Sun Y T, Bai H Y, Li M Z, Wang W H
Institute of Physics, Chinese Academy of Sciences , Beijing 100190, People's Republic of China.
University of Chinese Academy of Science , Beijing 100049, People's Republic of China.
J Phys Chem Lett. 2017 Jul 20;8(14):3434-3439. doi: 10.1021/acs.jpclett.7b01046. Epub 2017 Jul 12.
The prediction of the glass-forming ability (GFA) by varying the composition of alloys is a challenging problem in glass physics, as well as a problem for industry, with enormous financial ramifications. Although different empirical guides for the prediction of GFA were established over decades, a comprehensive model or approach that is able to deal with as many variables as possible simultaneously for efficiently predicting good glass formers is still highly desirable. Here, by applying the support vector classification method, we develop models for predicting the GFA of binary metallic alloys from random compositions. The effect of different input descriptors on GFA were evaluated, and the best prediction model was selected, which shows that the information related to liquidus temperatures plays a key role in the GFA of alloys. On the basis of this model, good glass formers can be predicted with high efficiency. The prediction efficiency can be further enhanced by improving larger database and refined input descriptor selection. Our findings suggest that machine learning is very powerful and efficient and has great potential for discovering new metallic glasses with good GFA.
通过改变合金成分来预测玻璃形成能力（GFA）是玻璃物理学中的一个具有挑战性的问题，同时也是一个对工业界来说具有巨大经济影响的问题。尽管几十年来已经建立了不同的预测GFA的经验指南，但仍然非常需要一种能够同时处理尽可能多的变量以有效预测良好玻璃形成体的综合模型或方法。在这里，通过应用支持向量分类方法，我们开发了从随机成分预测二元金属合金GFA的模型。评估了不同输入描述符对GFA的影响，并选择了最佳预测模型，结果表明与液相线温度相关的信息在合金的GFA中起关键作用。基于该模型，可以高效地预测良好的玻璃形成体。通过改进更大的数据库和优化输入描述符选择，可以进一步提高预测效率。我们的研究结果表明，机器学习非常强大且高效，在发现具有良好GFA的新型金属玻璃方面具有巨大潜力。
