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MLPotential MLPotential MLPotential
  • 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
  • MLPotential
  • Index
  • General
  • Installation & Uninstallation
  • Models & Backends
  • CPU & GPU (CUDA), parallelization
  • Examples
  • AMS driver’s tasks and properties
  • Frequently Asked Questions
  • MLPotential Keywords
  • KF output files
  1. Documentation /
  2. MLPotential /
  3. MLPotential Manual 2026.1

MLPotential Manual 2026.1¶

MLPotential is an interface between the Amsterdam Modeling Suite and several types of machine learning potential backends.

  • PDF version of the MLPotential Manual

Table of Contents:

  • General
  • Installation & Uninstallation
  • Models & Backends
  • CPU & GPU (CUDA), parallelization
  • Examples
  • AMS driver’s tasks and properties
  • Frequently Asked Questions
  • MLPotential Keywords
  • KF output files

  • Index

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