InfinityEBSD : Metrics-Guided Infinite-Size EBSD Map Generation With Diffusion Models
Sterley Labady, Youssef Mesri, Daniel Pino Munoz, Baptiste Flipon, Marc Bernacki

TL;DR
InfinityEBSD introduces a diffusion-based approach to generate large, realistic EBSD microstructure maps conditioned on physical metrics, enabling scalable, statistically representative microstructure synthesis for materials analysis.
Contribution
The paper presents a novel diffusion model conditioned on microstructural metrics to generate large-scale, realistic EBSD maps, supporting both extension of existing maps and creation of new ones from descriptors.
Findings
Generated maps match statistical descriptors of target metrics.
The method maintains morphological diversity and spatial coherence.
Generated maps are compatible with standard analysis software.
Abstract
Materials performance is deeply linked to their microstructures, which govern key properties such as strength, durability, and fatigue resistance. EBSD is a major technique for characterizing these microstructures, but acquiring large and statistically representative EBSD maps remains slow, costly, and often limited to small regions. In this work, we introduce InfinityEBSD, a diffusion-based method for generating monophase realistic EBSD maps of arbitrary size, conditioned on physically meaningful microstructural metrics. This approach supports two primary use cases: extending small experimental EBSD maps to arbitrary sizes, and generating entirely new maps directly from statistical descriptors, without any input map. Conditioning is achieved through eight microstructural descriptors, including grain size, grain perimeter, grain inertia ratio, coordination number and disorientation…
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Taxonomy
TopicsMachine Learning in Materials Science · Block Copolymer Self-Assembly · Model Reduction and Neural Networks
