Abstract The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To validate our model, we selected the ZrCoAl alloying system and investigated the microalloying effects on the glass forming possibility (GFP). According to the results, the microalloying process effects strongly depended on the inherent features of added element. Moreover, the CBNN model predicted a quaternary system, i.e. ZrCoAlNi, in which the high GFP area was extended through a wide range of chemical compositions. Finally, it is concluded that the established framework offers a roadmap for potential applications in the development of new quaternary MG alloys.

中文翻译：

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使用基于相关性的神经网络方法发现新型四元块状金属玻璃

摘要 成分加工参数的巨大空间导致了大量的试错实验工作，用于新型块状金属玻璃 (BMGs) 的工程设计。为了解决这个具有挑战性的问题，需要考虑能够限制生产合金成分的特定指南。在这项工作中，基于 7950 种合金成分的数据集开发了一种基于相关性的神经网络 (CBNN) 方法，通过预测铸造能力、降低的玻璃化转变 (Trg) 和临界厚度 (Dmax) 来设计潜在的新 MG。这种方法涉及影响因素的个体和相互特征，以提高预测精度。为了验证我们的模型，我们选择了 ZrCoAl 合金化系统并研究了微合金化对玻璃成形可能性 (GFP) 的影响。结果表明，微合金化过程的影响很大程度上取决于添加元素的固有特性。此外，CBNN 模型预测了一个四元系统，即 ZrCoAlNi，其中高 GFP 区域通过广泛的化学成分扩展。最后，得出的结论是，已建立的框架为开发新的四元 MG 合金的潜在应用提供了路线图。