  There currently exist no quantitative methods to determine the appropriate
conditions for solid-state synthesis. This not only hinders the experimental
realization of novel materials but also complicates the interpretation and
understanding of solid-state reaction mechanisms. Here, we demonstrate a
machine-learning approach that predicts synthesis conditions using large
solid-state synthesis datasets text-mined from scientific journal articles.
Using feature importance ranking analysis, we discovered that optimal heating
temperatures have strong correlations with the stability of precursor materials
quantified using melting points and formation energies ($\Delta G_f$, $\Delta
H_f$). In contrast, features derived from the thermodynamics of
synthesis-related reactions did not directly correlate to the chosen heating
temperatures. This correlation between optimal solid-state heating temperature
and precursor stability extends Tamman's rule from intermetallics to oxide
systems, suggesting the importance of reaction kinetics in determining
synthesis conditions. Heating times are shown to be strongly correlated with
the chosen experimental procedures and instrument setups, which may be
indicative of human bias in the dataset. Using these predictive features, we
constructed machine-learning models with good performance and general
applicability to predict the conditions required to synthesize diverse chemical
systems. Codes and data used in this work can be found at:
https://github.com/CederGroupHub/s4.
