  In single-junction photovoltaic (PV) devices, the maximum achievable power
conversion efficiency (PCE) is mainly limited by thermalization and
transmission losses, because polychromatic solar irradiation cannot be matched
to a single bandgap. Several concepts are being investigated to reduce these
losses, such as the classical vertical multijunction cells, 'lateral' tandem
cells, and multi-exciton generation in the form of photon up- and
down-conversion. While in theory, efficiencies exceeding 90% are possible
(Landsberg or thermodynamic limit), there are severe practical limitations in
terms of processability, cost, and spectral sensitivity. Here, we present a
simulation environment based on Bayesian Optimization that is able to predict
and optimize the electrical performance of multi-junction architectures, both
vertical and lateral, in combination with multi-exciton materials. With respect
to vertical stacks, we show that by optimizing bandgap energies of
multi-exciton generation (MEG) layers, double junctions can reach efficiencies
beyond those of five-junction tandem devices (57%). Moreover, such combinations
of MEG and double junction devices would be highly resilient against spectral
changes of the incoming sunlight. We point out three main challenges for PV
material science to realize such devices. With respect to lateral
architectures, we show that MEG layers might allow reducing nonradiative
voltage losses following the Energy Gap Law. Finally, we show that the
simulation environment is able to use machine learned quantitative
structure-property relationships obtained from high-throughput experiments to
virtually optimise the active layer (such as, the film thickness and the
donor-acceptor ratio) for a given architecture. The simulation environment thus
represents an important building block towards a digital twin of PV materials.
