A python module for building Chemical-tranferabel machine-learning potentials (CTPs). CTPs are models for atomistic simulations. The compound dependent parameters of 2b+3b potentials are described by some neural network. The models approximate the Born-Oppenheimer potential-energy suraface using density-functional-theory calculations as training data. The 2b+3b descriptor and potential module can be accesed also independently of the CTP approach. Publication is in preparation.
ahmetcik/Chemical-Transferable-Potentials
A python module for building Chemical-tranferabel machine-learning potentials (CTPs). CTPs are models for atomistic simulations. The compound dependent parameters of 2b+3b potentials are described by some neural network. The models approximate the Born-Oppenheimer potential-energy suraface using density-functional-theory calculations as training data. The 2b+3b descriptor and potential module can be accesed also independently of the CTP approach. Publication is in preparation.
PythonMIT