Machine learning formation enthalpies of intermetallics
Machine learning formation enthalpies of intermetallics
复制标题
DOI：
10.1063/5.0012323
复制
发表时间：
2020-09-14
影响因子：
3.2
通讯作者：
Mishra, Rohan
中科院分区：
文献类型：
--
作者：
Zhang, Zhaohan;Li, Mu;Mishra, Rohan
开发快速、准确的方法来发现金属间化合物与合金设计相关。虽然基于密度泛函理论 (DFT) 的方法通过提供稳定金属间化合物的能量和性能的快速获取加速了二元和三元合金的设计，但它们不适合快速筛选多主元素合金 (MPEA) 的巨大组合空间。在这里，提出了一种机器学习模型，用于预测二元金属间化合物的形成焓，并用于识别新的金属间化合物。该模型使用易于获取的元素属性作为描述符，在预测材料项目数据库中报告的稳定二元金属间化合物的形成焓时，平均绝对误差为 0.025eV/原子。该模型进一步预测在材料项目数据库中没有报告任何稳定金属间化合物的 112 个二元合金系统中会形成稳定的金属间化合物。 DFT 计算证实模型识别出的一种稳定金属间化合物 NbV2 位于凸包上。此外，自适应迁移学习方法用于推广该模型，以与 DFT 类似的精度来预测三元金属间化合物，这表明该模型可以扩展到识别 MPEA 中可能形成的成分复杂的金属间化合物。
Developing fast and accurate methods to discover intermetallic compounds is relevant for alloy design. While density-functional-theory (DFT)-based methods have accelerated design of binary and ternary alloys by providing rapid access to the energy and properties of the stable intermetallics, they are not amenable for rapidly screening the vast combinatorial space of multi-principal element alloys (MPEAs). Here, a machine-learning model is presented for predicting the formation enthalpy of binary intermetallics and is used to identify new ones. The model uses easily accessible elemental properties as descriptors and has a mean absolute error of 0.025eV/atom in predicting the formation enthalpy of stable binary intermetallics reported in the Materials Project database. The model further predicts stable intermetallics to form in 112 binary alloy systems that do not have any stable intermetallics reported in the Materials Project database. DFT calculations confirm one such stable intermetallic identified by the model, NbV2, to be on the convex hull. Furthermore, an adaptive transfer learning method is used to generalize the model to predict ternary intermetallics with a similar accuracy as DFT, which suggests that it could be extended to identify compositionally complex intermetallics that may form in MPEAs.