[Paper Review] 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 Science12 citations

TL;DR

The paper presents a thermodynamic precursor-selection strategy to navigate high-dimensional phase diagrams, enabling robotic synthesis of multicomponent oxides with higher phase purity.

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.

Motivation & Objective

• Motivate efficient synthesis of complex multicomponent oxides.
• Develop a thermodynamic approach to select precursors that avoid low-energy byproducts.
• Demonstrate the approach in a robotic inorganic materials synthesis laboratory.
• Validate that chosen precursors can accelerate phase transformation kinetics.

Proposed method

• Use a thermodynamic framework to identify precursors that circumvent competing low-energy byproducts.
• Maximize reaction energy to drive fast phase transformation kinetics.

Experimental results

Research questions

• RQ1 Can thermodynamic precursor selection reduce formation of undesired byproducts in multicomponent oxide synthesis?
• RQ2 Do precursors chosen by the proposed thermodynamic strategy yield higher phase purity in robotic synthesis?
• RQ3 How does precursor choice influence reaction kinetics and outcome in high-dimensional phase diagrams?

Key findings

• Predicted precursors frequently produced target materials with higher phase purity than traditional precursors.
• Validation conducted on 35 target quaternary oxides representative of intercalation battery cathodes and solid-state electrolytes.
• Robotic experiments covered 224 reactions spanning 27 elements with 28 unique precursors.