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Tools overview
AMS Foundation
Any license containing one of the core SCM modules will automatically include the Amsterdam Modeling Suite Foundation: A graphical user interface, the PLAMS Python scripting environment, and the central AMS Driver for complex tasks on the potential energy surface. It also includes various force fields, workflows, and analysis tools.
| License | AMS Foundation | ML Potential & Force Field |
| AMS Driver | ✓ | |
| GUI (Including support for QE, VASP & ASE) | ✓ | |
| MD Analysis | ✓ | |
| UFF, UFF4MOF, Sybyl, AMBER95 | ✓ | |
| Hybrid Engine, QM/MM | ✓ | |
| PLAMS | ✓ | |
| Zeolite/MOF Builder | ✓ | |
| Conformer Tools | ✓ | |
| ParAMS & Active Learning | ✓ | |
| Microkinetics | ✓ | |
| Zacros kMC Interface | ✓ | |
| OLED Tools: Deposition, Properties, Database | ✓ | |
| ChemTraYzer | ✓ | |
| Reactions Discovery | ✓ | |
| GFN-FF | ✓ | |
| GAFF | ✓ | |
| MLPotentials: ANI-2x, ANI-1ccx, AIMNet2 | ✓ | |
| MLPotentials: MACE, M3GNET, UMA, eSEN | ✓ | |
| ML Support: PiNN, SchNetPack, sGDML, Torch | ✓ | |
| APPLE&P (without parameters) | ✓ |
Workflows
Starting with the 2022 release of the Amsterdam Modeling Suite we include a set of workflow scripts for multiscale OLED modeling. These workflows are developed and validated in close collaboration with the Eindhoven University of Technology to bridge the gap between ab-initio atomistic modeling of OLED molecules with AMS, and device-level kinetic Monte Carlo simulations using Bumblebee. We attempt to provide a fully integrated multiscale simulation platform for the digital screening and prediction of successful OLED materials and devices.
When simulating a reactive system – where chemical bonds between atoms can change over time – it is not uncommon to observe a very large number of reactions. For large or complex simulations, the many reactive events make it challenging to derive meaningful information from the trajectory. ChemTraYzer2 (CT2) is a tool designed to help with this problem. CT2 reads each frame of a reactive MD trajectory, keeps track of all reactions that occur, and summarizes all of this information into several useful quantities:
- A list of all encountered species (reactants, products, intermediates)
- A list of unique reactions
- Reaction rate constants for all reactions
- Net fluxes of each species encountered in the simulation
- Other kinetic and population measures
Conformers is a flexible tool for conformer generation. It implements several conformer generation methods and can be combined with any AMS engine.
- Choose between the generation methods implemented in RDKit, Simulated Annealing or Grimme’s CREST.
- Generation of conformer sets from an initial geometry.
- Merging of conformer sets with a variety of equivalence comparison methods.
- Geometry optimizations of conformer sets with higher-level engines.
- Calculations of properties (e.g. spectra) for conformers from a set.
- also available in Python for custom workflows
The discovery of chemical reactions is a workflow consisting of three steps:
- Reactive molecular dynamics based on the NanoReactor or Lattice Deformation
- Network Extraction using ChemTraYzer2: Reactive MD Analysis and geometry optimizations
- Product Ranking
AMS Driver
The AMS driver can compute a variety of properties from the forces and energies provided by our various compute engines, e.g. ADF or ReaxFF. Interfacing your own compute engine or other external codes to the AMS driver, is straightforward using our interface to the popular atomic simulation environment (ASE) or the AMS external engine feature.
In chemistry and materials science, two types of Potential Energy Surface (PES) critical points are of particular interest: local minima and (first-order) saddle points and much time and effort can be spent trying to locate such points for a given system.
The PESexploration task in AMS bundles a set of tools that are capable of discovering minima and transition states automtically starting from any given starting structure on the PES.
The Amsterdam Modeling Suite offers powerfull Molecular Dynamics (MD) and Monte Carlo (MC) functionality. With plenty of advanced MD functionality and trajectory analysis tools, as well as excellent parallel scalability, AMS has been a popular choice for MD simulations. Thanks to the AMS driver concept, MD settings can easily be ported between compute engines, both internal and external, e.g. via the interface to the atomic simulation environment (ASE) engine.
Processes in thermodynamic equilibrium like battery discharge or adsorption isotherms can be studied with the Grand-Canonical Monte Carlo (GCMC) algorithm in AMS. The force-bias Monte Carlo algorithm has been used to atomic layer deposition, surface diffusion, growth and healing processes. Thanks to the AMS driver concept, MD settings can easily be ported between compute engines, both internal and external, e.g. via the interface to the atomic simulation environment (ASE) engine.
Interfaces
Parametrized models such as the reactive Force Field ReaxFF or the new Machine Learning Potentials can model large, realistic, systems with high efficency. However, the accuracy of such models varies. the The purpose of ParAMS is refitting the parameters of such models to increase the accuracy, or enable the study of new systems by creating new parameters in the first place. For this ParAMS helps you create and manage your training data, run high-dimensional parameter fits in smart and efficient way and evaluate the fitness of your new model.
Our graphical user interface (GUI) works out of the box with all the computational chemistry engines in the Amsterdam Modeling Suite. From generating complex models, choosing computational settings to running local or on remote clusters, our GUI supports you every step of the way. Jobresults are automatically fetched and easily visualized be it a Molecular Orbital diagram, a large reactive MD trajectory or the perfomance of a fitted Machine Learning Potential.
The GUI supports setting up calculations with VASP as an external engine to the AMS driver, hence “VASP via AMS”. This means that the AMS driver handles all changes to the system’s geometry during for example a geometry optimization, NEB calculation, or molecular dynamics simulation. The energy and forces at each step are calculated by a single point VASP calculation. Efficient use of restart files minimize the resulting overhead.