通过非参数机器学习揭示成分对核废料固定玻璃耐久性的影响

IF 6.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yu Song, Xiaonan Lu, Kaixin Wang, Joseph V. Ryan, Morten M. Smedskjaer, John D. Vienna, Mathieu Bauchy
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引用次数: 0

摘要

确保玻璃的长期化学耐久性对于核废料固定化操作至关重要。耐久性玻璃通常会根据其对标准化测试(如产品一致性测试或蒸汽水化测试 (VHT))的响应情况进行处置资格鉴定。VHT 使用升高的温度和水蒸气来加速玻璃的变化和次生相的形成。了解玻璃成分与 VHT 反应之间的关系具有重要的现实意义。然而,这种关系是复杂的、非线性的,有时还相当多变,这给确定单个氧化物对 VHT 响应的不同影响带来了挑战。在此,我们利用了由 654 个汉福德低活性废物 (LAW) 玻璃组成的数据集,并采用各种机器学习技术来探索这种关系。我们发现高斯过程回归 (GPR) 这种非参数回归方法的预测准确率最高。通过利用训练有素的模型,我们发现了每种氧化物对玻璃 VHT 响应的影响。此外,我们还讨论了在稀疏和异构数据集的背景下,推断材料性能时在拟合不足和拟合过度之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unveiling the effect of composition on nuclear waste immobilization glasses’ durability by nonparametric machine learning

Unveiling the effect of composition on nuclear waste immobilization glasses’ durability by nonparametric machine learning
Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leverage a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a nonparametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses’ VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets.
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来源期刊
npj Materials Degradation
npj Materials Degradation MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
7.80
自引率
7.80%
发文量
86
审稿时长
6 weeks
期刊介绍: npj Materials Degradation considers basic and applied research that explores all aspects of the degradation of metallic and non-metallic materials. The journal broadly defines ‘materials degradation’ as a reduction in the ability of a material to perform its task in-service as a result of environmental exposure. The journal covers a broad range of topics including but not limited to: -Degradation of metals, glasses, minerals, polymers, ceramics, cements and composites in natural and engineered environments, as a result of various stimuli -Computational and experimental studies of degradation mechanisms and kinetics -Characterization of degradation by traditional and emerging techniques -New approaches and technologies for enhancing resistance to degradation -Inspection and monitoring techniques for materials in-service, such as sensing technologies
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