  Optimization of atomic structures presents a challenging problem, due to
their highly rough and non-convex energy landscape, with wide applications in
the fields of drug design, materials discovery, and mechanics. Here, we present
a graph reinforcement learning approach, StriderNET, that learns a policy to
displace the atoms towards low energy configurations. We evaluate the
performance of StriderNET on three complex atomic systems, namely, binary
Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon.
We show that StriderNET outperforms all classical optimization algorithms and
enables the discovery of a lower energy minimum. In addition, StriderNET
exhibits a higher rate of reaching minima with energies, as confirmed by the
average over multiple realizations. Finally, we show that StriderNET exhibits
inductivity to unseen system sizes that are an order of magnitude different
from the training system.
