  Computational chemistry has become an important tool to predict and
understand molecular properties and reactions. Even though recent years have
seen a significant growth in new algorithms and computational methods that
speed up quantum chemical calculations, the bottleneck for trajectory-based
methods to study photoinduced processes is still the huge number of electronic
structure calculations. In this work, we present an innovative solution, in
which the amount of electronic structure calculations is drastically reduced,
by employing machine learning algorithms and methods borrowed from the realm of
artificial intelligence. However, applying these algorithms effectively
requires finding optimal hyperparameters, which remains a challenge itself.
Here we present an automated user-friendly framework, HOAX, to perform the
hyperparameter optimization for neural networks, which bypasses the need for a
lengthy manual process. The neural network generated potential energy surfaces
(PESs) reduces the computational costs compared to the ab initio-based PESs. We
perform a comparative investigation on the performance of different
hyperparameter optimiziation algorithms, namely grid search, simulated
annealing, genetic algorithm, and bayesian optimizer in finding the optimal
hyperparameters necessary for constructing the well-performing neural network
in order to fit the PESs of small organic molecules. Our results show that this
automated toolkit not only facilitate a straightforward way to perform the
hyperparameter optimization but also the resulting neural networks-based
generated PESs are in reasonable agreement with the ab initio-based PESs.
