  To aid in the automation of inorganic materials synthesis, we introduce an
algorithm (ARROWS3) that guides the selection of precursors used in solid-state
reactions. Given a target phase, ARROWS3 iteratively proposes experiments and
learns from their outcomes to identify an optimal set of precursors that leads
to maximal yield of that target. Initial experiments are selected based on
thermochemical data collected from first principles calculations, which enable
the identification of precursors exhibiting large thermodynamic force to form
the desired target. Should the initial experiments fail, their associated
reaction paths are determined by sampling a range of synthesis temperatures and
identifying their products. ARROWS3 then uses this information to pinpoint
which intermediate reactions consume most of the available free energy
associated with the starting materials. In subsequent experimental iterations,
precursors are selected to avoid such unfavorable reactions and therefore
maintain a strong driving force to form the target. We validate this approach
on three experimental datasets containing results from more than 200 distinct
synthesis procedures. When compared to several black-box optimization
algorithms, ARROWS3 identifies the most effective set of precursors for each
target while requiring substantially fewer experimental iterations. These
findings highlight the importance of using domain knowledge in the design of
optimization algorithms for materials synthesis, which are critical for the
development of fully autonomous research platforms.
