Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

Transition-metal chromophores with earth-abundant transition
metals
are an important design target for their applications in lighting
and nontoxic bioimaging, but their design is challenged by the scarcity
of complexes that simultaneously have well-defined ground states and
optimal target absorption energies in the visible region. Machine
learning (ML) accelerated discovery could overcome such challenges
by enabling the 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 “Jacob’s ladder”.
To accelerate the discovery of complexes with absorption energies
in the visible region while minimizing the effect of low-lying excited
states, we use two-dimensional (2D)­efficient global optimization
to sample candidate low-spin chromophores from multimillion complex
spaces. Despite the scarcity (i.e., ∼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 1000-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.