State Key Laboratory of Advanced Special Steel, Shanghai Key Laboratory of Advanced Ferrometallurgy, School of Materials Science and Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China.
Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, China.
J Chem Phys. 2023 May 28;158(20). doi: 10.1063/5.0147720.
The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural network method for the TiAlNb ternary alloy based on a dataset built by first-principles calculations. The training set includes bulk elementary metals and intermetallic structures with slab and amorphous configurations. This potential is validated by comparing bulk properties-including lattice constant and elastic constants, surface energies, vacancy formation energies, and stacking fault energies-with their respective density functional theory values. Moreover, our potential could accurately predict the average formation energy and stacking fault energy of γ-TiAl doped with Nb. The tensile properties of γ-TiAl are simulated by our potential and verified by experiments. These results support the applicability of our potential under more practical conditions.
TiAl 体系的复杂相图和键合性质使得传统的原子力场难以准确描述其各种性质和相。在这里，我们基于第一性原理计算构建的数据集，使用深度神经网络方法为 TiAlNb 三元合金开发了一种机器学习原子间势。训练集包括体相基本金属和具有片层和非晶结构的金属间化合物。通过与各自的密度泛函理论值比较，验证了该势对体性质（包括晶格常数和弹性常数、表面能、空位形成能和堆垛层错能）的预测能力。此外，我们的势还可以准确预测 Nb 掺杂 γ-TiAl 的平均形成能和堆垛层错能。通过我们的势模拟了 γ-TiAl 的拉伸性能，并通过实验进行了验证。这些结果支持了我们的势在更实际条件下的适用性。
