用可解释模型找出合成单相尖晶石的关键实验条件。
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
- 结合核分类与全局SHAP分析，识别关键实验特征。
- 发现前驱体和沉淀剂作用符合晶体生长理论。
- 为无机合成提供数据驱动的设计框架，适合材料研发者。
机器学习与高通量实验已显著加速混合金属氧化物催化剂的发现，但固态材料缺乏成熟合成路径仍是无机化学中的重大挑战。因此，可解释的机器学习模型至关重要，可揭示相形成的关键因素。本文聚焦于通过高通量共沉淀法合成的单相Fe₂(ZnCo)O₄尖晶石。我们结合核分类模型与新型全局SHAP分析，解析各实验特征对单相可合成性的贡献，识别出最关键因素。全局SHAP分析显示，前驱体与沉淀剂对单相尖晶石形成的贡献，与现有晶体生长理论高度一致。结果不仅强调了可解释机器学习在优化合成工艺中的价值，还建立了一个数据驱动的无机合成实验设计框架。
原文摘要 · Abstract (English)
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.
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