(Invited) Bridging Data Gaps in Li/Na Metal-Oxide Chemistry through Computational and Automated Synthesis Workflows
TL;DR
This work combines large-scale computational exploration with accelerated experimental synthesis to uncover underexplored regions of Li/Na-containing metal oxide chemistries relevant to electrochemical energy storage and develops solid-state and wet-chemical synthesis platforms supported by automation, robotics, and AI-guided decision-making.
Abstract
Data-driven materials discovery accelerates the identification of functional compounds but can be hindered by gaps in existing databases and biases introduced when missing phases go unrecognized. To address these limitations, we combine large-scale computational exploration with accelerated experimental synthesis to uncover underexplored regions of Li/Na-containing metal oxide chemistries relevant to electrochemical energy storage. Our computational workflow integrates diverse structural prototypes, isovalent substitution strategies, and existing experimental knowledge to identify new ground-state and metastable compositions, revealing promising cation-rich systems with potential for enhanced Li/Na-ion based energy storage. Complementing this effort, we develop solid-state and wet-chemical synthesis platforms supported by automation, robotics, and AI-guided decision-making. These workflows streamline precursor selection, reaction condition optimization, and navigation of complex chemistries and pathways. The computational insights and accelerated synthesis methods provide a unified framework for expanding inorganic materials databases and enabling the rational discovery of next-generation battery materials.