Machine learning potentials can provide very accurate descriptions of chemical systems at the computational cost of a force field. In combination with the AMS driver, machine learning potentials allow you to run accurate Molecular Dynamics simulations or to explore potential energy surfaces for e.g. frequency calculations or transition state searches.
AMS2020 features the pre-parameterized models ANI-1ccx and ANI-2x for direct usage but also allows you to develop and employ your own machine learning potentials using one of the available backend engines: PiNN, SchNetPack, sGDML, and TorchANI.
Machine Learning Potentials in AMS2020
SCM’s expert Matti Hellström demonstrates the new machine learning potential engine in the Amsterdam Modeling Suite.