标题： 基于基础视觉变压器的微观结构表示来学习材料的微观结构-性能关系的机器学习 显示英文标题

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

摘要： 从数据中学习微观结构-性能关系的机器学习是计算材料科学中的一个新兴方法。 大多数现有的机器学习工作都集中在为每个微观结构-性能关系开发任务特定的模型。 我们提出利用预训练的基础视觉变压器来提取任务无关的微观结构特征，并随后进行轻量级的机器学习以预测依赖于微观结构的性能。 我们在两个案例研究中展示了我们的方法，使用了预训练的最先进视觉变压器（CLIP、DINOv2、SAM）：（i）基于模拟数据的双相微观结构的弹性模量；（ii）基于文献中发表的实验数据的镍基和钴基高温合金的维氏硬度。 我们的结果表明，基础视觉变压器在无需昂贵的任务特定训练或定制深度学习模型的微调的情况下，具有对鲁棒的微观结构表示和高效的微观结构-性能关系机器学习的潜力。 显示英文摘要

摘要： 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.