论文标题
使用机器学习预测玻璃过渡温度降低
Prediction of Reduced Glass Transition Temperature using Machine Learning
论文作者
论文摘要
计算材料科学的出现为数据驱动的方法铺平了建模和制造材料制造的道路。通过使用合金组成的变化，对玻璃形成能力（GFA）等性质的预测仍然是材料科学领域的一个具有挑战性的问题。这也引起了制造业的重大财务问题。尽管存在各种经验指南来预测GFA，但仍然非常需要全面的预测模型。这项工作着重于研究一些流行的机器学习算法，以预测材料组成的玻璃过渡温度（TRG）。从实验中，我们得出结论，集合模型在预测TRG方面的表现更好。通过帮助我们开发具有显着特性的材料，这一结果可以在材料科学的分支中发挥作用。
The advent of computational material sciences has paved the way for data-driven approaches for modeling and fabrication of materials. The prediction of properties like the glass-forming ability (GFA) by using the variation in alloy composition remains to be a challenging problem in the field of material sciences. It also results in significant financial concerns for the manufacturing industry. Despite the existence of various empirical guides for the prediction of GFA, a comprehensive prediction model is still highly desirable. This work focuses on studying some of the popular machine learning algorithms for the prediction of the reduced glass transition temperature (Trg) of material compositions. From the experimentation, we conclude that the ensemble model performs better for predicting Trg. This result can prove instrumental in the branch of material sciences by helping us to develop materials having remarkable properties.
