74 research outputs found
Representing molecule-surface interactions with symmetry-adapted neural networks
The accurate description of molecule-surface interactions requires a detailed
knowledge of the underlying potential-energy surface (PES). Recently, neural
networks (NNs) have been shown to be an efficient technique to accurately
interpolate the PES information provided for a set of molecular configurations,
e.g. by first-principles calculations. Here, we further develop this approach
by building the NN on a new type of symmetry functions, which allows to take
the symmetry of the surface exactly into account. The accuracy and efficiency
of such symmetry-adapted NNs is illustrated by the application to a
six-dimensional PES describing the interaction of oxygen molecules with the
Al(111) surface.Comment: 13 pages including 8 figures; related publications can be found at
http://www.fhi-berlin.mpg.de/th/th.htm
Tutorial: How to Train a Neural Network Potential
The introduction of modern Machine Learning Potentials (MLP) has led to a
paradigm change in the development of potential energy surfaces for atomistic
simulations. By providing efficient access to energies and forces, they allow
to perform large-scale simulations of extended systems, which are not directly
accessible by demanding first-principles methods. In these simulations, MLPs
can reach the accuracy of electronic structure calculations provided that they
have been properly trained and validated using a suitable set of reference
data. Due to their highly flexible functional form the construction of MLPs has
to be done with great care. In this tutorial, we describe the necessary key
steps for training reliable MLPs, from data generation via training to final
validation. The procedure, which is illustrated for the example of a
high-dimensional neural network potential, is general and applicable to many
types of MLPs
Fingerprints for spin-selection rules in the interaction dynamics of O2 at Al(111)
We performed mixed quantum-classical molecular dynamics simulations based on
first-principles potential-energy surfaces to demonstrate that the scattering
of a beam of singlet O2 molecules at Al(111) will enable an unambiguous
assessment of the role of spin-selection rules for the adsorption dynamics. At
thermal energies we predict a sticking probability that is substantially less
than unity, with the repelled molecules exhibiting characteristic kinetic,
vibrational and rotational signatures arising from the non-adiabatic spin
transition.Comment: 4 pages including 3 figures; related publications can be found at
http://www.fhi-berlin.mpg.de/th/th.htm
Signatures of nonadiabatic O2 dissociation at Al(111): First-principles fewest-switches study
Recently, spin selection rules have been invoked to explain the discrepancy
between measured and calculated adsorption probabilities of molecular oxygen
reacting with Al(111). In this work, we inspect the impact of nonadiabatic spin
transitions on the dynamics of this system from first principles. For this
purpose the motion on two distinct potential-energy surfaces associated to
different spin configurations and possible transitions between them are
inspected by means of the Fewest Switches algorithm. Within this framework we
especially focus on the influence of such spin transitions on observables
accessible to molecular beam experiments. On this basis we suggest experimental
setups that can validate the occurrence of such transitions and discuss their
feasibility.Comment: 13 pages, 7 figure
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