From Sparse AFM Observations to Probabilistic Macroscale Mechanics: A Generative-Physics Framework for Plant Cell Walls

2026-10-05

AI Summary

This study addresses the challenge that sparse atomic force microscopy (AFM) observations fail to capture full structural variability and enable cross-scale mechanical mapping of plant cell walls. To overcome this, a generative physics-based framework is proposed. Methodologically, Stable Diffusion integrated with LoRA fine-tuning is pioneered to generate AFM microstructures, which are subsequently coupled with strain-controlled finite element homogenization and stochastic simulations to establish a probabilistic linkage from sparse nanoscale data to macroscopic continuum mechanics. The framework successfully predicts effective elastic properties and macroscopic stress response distributions, reveals the regulatory mechanisms by which stiffness ratios govern multimodal variability, and establishes a comprehensive cross-scale computational workflow.

Abstract

Nanoscale imaging of developing plant cell walls is expensive, and a limited number of atomic force microscopy (AFM) scans cannot capture the full structural variability of the wall. We present a generative-physics workflow that links sparse AFM observations of cotton (Gossypium hirsutum) fiber cell walls at 8 days post-anthesis (DPA) to distributions of effective elastic properties and a larger-scale mechanical response. We adapt a Stable Diffusion model with Low-Rank Adaptation (LoRA) to expand the experimental scans into an ensemble of AFM-like microstructures and assess the synthetic structures using microfibril crossover count and crossover angle. Each microstructure is mapped to spatially varying Young's modulus and Poisson's ratio fields and analyzed using strain-controlled finite element homogenization. Repeating this process across the image ensemble and a prescribed sweep of matrix-to-fibril stiffness ratios produces distributions of effective Young's modulus and Poisson's ratio. A single constitutive pair cannot capture this variation. The resulting distributions depend strongly on the matrix-to-fibril stiffness ratio and, in several cases, exhibit apparent multimodality. We further propagate the paired effective properties into 20 stochastic realizations of a tensile simulation of a larger specimen, yielding a distribution of macroscale stress response. The framework provides a probabilistic link between sparse nanoscale observations and continuum-scale mechanics while retaining variability across scales. The present results establish the computational workflow, while calibration of the intensity-to-property mapping against nanomechanical measurements remains necessary for quantitative prediction at the fiber scale.

Problem

• plant cell walls
• atomic force microscopy
• multiscale mechanics
• structural variability
• nanoscale imaging

Innovation

• Generative-physics framework
• Stable Diffusion with LoRA
• Finite element homogenization
• Multiscale mechanics
• Probabilistic modeling