Speaker
Michela Pauletti
(UZH)
Description
In the past, potential energy functions for atomistic simulations have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. For years people kept fitting potentials to develop new force field, cheap and reasonably accurate methods to carry out computer simulations.
Recent advances in machine learning offer new approaches for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations.
This talk wants to give an overview on this topic and some examples of applications as well.
