Glass composition screening seeks to identify the optimal combination of components that satisfy specific performance criteria, thereby facilitating the development of new glass materials for a wide range of applications. However, the inherent complexity of multicomponent glass systems presents significant challenges in effectively correlating composition with property and achieving efficient screening processes. Current machine learning approaches for composition screening predominantly rely on supervised learning paradigms, which focus on precisely fitting sample labels. These methods not only require large amounts of high-quality data but are also susceptible to overfitting noisy samples and frequently occurring head samples, thereby limiting their generalization capabilities. In this study, we introduce a novel self-supervised learning framework specifically designed to screen glass compositions within predefined glass transition temperature (Tg) ranges. We reformulate the compositional screening task as a classification problem, aiming to predict whether the Tg of a given composition falls within the specified range. To enhance the training dataset and improve the model's resilience to noise, we propose an innovative data augmentation strategy grounded in asymptotic theory. Additionally, we present DeepGlassNet, a specialized network architecture engineered to capture and analyze the intricate interactions between different glass components. Our results demonstrate that DeepGlassNet achieves significantly higher screening accuracy compared to traditional methods and is readily adaptable to other composition screening tasks.

翻译：玻璃成分筛选旨在确定满足特定性能要求的最优组分组合，从而促进面向广泛应用的新型玻璃材料开发。然而，多组分玻璃体系固有的复杂性，在有效关联成分与性能以及实现高效筛选过程方面带来了显著挑战。当前用于成分筛选的机器学习方法主要依赖于监督学习范式，其侧重于精确拟合样本标签。这些方法不仅需要大量高质量数据，而且容易对噪声样本和频繁出现的头部样本过拟合，从而限制了其泛化能力。在本研究中，我们提出了一种新颖的自监督学习框架，专门设计用于在预定义的玻璃化转变温度（Tg）范围内筛选玻璃成分。我们将成分筛选任务重新构建为一个分类问题，旨在预测给定成分的Tg是否落在指定范围内。为了增强训练数据集并提高模型对噪声的鲁棒性，我们提出了一种基于渐近理论的创新数据增强策略。此外，我们提出了DeepGlassNet，这是一种专门设计的网络架构，用于捕捉和分析不同玻璃组分之间复杂的相互作用。我们的结果表明，与传统方法相比，DeepGlassNet实现了显著更高的筛选准确率，并且易于适应其他成分筛选任务。