ParAMS
Optimize ReaxFF, DFTB & MLFF parameters
Easily build your training data and optimize ReaxFF, DFTB and Machine Learning Force Field parameters with the ParAMS module of the Amsterdam Modeling Suite.
Graphical and Python parametrization tools
Build and visualize training data
Optimize ReaxFF & DFTB parameters
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Easily build your training data and optimize ReaxFF, DFTB and Machine Learning Force Field parameters with the ParAMS module of the Amsterdam Modeling Suite.
ParAMS is part of the Advanced workflows and tools module of the Amsterdam Modeling Suite. To fit ReaxFF parameters, you also need a ReaxFF license. To fit DFTB parameters, you also need a DFTB license. For building training data with DFT, an ADF and BAND license will be useful.
SCM’s expert Matti Hellström demonstrates ParAMS and gives some useful tips on how to parametrize your own ReaxFF or DFTB parameters.