  Transition metal chromophores with earth-abundant transition metals are an
important design target for their applications in lighting and non-toxic
bioimaging, but their design is challenged by the scarcity of complexes that
simultaneously have optimal target absorption energies in the visible region as
well as well-defined ground states. Machine learning (ML) accelerated discovery
could overcome such challenges by enabling screening of a larger space, but is
limited by the fidelity of the data used in ML model training, which is
typically from a single approximate density functional. To address this
limitation, we search for consensus in predictions among 23 density functional
approximations across multiple rungs of Jacobs ladder. To accelerate the
discovery of complexes with absorption energies in the visible region while
minimizing MR character, we use 2D efficient global optimization to sample
candidate low-spin chromophores from multi-million complex spaces. Despite the
scarcity (i.e., approx. 0.01\%) of potential chromophores in this large
chemical space, we identify candidates with high likelihood (i.e., > 10\%) of
computational validation as the ML models improve during active learning,
representing a 1,000-fold acceleration in discovery. Absorption spectra of
promising chromophores from time-dependent density functional theory verify
that 2/3 of candidates have the desired excited state properties. The
observation that constituent ligands from our leads have demonstrated
interesting optical properties in the literature exemplifies the effectiveness
of our construction of a realistic design space and active learning approach.
