  Lead-free halide double perovskites are promising stable and non-toxic
alternatives to methylammonium lead iodide in the field of photovoltaics. In
this context, the most commonly used double perovskite is Cs$_2$AgBiBr$_6$, due
to its favorable charge transport properties. However, the maximum power
conversion efficiency obtained for this material does not exceed 3\%, as a
consequence of its wide indirect gap and its intrinsic and extrinsic defects.
On the other hand, the materials space that arises from the substitution of
different elements in the 4 lattice sites of this structure is large and still
mostly unexplored. In this work a neural network is used to predict the band
gap of double perovskites from an initial space of 7056 structures and select
candidates suitable for visible light absorption. Successive hybrid DFT
calculations are used to evaluate the thermodynamic stability, the power
conversion efficiency and the effective masses of the selected compounds, and
to propose novel potential solar absorbers.
