Copper selenides
are an important family of materials with applications
in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite
being a binary material system, the Cu–Se phase diagram is
complex and contains multiple crystal structures in addition to several
metastable structures that are not found on the thermodynamic phase
diagram. Consequently, the ability to synthetically navigate this
complex phase space poses a significant challenge. We demonstrate
that data-driven learning can successfully map this phase space in
a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses
via classification techniques to enable predictive phase determination.
A surrogate model was constructed with experimental data derived from
a design matrix of four experimental variables: C–Se bond strength
of the selenium precursor, time, temperature, and solvent composition.
The reactions in the surrogate model resulted in 11 distinct phase
combinations of copper selenide. These data were used to train a classification
model that predicts the phase with 95.7% accuracy. The resulting decision
tree enabled conclusions to be drawn about how the experimental variables
affect the phase and provided prescriptive synthetic conditions for
specific phase isolation. This guided the accelerated phase targeting
in a minimum number of experiments of klockmannite CuSe, which could
not be isolated in any of the reactions used to construct the surrogate
model. The reaction conditions that the model predicted to synthesize
klockmannite CuSe were experimentally validated, highlighting the
utility of this approach.