Accelerated discovery of Magnesium-based amorphous alloys through a property-driven active learning strategy

Weibin Ma, Bingyao Liu, Tian Lu, Wencong Lu, Chang Ren, Leikai Xing, Minjie Li, Kang Sun, Aimin Zhang
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Abstract

Magnesium (Mg)-based amorphous alloys hold significant potential for applications in the automotive, aerospace, and biomedical industries. However, they are limited by their smaller size compared to other amorphous alloys. A higher reduced glass transition temperature () is associated with larger sizes in Mg-based amorphous alloys. Yet, due to the vast chemical space involved, designing Mg-based amorphous alloys with higher using traditional ‘trial and error’ method is a challenging endeavor. In this work, we developed a property-driven active learning strategy to customize Mg-based amorphous alloys with enhanced . After just two iterations, we successfully tailored four amorphous alloys with high values. Under identical experimental conditions, two of these alloys exhibited values surpassing that of MgAgCuGd, the alloy with best value in the reported references. SHAP analysis revealed that tends to be higher when the Ag atomic ratio exceeds 0.045, the Cu atomic ratio is below 0.18, the Ni atomic ratio is below 0.025, and the Mg atomic ratio is below 0.665. Our work offers a reliable strategy for designing Mg-based amorphous alloys with higher and provides valuable insights for the rational design of these alloys.
通过属性驱动的主动学习策略加速发现镁基非晶合金
镁(Mg)基无定形合金在汽车、航空航天和生物医学等行业的应用潜力巨大。然而,与其他非晶合金相比,它们的尺寸较小,因而受到限制。在镁基非晶合金中,较高的玻璃化转变温度()与较大的尺寸有关。然而,由于涉及巨大的化学空间,使用传统的 "反复试验 "方法设计出更大尺寸的镁基非晶合金是一项具有挑战性的工作。在这项工作中,我们开发了一种属性驱动的主动学习策略,以定制具有更高......性能的镁基非晶合金。仅经过两次迭代,我们就成功定制了四种具有高值的非晶合金。在相同的实验条件下,其中两种合金的数值超过了 MgAgCuGd,而 MgAgCuGd 是已报道的参考文献中数值最高的合金。SHAP分析表明,当Ag原子比超过0.045、Cu原子比低于0.18、Ni原子比低于0.025、Mg原子比低于0.665时,SHAP值往往较高。我们的研究为设计更高的镁基非晶合金提供了可靠的策略,并为这些合金的合理设计提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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