  FLAME is a software package to perform a wide range of atomistic simulations
for exploring the potential energy surfaces (PES) of complex condensed matter
systems. The range of methods include molecular dynamics simulations to sample
free energy landscapes, saddle point searches to identify transition states,
and gradient relaxations to find dynamically stable geometries. In addition to
such common tasks, FLAME implements a structure prediction algorithm based on
the minima hopping method (MHM) to identify the ground state structure of any
system given solely the chemical composition, and a framework to train a neural
network potential to reproduce the PES from $\textit{ab initio}$ calculations.
The combination of neural network potentials with the MHM in FLAME allows a
highly efficient and reliable identification of the ground state as well as
metastable structures of molecules and crystals, as well as of nano structures,
including surfaces, interfaces, and two-dimensional materials. In this
manuscript, we provide detailed descriptions of the methods implemented in the
FLAME code and its capabilities, together with several illustrative examples.
