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Amsterdam Modeling Suite
Atomistic Scale
Electronic Structure
ADF

Understand and predict chemical properties with our fast and accurate molecular DFT code.

Periodic DFT

BAND & Quantum Espresso: Calculate reactivity, band gaps, optical response, and other properties for periodic systems.

DFTB & MOPAC

Model larger molecules and periodic systems, or prescreen many candidates, with the fast electronic structure methods DFTB and MOPAC.
Interatomic Potentials
ReaxFF

Study large, chemically evolving systems with ReaxFF molecular dynamics.

Machine Learning Potentials

Use preparametrized ML potentials M3GNET, ANI-1ccx or your own models.

Force Fields

GFN-FF, Apple&P, UFF, and more- (polarizable) force fields.
Meso- & Macroscale
kMC and Microkinetics

Predict catalytic turn-over frequencies with microkinetics and kinetic Monte Carlo.
Bumblebee: OLED stacks

3D kinetic Monte Carlo for simulating OLED device-level physics
Fluid Thermodynamics
COSMO-RS

Quick physical property predictions, thermodynamic properties in solution, and solvent screening.

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OLED workflows

Automatic workflows to simulate physical vapor deposition and calculate properties for OLED device modeling.
ChemTraYzer2

Automatically extract reaction pathways and reaction rates from reactive MD trajectories.
Conformers

Easily generate, screen, refine, and select conformers. Pass on to other modules for conformational averaging.
Reactions Discovery

Predict chemical (side) reactions from nothing but constituent molecules.
AMS Driver
Properties

Calculate frequencies, phonons, and more. Use forces and energies from AMS or external engines.
PES Exploration

Minimize structures, find transitions states, scan multiple coordinates.
Molecular Dynamics

Use advanced thermo- and barostats, non-equilibrium and accelerated MD, molecule gun.

Monte Carlo

Grand Canonical Monte Carlo to study absorption, (dis)charge processes.
Interfaces
ParAMS

Versatile graphical and python scripting tools to create training sets and parametrize DFTB, ReaxFF, and machine learned potentials.
PLAMS

Versatile python scripting interface to create your own computational chemistry workflows
GUI

Powerful graphical interface to set up, run, and analyze calculations. Even across different platforms.
VASP

Interface to popular plane-wave code VASP. Easily set up PES Scans to create training data.

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Home > Documentation

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Table of contents

  • 1. General
  • 2. Tutorials: Getting Started
  • 3. Tutorials: ReaxFF & DFTB
  • 4. Tutorials: Machine Learning Potentials
  • 5. Tutorials: Advanced ParAMS
  • 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
      • 6.2.5. 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
  • 9. User Interventions
  • 10. Required Citations
  • 11. Frequently Asked Questions
ParAMS
  • Documentation/
  • ParAMS/
  • 6. Input file: params.in/
  • 6.2. Task: Optimization

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
  • 6.2.5. 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
Next Previous
AMS Modules
Electronic Structure
ADF: molecular DFT Periodic DFT DFTB & MOPAC
Interatomic Potentials
ReaxFF ML Potentials Force Fields
Kinetics
kMC and Microkinetics Bumblebee: OLEDs
Macroscale
COSMO-RS
Application Areas
Research Topics
Batteries Biotechnology Bonding Analysis Catalysis Heavy Elements Inorganic Chemistry Materials Science Nanoscience Oil & Gas OLEDs Perovskites Polymers Semiconductors Spectroscopy
Where to use AMS?
Industry Government Lab National Supercomputer Academic Research Teaching
Tools
Workflows
Conformers OLED workflows Reaction analysis Reaction discovery
AMS Driver
Hybrid Engine Molecular Dynamics Monte Carlo PES Exploration Properties
Python Utilities
ACErxn ParAMS PLAMS pyZacros
Interfaces
GUI VASP Parametrization
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