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ParAMS ParAMS ParAMS
  • Installation
    • Windows
    • Linux
    • MacOS
    • Package manager Install optional components
  • Tutorials
    • Getting started
    • All tutorials
  • AMS Driver
    • Geometry and System
    • Geometry Optimization
    • Molecular Dynamics
    • Nudged Elastic Band (NEB)
    • PES Exploration
    • PES Scan, Linear Transit
    • Transition State Search
    • Vibrational Spectroscopy
  • Engines
    • ADF Molecular DFT
    • BAND Periodic DFT
    • DFTB Semi-empirical
    • ForceField AMBER, UFF, APPLE&P...
    • GFNFF Force field
    • Hybrid QM/MM and mixed methods
    • ML Potential Machine Learning potentials
    • MOPAC Semi-empirical
    • Quantum ESPRESSO Periodic DFT
    • VASP Periodic DFT
    • ReaxFF Reactive force field
    • ASE engine Use any ASE calculator
  • Meso/Macro
    • COSMO-RS Thermodynamic properties of fluids
    • Bumblebee OLED device modeling
    • Microkinetics
    • Zacros Kinetic Monte Carlo
  • Tools
    • GUI Graphical User Interface
    • ParAMS Parametrization tool
    • Simple Active Learning On-the-fly training of ML
    • OLED Deposition and Properties Multiscale OLED modeling
    • Reactions Discovery Automatic reaction-discovery workflow
    • ACE Reaction Network Generation of reaction networks
    • Conformers Conformers generation
    • MD Trajectory Analysis Analysis of MD trajectories
    • ChemTraYzer2 Detect reactions from MD simulations
    • Utilities Various utility tools
  • Scripting
    • Python Scripting Examples Examples for Python scripting
    • PLAMS Python Library for Automating Molecular Simulations
    • amspython Python stack shipped with AMS
    • pyCRS Python Scripting with COSMO-RS
    • SCM Base Library Python library with core modules
    • reactmap Atom mapping between reactants and products
    • Command-line tools
    • ASE calculator
    • pyZacros Python Library Zacros (Kinetic Monte Carlo)
  • AMS2026.1
  • Other versions
  • ParAMS

Table of contents

  • 1. General
  • 2. Tutorials: Getting Started
    • 2.1. Introduction to parametrization
    • 2.2. Getting Started: Lennard-Jones
    • 2.3. Import training data (GUI)
  • 3. Tutorials: ReaxFF & DFTB
    • 3.1. ReaxFF (basic): H₂O bond scan
    • 3.2. ReaxFF (advanced): ZnS
    • 3.3. ReaxFF: Convert old file format
    • 3.4. ReaxFF: Training set for cobalt
    • 3.5. GFN1-xTB: Lithium fluoride
    • 3.6. DFTB repulsive potential
  • 4. Tutorials: Machine Learning Potentials
    • 4.1. Train M3GNet with the ParAMS GUI
    • 4.2. Train MACE with the ParAMS GUI
  • 5. Tutorials: Advanced ParAMS
    • 5.1. Simple benchmark calculations (ParAMS Task SinglePoint)
    • 5.2. Benchmarks from literature (advanced)
    • 5.3. Generate reference values
    • 5.4. Parallel optimizers
    • 5.5. Parameter sensitivity analysis
    • 5.6. Restarting an optimization
    • 5.7. Training and validation sets
    • 5.8. ASE Calculator parametrization
  • 6. Input file: params.in
    • 6.1. General input
    • 6.2. Task: Optimization
      • 6.2.1. Optimization in ParAMS 2023.1
      • 6.2.2. General Naming Pattern
      • 6.2.3. Optimizers
      • 6.2.4. Stoppers and Exit Conditions
        • 6.2.4.1. Difference Between Stoppers and ExitConditions
        • 6.2.4.2. Optimizer Stoppers
        • 6.2.4.3. Exit Conditions
      • 6.2.5. Generator, Selector, Spawning
      • 6.2.6. Parallelization options
      • 6.2.7. Checkpoints
      • 6.2.8. Output and logging
      • 6.2.9. Parameter constraints
      • 6.2.10. Other input
    • 6.3. Task: SinglePoint
    • 6.4. Task: Sensitivity
    • 6.5. Task: GenerateReference
    • 6.6. Task: MachineLearning
  • 7. ParAMS Main Script
  • 8. Python Classes and Functions
    • 8.1. Results Importer
      • 8.1.1. ResultsImporter overview
      • 8.1.2. AMS, VASP, Quantum ESPRESSO, and Gaussian reference data
      • 8.1.3. ResultsImporters API
    • 8.2. Extractors and Comparators
    • 8.3. Parameter Interfaces
      • 8.3.1. Available Parameter Interfaces
        • 8.3.1.1. GFN1-xTB
        • 8.3.1.2. ReaxFF
        • 8.3.1.3. SCC-DFTB repulsive potential
        • 8.3.1.4. Lennard-Jones
        • 8.3.1.5. No parameters (empty interface)
        • 8.3.1.6. ASE Parameters
      • 8.3.2. Working with Parameter Interfaces
    • 8.4. ParAMSJob
    • 8.5. ParAMSResults
    • 8.6. Job and Engine Collections
    • 8.7. Data Set
    • 8.8. Data Set Evaluator
    • 8.9. Utilities
      • 8.9.1. ParAMS Converters
      • 8.9.2. Plotting Functions
      • 8.9.3. Helper functions
      • 8.9.4. Weights schemes
    • 8.10. Internal ParAMS classes
      • 8.10.1. Architecture Quick Reference
      • 8.10.2. Optimization
      • 8.10.3. Parallelization
      • 8.10.4. Constraints
      • 8.10.5. Logger
      • 8.10.6. Loss Functions
      • 8.10.7. GloMPO Backend
      • 8.10.8. Experimental Features
        • 8.10.8.1. Working with Data Sets
  • 9. User Interventions
  • 10. Required Citations
  • 11. Frequently Asked Questions
  1. Documentation /
  2. ParAMS /
  3. 8. Python Classes and Functions /
  4. 8.3. Parameter Interfaces

8.3. Parameter Interfaces¶

Any AMS engine that can be parameterized is represented by an Interface class (derived from BaseParameters). This mostly acts as a translation layer between the Optimizer and AMS.

  • 8.3.1. Available Parameter Interfaces
    • 8.3.1.1. GFN1-xTB
    • 8.3.1.2. ReaxFF
    • 8.3.1.3. SCC-DFTB repulsive potential
    • 8.3.1.4. Lennard-Jones
    • 8.3.1.5. No parameters (empty interface)
    • 8.3.1.6. ASE Parameters
  • 8.3.2. Working with Parameter Interfaces
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8.2. Extractors and Comparators
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8.3.1. Available Parameter Interfaces

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