  Dynamics of flexible molecules are often determined by an interplay between
local chemical bond fluctuations and conformational changes driven by
long-range electrostatics and van der Waals interactions. This interplay
between interactions yields complex potential-energy surfaces (PES) with
multiple minima and transition paths between them. In this work, we assess the
performance of state-of-the-art Machine Learning (ML) models, namely sGDML,
SchNet, GAP/SOAP, and BPNN for reproducing such PES, while using limited
amounts of reference data. As a benchmark, we use the cis to trans thermal
relaxation in an azobenzene molecule, where at least three different transition
mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models
can globally achieve chemical accuracy of 1 kcal mol-1 with fewer than 1000
training points, predictions greatly depend on the ML method used as well as
the local region of the PES being sampled. Within a given ML method, large
differences can be found between predictions of close-to-equilibrium and
transition regions, as well as for different transition mechanisms. We identify
key challenges that the ML models face in learning long-range interactions and
the intrinsic limitations of commonly used atom-based descriptors. All in all,
our results suggest switching from learning the entire PES within a single
model to using multiple local models with optimized descriptors, training sets,
and architectures for different parts of complex PES.
