Neural Network Potentials for Molecule- Surface Interactions
Typ
Examensarbete för masterexamen
Program
Publicerad
2019
Författare
Österbacka, Nicklas
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A method of approximating the potential energy surface (PES) of molecules interacting
with crystalline surfaces was used to find an analytic representation of the
interactions between molecular hydrogen and a solid copper surface. The potential
energy of the H2/Cu(111) system was sampled using density functional theory with
the Perdew-Burke-Ernzerhof exchange-correlation functional. The machine learning
framework TensorFlow was used to generate neural network representations of the
energy surface from this data set.
The final representation accurately reproduces the features of the underlying PES
around potential entry channels for hydrogen dissociation, which agrees well with
other studies. The model also accurately predicts the zero-point energy of the H2
molecule.
In addition to examining the energy landscape around these entry channels the
dissociative sticking probability at different translational kinetic energies for the
system is estimated using classical trajectory methods. It is found that this produces
sticking probabilities that disagree with comparable experimental results; they are
in agreement with similar computational estimates, however, and reflect that the
sticking probability increases with the kinetic energy in qualitative agreement with
experiments.
Beskrivning
Ämne/nyckelord
density functional theory , artificial neural networks , potential energy surfaces , molecular dynamics