Machine Learning-Guided Exploration of Glass-Forming Ability in Multicomponent Alloys
Machine Learning-Guided Exploration of Glass-Forming Ability in Multicomponent Alloys
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DOI：
10.1007/s11837-022-05549-w
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发表时间：
2022-10
期刊：
影响因子：
2.6
通讯作者：
Yi Yao;Timothy Sullivan;Feng Yan;Jiaqi Gong;Lin Li
中科院分区：
文献类型：
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作者：
Yi Yao;Timothy Sullivan;Feng Yan;Jiaqi Gong;Lin Li
合金系统中非晶形成能力的预测是材料科学和冶金应用中的一个具有挑战性的问题。在这项研究中，我们建立了人工神经网络(ANN)模型来研究多元合金的GFA，基于磁控溅射制备的三元合金和五元合金的数据集。通过用不同的数据集组合训练神经网络模型，解决了数据源对模型性能的影响，特别是模型在不可见多元合金体系中预测GFA的泛化能力问题。在组合数据集上训练的神经网络模型对几种CoCrFeNi基多元合金表现出最好的性能，特别是在遗漏一种合金系统验证中的均方根误差较低，以及较高的模型稳健性。为了进一步验证人工神经网络模型，我们采用磁控共溅射方法制备了CoCrFeNi-Mo金属薄膜，并用X射线衍射仪和电子显微镜对薄膜的结构和相结构进行了表征。实验结果与ANN模型的预测吻合较好，表明数据驱动的机器学习方法在未来的多元非晶态合金设计中是一种有用的工具。
The prediction of glass-forming ability (GFA) in alloy systems is a challenging problem in material science as well as for metallurgical applications. In this study, we build artificial neural network (ANN) models to investigate the GFA of multicomponent alloys, based on the datasets assembled from ternary alloys as well as quinary alloys prepared by magnetron sputtering. Through training the ANN models with different combinations of datasets, we tackle the problem of the influence of the data source on the model performance, especially the generalizability of the models in predicting the GFA in unseen multicomponent alloy systems. The ANN model trained on a combined dataset exhibits the best performance, specifically low root mean square error in leave-one-alloy-system-out validation and high model robustness, for several CoCrFeNi-based multicomponent alloys. To further verify the ANN models, we synthesize CoCrFeNi-Mo metallic thin films by magnetron co-sputtering and characterize the structure and phase information via x-ray diffraction and electron microscopy. The outcomes of our experiments agree reasonably well with the ANN model predictions, indicating that the data-driven machine learning approach can be a useful tool in the future design of multicomponent amorphous alloys.