[论文解读] Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Jiadong Chen, Samuel R. Cross+4 more|arXiv (Cornell University)|Apr 3, 2023

Machine Learning in Materials Science被引用 12

一句话总结

这篇论文提出了一种热力学前驱体选择策略，以在高维相图中导航，实现机器人合成多组分氧化物，获得更高的相纯度。

ABSTRACT

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental science, from which we can develop new insights into materials synthesis for both robot and human chemists.

研究动机与目标

• 推动复杂多组分氧化物的高效合成。
• 开发一种热力学方法来选择避免低能副产物的前驱体。
• 在机器人无机材料合成实验室中展示该方法。
• 验证所选前驱体能加速相转变动力学。

提出的方法

• 使用热力学框架识别避免竞争性低能副产物的前驱体。
• 使反应能最大化以驱动快速相转变动力学。

实验结果

研究问题

• RQ1 热力学前驱体选择能否降低多组分氧化物合成中不希望的副产物的形成？
• RQ2 提出的热力学策略所选前驱体在机器人合成中是否能实现更高的相纯度？
• RQ3 前驱体选择如何影响高维相图中的反应动力学和结果？

主要发现

• 预测的前驱体在很大程度上比传统前驱体更易获得目标材料的高相纯度。
• 在代表插层电池正极和固态电解质的35种目标四元氧化物上进行了验证。
• 机器人实验覆盖224个反应，涵盖27种元素，使用28种独特前驱体。