Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
This work addresses the lack of established synthesis routes for solid-state materials in inorganic chemistry, providing a framework for data-informed experimental design, though it is incremental as it applies existing methods to a specific domain.
The study tackled the challenge of identifying synthesis conditions for single-phase Fe2(ZnCo)O4 spinel by using an interpretable machine learning model, which revealed that precursor and precipitating agent features align with crystal growth theories to guide synthesis protocols.
Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes for solid-state materials remains a significant challenge in inorganic chemistry. An interpretable machine learning model is therefore essential, as it provides insights into the key factors governing phase formation. Here, we focus on the formation of single-phase Fe$_2$(ZnCo)O$_4$, synthesized via a high-throughput co-precipitation method. We combined a kernel classification model with a novel application of global SHAP analysis to pinpoint the experimental features most critical to single phase synthesizability by interpreting the contributions of each feature. Global SHAP analysis reveals that precursor and precipitating agent contributions to single-phase spinel formation align closely with established crystal growth theories. These results not only underscore the importance of interpretable machine learning in refining synthesis protocols but also establish a framework for data-informed experimental design in inorganic synthesis.