Scholarlink

<p>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 <i>interpretable</i> 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<sub>2</sub>(ZnCo)O<sub>4</sub>, synthesized via a high-throughput co-precipitation method. We combined a kernel classification model with an application of <i>global</i> SHapley Additive exPlanations (SHAP) analysis to pinpoint the experimental features most critical to single phase synthesizability by interpreting the contributions of each feature. <i>Global</i> 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 <i>interpretable</i> machine learning in refining synthesis protocols but also establish a framework for data-informed experimental design in inorganic synthesis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Kernel learning assisted synthesis condition exploration for ternary spinel

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

查看全文
  • Yutong Liu,
  • Mehrad Ansari,
  • Robert Black,
  • Jason Hattrick-Simpers

摘要

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 Fe2(ZnCo)O4, synthesized via a high-throughput co-precipitation method. We combined a kernel classification model with an application of global SHapley Additive exPlanations (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.