  Assessing the synthesizability of inorganic materials is a grand challenge
for accelerating their discovery using computations. Synthesis of a material is
a complex process that depends not only on its thermodynamic stability with
respect to others, but also on factors from kinetics, to advances in synthesis
techniques, to the availability of precursors. This complexity makes the
development of a general theory or first-principles approach to
synthesizability currently impractical. Here we show how an alternative pathway
to predicting synthesizability emerges from the dynamics of the materials
stability network: a scale-free network constructed by combining the convex
free-energy surface of inorganic materials computed by high-throughput density
functional theory and their experimental discovery timelines extracted from
citations. The time-evolution of the underlying network properties allows us to
use machine-learning to predict the likelihood that hypothetical,
computer-generated materials will be amenable to successful experimental
synthesis.
