Our flagship computational chemistry program Amsterdam Density Functional (ADF) is particularly strong in understanding and predicting structure, reactivity (catalysis), and spectra of molecules. Density Functional Theory (DFT) calculations are easily prepared and analyzed with our integrated graphical user interface.

More about this module

BAND, the accurate periodic density functional theory (DFT) code of the Amsterdam Modeling Suite shares many powerful features with our molecular DFT code ADF. Using atomic orbitals for periodic DFT calculations has many advantages over plane waves like a proper treatment of surfaces, efficient computations of sparse matter, and more direct and detailed analysis methods.

More about this module

For fast calculations on dense systems, we also ship and support the plane wave code Quantum ESPRESSO.

More about this module

Density-Functional based Tight-Binding (DFTB) allows to perform calculations of large systems over long timescales even on a desktop computer. Relatively accurate results are obtained at a fraction of the cost of density functional theory (DFT) by using pre-calculated parameters, a minimal basis, and including only nearest-neighbor interactions.

More about this module

MOPAC uses the nearest neighbor and minimal basis set approximations, making it fast and linear scaling. MOPAC has been parametrized against an enormous set of thoroughly examined experimental data, in a huge, commendable effort by Dr. Jimmy Stewart. The latest parametrization set (PM7) is also the most accurate. Since AMS2019, the MOPAC and DFTB modules are now bundled together and only one license is needed to use both methods.

More about this module

A fast, atomistic potential for studying reactions (bond breaking and forming) in complex chemical mixtures totaling hundreds of thousands of atoms.

More about this module

Machine learning potentials, based on, for example neural networks or Gaussian process regression, can provide very accurate descriptions of chemical systems at a very low computational cost. Machine learning potentials are fitted (trained, parameterized) to reproduce reference data, typically calculated using an ab initio or DFT method.

More about this module

The polarizable force field GFN-FF by Spicher and Grimme is an automated, polarizable Force Field for most of the periodic table. It combines speed with near quantum accuracy.

More about this module

APPLE&P is a polarizable force field, especially well suited for (many) electrolytes and polymers.

More about this module

Non polarizable force fields in AMS include the universal force field UFF, Amber95, Tripos 5.2 and GAFF.

More about this module

Zacros is a powerful Kinetic Monte Carlo software package for simulating molecular phenomena on catalytic surfaces. It allows for dynamic modelling of adsorption, desorption, surface diffusion, and reaction processes on heterogeneous catalysts.

More about this module

In the conversion from reagents to their final product(s), often many smaller intermediate steps are involved. These elementary reaction steps have each their individual energy barriers and rate constants, the combination of which yields the overall reaction system behavior. Via microkinetic modeling such a system can be investigated, yielding information on reaction rates and rate-limiting factors.

More about this module

The COnductor-like Screening MOdel for Realistic Solvents calculates thermodynamic properties of fluids and solutions based on quantum mechanical data. Properties from COSMO-RS have predictive power outside the parametrization set, as opposed to empirical models (e.g. UNIFAC).

The recent reparametrization of COSMO-SAC (segmented activity coefficients) model by Stan Sandler’s group improves partition coefficients (logP) for a set of solvent combinations. Furthermore, vapor-liquid equilibrium predictions are improved yielding a better agreement between calculated and experimental vapor pressures of mixtures.

More about this module