用预训练视觉模型提取材料显微结构特征，高效预测性能
Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers
- 用CLIP、DINOv2等预训练模型提取显微结构通用特征
- 在模拟与实验数据上准确预测弹性模量和硬度
- 无需定制模型训练，适合快速开发材料性能预测
基于数据的机器学习在计算材料科学中正成为新兴方法。现有工作多针对每种显微结构-性能关系设计专用模型。本文提出利用预训练的基础视觉变压器（如CLIP、DINOv2、SAM）提取任务无关的显微结构特征，并结合轻量级机器学习模型预测显微结构相关的性能。我们在两个案例中验证该方法：(i) 基于模拟数据预测双相显微结构的弹性模量；(ii) 基于文献中的实验数据预测镍基和钴基高温合金的维氏硬度。结果表明，基础视觉变压器能提供鲁棒的显微结构表征，可在无需昂贵的任务特定训练或定制深度学习模型微调的情况下，高效实现显微结构-性能关系的机器学习。
原文摘要 · Abstract (English)
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.
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