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

Abstract

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 (D₅₀) 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.