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

2025-05-12

AI Summary

Microstructural features of lithium-manganese-rich layered oxide cathode precursors are difficult to quantify and optimize as explicit design variables. Method: This study establishes an image-driven closed-loop microstructure inverse design framework, integrating Stable Diffusion-based SEM image generation, quantitative microstructural analysis (texture, sphericity, D50), and particle swarm optimization (PSO) to enable end-to-end mapping from target morphology to co-precipitation process parameters (pH, reagent concentration, reaction time). Contribution/Results: Experimental validation confirms a D50 prediction error <5%; the multi-objective morphology-guided synthesis conditions demonstrate high reproducibility and accuracy. This framework significantly enhances the capability for on-demand microstructural design of cathode precursors, marking the first incorporation of diffusion models into a closed-loop materials design workflow.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimizing microstructure as a design variable for materials performance
• Predicting SEM morphologies from coprecipitation conditions using AI
• Inverse design of synthesis conditions for target microstructures

Innovation

• AI-driven diffusion model for microstructure prediction
• Particle swarm optimization for synthesis parameters
• Image-guided closed-loop framework for materials design