Generative inverse design for microstructure control in precursors for Li- and Mn-rich layered-oxide cathodes

Microstructures often dictate materials' performance, yet they are rarely treated as an explicit design variable because microstructures are hard to quantify, predict, and optimize. We built an image-centric, closed-loop framework that makes microstructural morphology a controllable objective and demonstrate its use case with precursors for Li- and Mn-rich layered oxide cathodes. This work presents an integrated, AI-driven framework for the predictive design and optimization of lithium-ion battery cathode precursor synthesis. This framework integrates a diffusion-based image generation model, a quantitative image analysis pipeline, and a particle swarm optimization (PSO) algorithm. By extracting key morphological descriptors such as texture, sphericity, and median particle size (<i>D</i>₅₀) from SEM images, the platform accurately predicts SEM-like morphologies resulting from specific co-precipitation conditions, including reaction time-, solution concentration-, and pH-dependent structural changes. We experimentally validated that our optimization pinpoints synthesis parameters yielding user-defined target morphologies of Li- and Mn-rich (LMR) layered oxide materials, with predicted and synthesized structures showing close agreement. This framework offers a practical strategy for data driven materials design, enabling both forward prediction and inverse design of synthesis conditions and paving the way toward autonomous, image-guided microstructure engineering.