Glass composition screening is essential for advancing new glass materials, yet the inherent complexity of multicomponent systems presents significant challenges. Current supervised learning methods for this task rely heavily on large amounts of high-quality data and are prone to overfitting on noisy samples, which limits their generalization ability. In this work, we propose a novel self-supervised learning framework designed specifically for screening glass compositions within pre-defined glass transition temperature (Tg) ranges. We reformulate the screening task as a classification problem, aiming to predict whether the glass transition temperature of a given composition falls within a target interval. To improve the model's robustness to noise, we introduce an innovative data augmentation strategy grounded in asymptotic theory. Additionally, we present DeepGlassNet, a dedicated network architecture developed to capture and analyze the complex interactions among constituent elements in glass compositions. Experimental results demonstrate that DeepGlassNet achieves superior screening accuracy compared to traditional methods and exhibits strong adaptability to other composition-related screening tasks. This study not only provides an efficient methodology for designing multicomponent glasses but also establishes a foundation for applying self-supervised learning in material discovery. Code and data are available at: https://github.com/liubin06/DeepGlassNet

翻译：玻璃成分筛选对于开发新型玻璃材料至关重要，然而多组分体系固有的复杂性带来了显著挑战。当前用于该任务的监督学习方法严重依赖大量高质量数据，且易在噪声样本上过拟合，这限制了其泛化能力。本文提出一种专门为在预定玻璃化转变温度（Tg）范围内筛选玻璃成分而设计的新型自监督学习框架。我们将筛选任务重新定义为分类问题，旨在预测给定成分的玻璃化转变温度是否落在目标区间内。为增强模型对噪声的鲁棒性，我们引入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——一种专门开发的网络架构，用于捕捉和分析玻璃成分中组成元素间的复杂相互作用。实验结果表明，与传统方法相比，DeepGlassNet实现了更优的筛选精度，并对其他成分相关筛选任务展现出强大的适应性。本研究不仅为设计多组分玻璃提供了高效方法，还为自监督学习在材料发现中的应用奠定了基础。代码与数据可在以下网址获取：https://github.com/liubin06/DeepGlassNet