 2.12.2.1. Training set without reference values (training_set.yaml)
        • 2.12.2.2. Jobs and Engines (job_collection.yaml, job_collection_engines.yaml)
      • 2.12.3. Calculate the reference values
      • 2.12.4. Output files for reference calculations and data
        • 2.12.4.1. The reference.cache folder
        • 2.12.4.2. The training_set.ref.yaml file
    • 2.13. The params Python library
      • 2.13.1. Run an optimization with the Python library
      • 2.13.2. Calculate reference values
      • 2.13.3. Training and validation sets with the Python library
    • 2.14. DFTB repulsive potential
      • 2.14.1. job_collection.yaml and training_set.yaml
      • 2.14.2. Background: znorg-0-1
      • 2.14.3. Parameter interface
      • 2.14.4. Run the parametrization
      • 2.14.5. Results
      • 2.14.6. Modify the analytical repulsive potential
  • 3. ParAMS Main Script
    • 3.1. The Configuration File
      • 3.1.1. Generate a template config file
      • 3.1.2. List of variables in params.conf.py
  • 4. Python Classes and Functions
    • 4.1. Architecture Quick Reference
      • 4.1.1. Diagram
      • 4.1.2. Simple usage of some classes
    • 4.2. Job and Engine Collections
      • 4.2.1. Job Collection
        • 4.2.1.1. Adding Jobs
        • 4.2.1.2. Lookup
        • 4.2.1.3. Removing Entries
        • 4.2.1.4. Renaming Entries
        • 4.2.1.5. Comparison
        • 4.2.1.6. Saving and loading
        • 4.2.1.7. Generating AMSJobs
        • 4.2.1.8. Running Collection Jobs
      • 4.2.2. Engine Collection
      • 4.2.3. Collections API
        • 4.2.3.1. JCEntry
        • 4.2.3.2. JobCollection
        • 4.2.3.3. Engine
        • 4.2.3.4. EngineCollection
        • 4.2.3.5. Collection Base Class
    • 4.3. Data Set
      • 4.3.1. An example DataSet
      • 4.3.2. Load or store DataSet
      • 4.3.3. Adding entries
      • 4.3.4. Demonstration: Working with a DataSet
        • 4.3.4.1. Add an entry
        • 4.3.4.2. DataSetEntry attributes
        • 4.3.4.3. Accessing the DataSet entries
        • 4.3.4.4. Delete a DataSet entry
        • 4.3.4.5. Split a DataSet into subsets
        • 4.3.4.6. DataSet header
        • 4.3.4.7. Save the data set
      • 4.3.5. Calculating and Adding Reference Data with AMS
      • 4.3.6. Calculating the Loss Function Value
      • 4.3.7. Checking for Consistency with a given Job Collection
      • 4.3.8. Sigma vs. weight: What is the difference?
      • 4.3.9. Data Set Entry API
      • 4.3.10. Data Set API
    • 4.4. Extractors and Comparators
      • 4.4.1. Available Extractors
        • 4.4.1.1. Angle
        • 4.4.1.2. Average distance
        • 4.4.1.3. Bulk modulus
        • 4.4.1.4. Cell angles
        • 4.4.1.5. Cell lengths
        • 4.4.1.6. Cell volume
        • 4.4.1.7. Charges
        • 4.4.1.8. Dihedral
        • 4.4.1.9. Distance
        • 4.4.1.10. Distance vector
        • 4.4.1.11. Energy
        • 4.4.1.12. Forces
        • 4.4.1.13. Hessian
        • 4.4.1.14. PES
        • 4.4.1.15. PES compared
        • 4.4.1.16. PESScan angle
        • 4.4.1.17. PESScan dihedral
        • 4.4.1.18. PESScan distance
        • 4.4.1.19. RMSD
        • 4.4.1.20. Shear modulus
        • 4.4.1.21. Stress tensor
        • 4.4.1.22. Stress tensor 1D
        • 4.4.1.23. Stress tensor 2D
        • 4.4.1.24. Stress tensor 3D
        • 4.4.1.25. Stress tensor diagonal 2D
        • 4.4.1.26. Stress tensor diagonal 3D
        • 4.4.1.27. Stress tensor off-diagonal 2D
        • 4.4.1.28. Stress tensor off-diagonal 3D
        • 4.4.1.29. Vibrational frequencies
        • 4.4.1.30. Young modulus
      • 4.4.2. Custom Extractors
      • 4.4.3. Supported Data Structures
      • 4.4.4. Custom Comparators
    • 4.5. Data Set Evaluator
      • 4.5.1. DataSetEvaluator class
        • 4.5.1.1. Example: DataSetEvaluator.calculate_reference()
        • 4.5.1.2. Example: DataSetEvaluator.run()
        • 4.5.1.3. Example: Load a saved DataSetEvaluator
      • 4.5.2. DataSetEvaluator API
    • 4.6. Results Importer
      • 4.6.1. ResultsImporter overview
        • 4.6.1.1. ResultsImporter summary
        • 4.6.1.2. ResultsImporter settings
        • 4.6.1.3. Training set, validation set, etc.
        • 4.6.1.4. Save and load from disk
        • 4.6.1.5. add_singlejob
        • 4.6.1.6. add_trajectory_singlepoints
        • 4.6.1.7. add_reaction_energy
        • 4.6.1.8. add_pesscan_singlepoints
        • 4.6.1.9. add_neb_singlepoints
        • 4.6.1.10. add_pesexploration_singlepoints
      • 4.6.2. AMS, VASP and Quantum ESPRESSO reference data
        • 4.6.2.1. AMS
        • 4.6.2.2. VASP
        • 4.6.2.3. Quantum ESPRESSO
      • 4.6.3. ResultsImporters API
    • 4.7. Parameter Interfaces
      • 4.7.1. Available Parameter Interfaces
        • 4.7.1.1. GFN1-xTB
        • 4.7.1.2. ReaxFF
        • 4.7.1.3. SCC-DFTB repulsive potential
        • 4.7.1.4. Lennard Jones
      • 4.7.2. Parameter Interface Basics
      • 4.7.3. Working with Parameters
      • 4.7.4. The Active Parameters Subset
      • 4.7.5. Storage
        • 4.7.5.1. Lossless Storage
      • 4.7.6. Relation to PLAMS Settings
      • 4.7.7. Parameter API
      • 4.7.8. Interface Base Class API
    • 4.8. Optimizers
      • 4.8.1. CMA-ES
        • 4.8.1.1. List of valid cmasettings
        • 4.8.1.2. References
      • 4.8.2. Scipy
      • 4.8.3. Nevergrad
      • 4.8.4. Adaptive Rate MC
      • 4.8.5. Simple Grid Optimizer
      • 4.8.6. Optimizer Base Class
        • 4.8.6.1. BaseOptimizer API
        • 4.8.6.2. MinimizeResult API
    • 4.9. Optimization
      • 4.9.1. Optimization Setup
      • 4.9.2. Optimization API
    • 4.10. Parallelization
    • 4.11. Constraints
    • 4.12. Callbacks
      • 4.12.1. Default callbacks
      • 4.12.2. Logger
      • 4.12.3. Timeout
      • 4.12.4. Target Value
      • 4.12.5. Maximum Iterations
      • 4.12.6. Early Stopping
      • 4.12.7. Stopfile
      • 4.12.8. Time per Evaluation
      • 4.12.9. Load Average
      • 4.12.10. User-Defined Callbacks
      • 4.12.11. Callback API
    • 4.13. Loss Functions
      • 4.13.1. Specifying the loss function
      • 4.13.2. Technical information
      • 4.13.3. Sum of Squares Error
      • 4.13.4. Sum of Absolute Errors
      • 4.13.5. Mean Absolute Error
      • 4.13.6. Root-Mean-Square Error
      • 4.13.7. Loss Function API
    • 4.14. Utilities
      • 4.14.1. ParAMS Converters
        • 4.14.1.1. AMS Job to Settings
        • 4.14.1.2. AMS Job to Engine
      • 4.14.2. Plotting Functions
      • 4.14.3. Helper functions
      • 4.14.4. Weights schemes
        • 4.14.4.1. Types of weights schemes
        • 4.14.4.2. Examples of weight schemes
        • 4.14.4.3. Weights schemes API
    • 4.15. Experimental Features
      • 4.15.1. Working with Parameter Interfaces
        • 4.15.1.1. Active Parameter Search
      • 4.15.2. Working with Data Sets
        • 4.15.2.1. Data Set Sensitivity
        • 4.15.2.2. Normalization of Data Set Weights
  • 5. Required Citations
  • 6. Frequently Asked Questions
ParAMS
  • Documentation/
  • ParAMS/
  • Parameterization Tools for AMS

