论文标题
卷积神经网络可实现多主体元素合金中路径依赖性扩散屏障光谱的高保真预测
Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys
论文作者
论文摘要
新兴的多质元素合金（MPEA）提供了一个巨大的组成空间，以寻找新的材料以进行技术进步。但是，如何从如此庞大的设计空间中有效地识别目标特性的最佳组成是材料科学的巨大挑战。在这里，我们开发了一个卷积神经网络（CNN）模型，该模型可以准确，有效地预测路径依赖性的空位迁移能屏障，这对于任何组合物和给定合金系统中的不同化学短距离对MPEA的扩散行为和许多高温性能至关重要。 CNN模型的成功使得为不同的MPEA系统开发一个扩散屏障数据库，这将加速合金筛选，以发现具有理想特性的新组合物。此外，还发现了与迁移能源障碍有关的局部配置的长度规模，并讨论了这种成功对材料科学其他方面的影响。
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.
