Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers

TL;DR

This paper demonstrates that pre-trained vision transformers can effectively extract microstructure features for predicting material properties, reducing the need for task-specific model training in materials science.

Contribution

It introduces a novel approach using foundational vision transformers for task-agnostic microstructure feature extraction in property prediction.

Findings

Vision transformers effectively represent microstructures.

Accurate property predictions without task-specific training.

Applicable to both simulated and experimental data.

Abstract

Machine learning of microstructure--property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure--property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOv2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data; and (ii) Vicker's hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure--property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models.