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arXiv 1803.01416cond-mat.mtrl-sci
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晶界原子动力学的机器学习确定

Machine learning determination of atomic dynamics at grain boundaries

Tristan A. Sharp, Spencer L. Thomas, Ekin D. Cubuk, Samuel S. Schoenholz, David J. Srolovitz, Andrea J. Liu

摘要:

在多晶材料中,晶界是增强的原子运动的位点,但是晶界网络内的原子结构的复杂性使得难以将结构和原子动力学联系起来。在这里,我们使用机器学习技术来建立这些材料的局部结构和动力学之间的联系。根据以前对大块玻璃质材料的研究,我们定义了一个纯粹的结构量,柔软度,它捕捉了原子重排的倾向。这种方法正确地识别结晶区,堆垛层错和孪晶界具有低的原子重排的可能性,同时在高能晶界内发现大的可变性。正如在玻璃[9,19,26]中所发现的那样,具有给定软度的原子重新排列的概率几乎是阿克里欧斯。这表明了一个定义明确的能量势垒,以及一个定义明确的前因子的阿克里乌斯形式的原子的给定的软度。低软原子的前因子的减少表明熵的变化对晶界中的原子动力学具有主导影响。

英文摘要:

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity, softness, that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements, while finding a large variability within high-energy grain boundaries. As has been found in glasses [9,19,26], the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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