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arXiv 2203.06503cond-mat.mtrl-scicond-mat.dis-nnphysics.app-ph
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卷积神经网络实现多主元素合金中路径相关扩散势垒谱的高保真预测

Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys

Zhao Fan, Bin Xing, Penghui Cao

摘要:

新兴的多主元素合金(MPEAs)提供了一个广阔的成分空间,以寻找新的材料的技术进步。然而,如何从如此大的设计空间中有效地确定目标性能的最佳组合物是材料科学中的一个巨大挑战。在这里,我们开发了一个卷积神经网络(CNN)模型,可以准确,有效地预测路径依赖的空位迁移能垒,这是至关重要的扩散行为和许多高温性能,MPEAs在任何组成和不同的化学短程秩序在给定的合金系统。CNN模型的成功使其有希望为不同的MPEA系统,这将加速合金筛选发现新的组合物与所需的性能,开发一个数据库的扩散障碍。此外,还揭示了与迁移能垒相关的局域组态的长度尺度,并讨论了这一成功对材料科学其他方面的意义。

英文摘要:

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. However, how to efficiently identify optimal compositions from such a large design space for targeted properties is a grand challenge in material science. Here we developed a convolutional neural network (CNN) model that can accurately and efficiently predict path-dependent vacancy migration energy barriers, which are critical to diffusion behaviors and many high-temperature properties, of MPEAs at any compositions and with different chemical short-range orders within a given alloy system. The success of the CNN model makes it promising for developing a database of diffusion barriers for different MPEA systems, which would accelerate alloy screening for the discovery of new compositions with desirable properties. Besides, the length scale of local configurations relevant to migration energy barriers is uncovered, and the implications of this success to other aspects of materials science are discussed.

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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