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arXiv 2209.04010cond-mat.mtrl-sci
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局部化学环境对多主元素合金中空位扩散的影响

Effects of the local chemical environment on vacancy diffusion in multi-principal element alloys

Mauricio Ponga, Mohamed Hendy, Okan K. Orhan, Swarnava Ghosh

摘要:

多主元素合金(MPEAs)是一种具有极大组成空间和空间变化的化学环境的新型材料,与传统材料相比,具有优异的性能。然而,由于问题的大规模,从局部化学环境预测基本性质是具有挑战性的。为了研究这个基本问题,我们采用原子模拟(从头算和分子动力学)和卷积神经网络(CNN)的组合,以评估点缺陷和迁移能量在等摩尔CoFeCrNi MPEA。我们展示了如何使用一个小的局部化学环境的子集,可以合理的精度预测点缺陷的能量。使用CNN,我们开发了一个晶格蒙特卡罗模拟,计算迁移路径和扩散率的空缺。值得注意的是,我们的工作说明了如何当地的化学环境导致上升的点缺陷能量,这是负责空位的宏观扩散的分布函数。特别是,我们观察到,空位被困在超级盆地包围的大迁移和低迁移能态。因此,空位扩散率高度依赖于环境,并且对于给定的温度可以改变几个数量级。我们的工作说明了理解MPEAs中的性质的重要性,这取决于局部化学环境和CNN提供计算高维空间中能量的模型的能力,该模型可用于将事物扩展到高阶模型。

英文摘要:

Multi-principal element alloys (MPEAs) are exciting systems showing remarkable properties compared to conventional materials due to their exceedingly large compositional space and spatially varying chemical environment. However, predicting fundamental properties from the local chemical environment is challenging due to the large scale of the problem. To investigate this fundamental problem, we employ a combination of atomistic simulations (using ab-initio and molecular dynamics) and convolutional neural networks (CNNs) to evaluate point defect and migration energies in an equimolar CoFeCrNi MPEA. We show how energies of point defects can be predicted with reasonable accuracy using a small subset of local chemical environments. Using the CNNs, we develop a lattice Monte Carlo simulation that computes the migration path and diffusivities of vacancies. Remarkably, our work illustrates how the local chemical environment leads rise to a distribution function of the point defect energies, which is responsible for the macroscopic diffusivity of vacancies. In particular, we observed that vacancies get trapped in super basins surrounded by large migration and connected with low migration energy states. As a result, vacancy diffusivity is highly dependent on the environment and could change several orders of magnitude for a given temperature. Our works illustrate the importance of understanding properties in MPEAs depending on the local chemical environment and the ability of CNN to provide a model for computing energies in high-dimensional spaces, which can be used to scale things up to higher-order models.

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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