This paper presents a novel approach to glass composition screening through a self-supervised learning framework, addressing the challenges posed by glass transition temperature (Tg) prediction. Given the critical role of Tg in determining glass performance across various applications, we reformulate the composition screening task as a classification problem, allowing for direct prediction of whether specific compositional samples fall within a designated Tg range. Our model leverages advanced self-supervised learning techniques to optimize for the area under the curve (AUC) metric, mitigating the adverse effects of noise and class imbalances in training data. We introduce a data augmentation method based on the law of large numbers to enhance sample size and improve noise robustness. Additionally, our DeepGlassNet backbone encoder captures intricate second-order and higher-order interactions among components, providing insights into their collective impact on glass properties. We validate our approach using data from the SciGlass database, demonstrating its capability to accurately predict Tg for compositions within the specified range, while also exploring extrapolation to untested samples. This work not only enhances the accuracy of glass composition screening but also offers scalable solutions applicable to material screening across various fields, thereby advancing the development of novel materials.

翻译：本文提出了一种通过自监督学习框架进行玻璃成分筛选的新方法，以应对玻璃化转变温度（Tg）预测带来的挑战。鉴于Tg在决定玻璃于各类应用中的性能方面具有关键作用，我们将成分筛选任务重新表述为一个分类问题，从而能够直接预测特定成分样品是否落在指定的Tg范围内。我们的模型利用先进的自监督学习技术来优化曲线下面积（AUC）指标，以减轻训练数据中噪声和类别不平衡的不利影响。我们引入了一种基于大数定律的数据增强方法，以增加样本量并提高噪声鲁棒性。此外，我们的DeepGlassNet骨干编码器能够捕捉组分之间复杂的二阶及更高阶相互作用，从而揭示它们对玻璃性能的集体影响。我们使用SciGlass数据库中的数据验证了我们的方法，证明了其能够准确预测指定范围内成分的Tg，同时也探索了对未测试样本的外推能力。这项工作不仅提高了玻璃成分筛选的准确性，而且提供了可扩展的解决方案，适用于跨多个领域的材料筛选，从而推动了新型材料的开发。