The
Chevrel phase (CP) is a class of molybdenum chalcogenides that
exhibit compelling properties for next-generation battery materials,
electrocatalysts, and other energy applications. Despite their promise,
CPs are underexplored, with only ∼100 compounds synthesized
to date due to the challenge of identifying synthesizable phases.
We present an interpretable machine-learned descriptor (H
δ) that rapidly and accurately estimates decomposition
enthalpy (ΔH
d) to assess CP stability.
To develop H
δ, we first used density
functional theory to compute ΔH
d for 438 CP compositions. We then generated >560 000 descriptors
with the new machine learning method SIFT, which provides an easy-to-use
approach for developing accurate and interpretable chemical models.
From a set of >200 000 compositions, we identified 48 501
CPs that H
δ predicts are synthesizable
based on the criterion that ΔH
d <
65 meV/atom, which was obtained as a statistical boundary from 67
experimentally synthesized CPs. The set of candidate CPs includes
2307 CP tellurides, an underexplored CP subset with a predicted preference
for channel site occupation by cation intercalants that is rare among
CPs. We successfully synthesized five of five novel CP tellurides
attempted from this set and confirmed their preference for channel
site occupation. Our joint computational and experimental approach
for developing and validating screening tools that enable the rapid
identification of synthesizable materials within a sparse class is
likely transferable to other materials families to accelerate their
discovery.