The SCM team is proud to present the Amsterdam Modeling Suite 2020. Read the release notes for more details.
A fast G0W0 and RPA implementation is available in ADF. Furthermore, ADF has been fully integrated into the AMS driver to enable advanced and more efficient potential energy surface exploration, easier switching between modules and seamless workflows. ReaxFF now also uses its AMS driver integration by default, making charge constraints easier, and also enabling 1D and 2D periodic ReaxFF calculations.
The new hybrid engine enables multi-layer calculations for any periodicity with multiple and diverse QM and MM layers. Electrostatic embedding is available for 2-layer QM/MM.
AMS driver can now be used with various machine learning potentials through the MLPotential module with various machine learning backends and pre-parametrized neural network potentials ANI-1ccx and ANI-2x.
The AMS driver also has many more analysis and geometry optimization improvements.
Species that can occur in multiple forms (conformers, solvent association, dissociation) in solution can now be treated with COSMO-RS for more accurate thermodynamic predictions.