Parameterization Tools for AMS¶

Table of contents

  • 1. General
    • 1.1. What is ParAMS?
    • 1.2. What’s new in ParAMS 2022?
    • 1.3. Theory of Parameter Fitting: A Lennard-Jones Example
    • 1.4. General Application
  • 2. Tutorials
    • 2.1. Introduction to parametrization
    • 2.2. Getting Started: Lennard-Jones Potential for Argon
    • 2.3. Import training data (GUI)
    • 2.4. Import training data (Python)
    • 2.5. ReaxFF: Gaseous H₂O
    • 2.6. ReaxFF: Adsorption on ZnS(110)
    • 2.7. ReaxFF: Convert old training sets to ParAMS format
    • 2.8. ReaxFF: Training set for cobalt
    • 2.9. GFN1-xTB: Lithium fluoride
    • 2.10. Training and validation sets
    • 2.11. Restarting (continuing) an optimization
    • 2.12. Calculate reference values with ParAMS
    • 2.13. The params Python library
    • 2.14. DFTB repulsive potential
  • 3. ParAMS Main Script
    • 3.1. The Configuration File
  • 4. Python Classes and Functions
    • 4.1. Architecture Quick Reference
    • 4.2. Job and Engine Collections
    • 4.3. Data Set
    • 4.4. Extractors and Comparators
    • 4.5. Data Set Evaluator
    • 4.6. Results Importer
    • 4.7. Parameter Interfaces
    • 4.8. Optimizers
    • 4.9. Optimization
    • 4.10. Parallelization
    • 4.11. Constraints
    • 4.12. Callbacks
    • 4.13. Loss Functions
    • 4.14. Utilities
    • 4.15. Experimental Features
  • 5. Required Citations
  • 6. Frequently Asked Questions
Next
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