Learning grain boundary segregation behavior through fingerprinting complex atomic environments

IF 7.5 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Jacob P. Tavenner, Ankit Gupta, Gregory B. Thompson, Edward M. Kober, Garritt J. Tucker
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Abstract

Although continuum-scale segregation is a well-documented behavior in multi-species materials, detailed site-specific behavior remains largely unexplored. This is partially due to the complexity of analyzing materials at the requisite time and length scales for describing segregation with full atomic accuracy. Here, we better evaluate the segregation behavior of disordered grain boundary (GB) atomic environments through leveraging a set of Strain Functional Descriptors (SFDs) to generate an atomic descriptor (i.e., fingerprint). Using this atomic fingerprint, we resolve key relationships between atomic structure and segregation energy. Machine learning (ML) techniques are utilized in concert with this SFD fingerprint to elucidate complex relationships relating segregation potential to changes in specific features of the local Gaussian density captured by the SFDs. Finally, we identify relationships that indicate both individual and joint structure-property correlations. Linking atomic segregation energy to key structural features demonstrates the value of higher-order descriptors for uncovering complex structure-property relationships at an atomic scale. Describing site-specific segregation in multi-species materials is a computationally complex task that typically requires model simplification, at the expense of atomic accuracy, or limitation to small samples. Here, the relationships between local atomic environments at grain boundaries and their segregation energies are investigated by developing suitable machine learning atomic descriptors.

Abstract Image

Abstract Image

通过复杂原子环境指纹识别学习晶界偏析行为
尽管连续尺度的偏析是多物种材料中一种有据可查的行为,但详细的特定位点行为在很大程度上仍未得到探索。这部分是由于在必要的时间和长度尺度上分析材料的复杂性造成的,而这些尺度是以完全原子精度来描述偏析的。在这里,我们通过利用一组应变功能描述符(SFD)来生成原子描述符(即指纹),从而更好地评估了无序晶界(GB)原子环境的偏析行为。利用该原子指纹,我们解决了原子结构与偏析能之间的关键关系。我们将机器学习(ML)技术与 SFD 指纹结合起来使用,以阐明偏析电位与 SFD 捕获的局部高斯密度特定特征变化之间的复杂关系。最后,我们确定了表明个体和联合结构-性质相关性的关系。将原子偏析能与关键结构特征联系起来,证明了高阶描述符在揭示原子尺度上复杂的结构-性质关系方面的价值。
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来源期刊
Communications Materials
Communications Materials MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
12.10
自引率
1.30%
发文量
85
审稿时长
17 weeks
期刊介绍: Communications Materials, a selective open access journal within Nature Portfolio, is dedicated to publishing top-tier research, reviews, and commentary across all facets of materials science. The journal showcases significant advancements in specialized research areas, encompassing both fundamental and applied studies. Serving as an open access option for materials sciences, Communications Materials applies less stringent criteria for impact and significance compared to Nature-branded journals, including Nature Communications.
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