Image-Guided Microstructure Optimization using Diffusion Models: Validated with Li-Mn-rich Cathode Precursors

TL;DR

This paper presents an AI-driven, image-based framework that predicts and optimizes microstructure morphologies of cathode precursors in lithium-ion batteries, enabling targeted synthesis through a closed-loop design process.

Contribution

It introduces an integrated diffusion model, image analysis, and optimization pipeline for microstructure design, advancing autonomous, image-guided materials engineering.

Findings

Accurately predicts SEM-like morphologies from synthesis parameters.

Successfully optimizes synthesis conditions for desired microstructures.

Close agreement between predicted and experimentally synthesized structures.

Abstract

Microstructure often dictates materials performance, yet it is rarely treated as an explicit design variable because microstructure is hard to quantify, predict, and optimize. Here, we introduce an image centric, closed-loop framework that makes microstructural morphology into a controllable objective and demonstrate its use case with Li- and Mn-rich layered oxide cathode precursors. 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 coprecipitation conditions, including reaction time-, solution concentration-, and pH-dependent structural changes. Optimization then pinpoints synthesis parameters that yield user defined target morphologies, as experimentally validated by the close agreement between predicted and synthesized structures. 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.