The rapid advancement of machine learning has unlocked numerous opportunities for materials science, particularly in accelerating the design and analysis of materials. However, a significant challenge lies in the scarcity and high cost of obtaining high-quality materials datasets. In other fields, such as natural language processing, foundation models pre-trained on large datasets have achieved exceptional success in transfer learning, effectively leveraging latent features to achieve high performance on tasks with limited data. Despite this progress, the concept of foundation models remains underexplored in materials science. Here, we present a foundation model specifically designed for composite materials. Our model is pre-trained on a dataset of short-fiber composites to learn robust latent features. During transfer learning, the MMAE accurately predicts homogenized stiffness, with an R2 score reaching as high as 0.959 and consistently exceeding 0.91, even when trained on limited data. These findings validate the feasibility and effectiveness of foundation models in composite materials. We anticipate extending this approach to more complex three-dimensional composite materials, polycrystalline materials, and beyond. Moreover, this framework enables high-accuracy predictions even when experimental data are scarce, paving the way for more efficient and cost-effective materials design and analysis.

翻译：机器学习的快速发展为材料科学带来了众多机遇，特别是在加速材料设计与分析方面。然而，一个重大挑战在于获取高质量材料数据集的稀缺性和高昂成本。在其他领域，如自然语言处理，基于大规模数据集预训练的基础模型在迁移学习中取得了显著成功，能够有效利用潜在特征在数据有限的任务上实现高性能。尽管取得了这些进展，基础模型的概念在材料科学中仍未得到充分探索。本文提出了一种专门针对复合材料设计的基础模型。该模型在短纤维复合材料数据集上进行预训练，以学习稳健的潜在特征。在迁移学习过程中，该模型能够准确预测均质化刚度，其R2分数最高可达0.959，即使在有限数据上训练时也始终超过0.91。这些结果验证了基础模型在复合材料领域的可行性和有效性。我们预计将这一方法扩展到更复杂的三维复合材料、多晶材料及其他体系。此外，该框架即使在实验数据稀缺的情况下也能实现高精度预测，为更高效、更具成本效益的材料设计与分析开辟了道路。