This work presents an
approach to aid the discovery of novel inorganic solids by highlighting regions
of underexplored, yet likely productive composition space using machine
learning. A support vector regression (SVR) algorithm was constructed first to determine
a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical
composition using data from 313,965 high-throughput density functional theory calculations.
The resulting predicted formation energies (r<sup>2</sup> = 0.94; MAE = 8.5 meV/atom)
were then used to construct zero-kelvin convex hull diagrams and identify compositions
immediately on the hull, as well as +50 meV above the convex hull to capture potential
compounds that are considered energetically unfavorable but that are still experimentally
accessible. Using this methodology, four ternary composition diagrams, Y−Ag−<i>Tr</i> (<i>Tr</i>
= B, Al, Ga, In), were explored owing to the diversity of chemistries as a
function of triel element to provide experimental validation for the
predictions. A particularly promising but unexplored region in the Y−Ag−In diagram
was identified, and the ensuing solid-state high-temperature synthesis produced
YAg<sub>0.65</sub>In<sub>1.35</sub>, which has not been reported. First-principle
calculations were finally used to determine the ordering of Ag and In as well
as confirm the crystal structure solution. Our combination of machine learning,
inorganic synthesis, and computational modeling describes a new avenue where data-centric
models and computation play a critical role in supporting the experimental
examination of unexplored phase diagrams.
Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis
