Abstract
Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding ...of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS
3
) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS
3
proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS
3
identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.
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.
