Tan Tianlun, Han Xiangjie, Liu Yuzhen, Wang Hao, Lu Xiaogang, Chen Ying
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, People's Republic of China.
Department of Nanomechanics, School of Engineering, Tohoku University, 6-6-01 Aramakiaoba, Aoba-ku, Sendai 980-8579, Japan.
J Phys Condens Matter. 2025 Apr 28;37(20). doi: 10.1088/1361-648X/adcdb2.
A deep learning-driven Ti-Al-Nb ternary interatomic potential is developed continuously through DP-GEN framework, combining first-principles accuracy with molecular dynamics scalability. The neural network potential demonstrates exceptional transferability in predicting critical properties of Nb-doped-TiAl and-TiAl phases. Nb influence on shear deformation in-TiAl is investigated. Meanwhile, Nb-doped/interface tensile perpendicular to the interface and shear simulations along 1/2[11¯0] and 1/2[112¯] are performed in order to simulate the local configurations in Ti-Al PST single crystals. This model provides a computational framework for interfacial engineering in lamellar TiAl alloys.
通过DP-GEN框架不断开发一种由深度学习驱动的Ti-Al-Nb三元原子间势，将第一性原理精度与分子动力学可扩展性相结合。该神经网络势在预测Nb掺杂TiAl和-TiAl相的关键性质方面表现出卓越的迁移性。研究了Nb对-TiAl中剪切变形的影响。同时，进行了垂直于界面的Nb掺杂/界面拉伸以及沿1/2[11¯0]和1/2[112¯]的剪切模拟，以模拟Ti-Al PST单晶中的局部构型。该模型为层状TiAl合金的界面工程提供了一个计算框架。
