Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L1$_0$-TiAl and D0$_{19}$-Ti$_3$Al Alloys
Ji Qi, Z. H. Aitken, Qingxiang Pei, Anne Marie Z. Tan, Yunxing Zuo, M., H. Jhon, S. S. Quek, T. Wen, Zhaoxuan Wu, Shyue Ping Ong

TL;DR
This paper introduces a highly accurate machine learning interatomic potential for Ti-Al alloys, enabling detailed atomistic simulations of dislocation and fracture mechanisms in dual-phase TiAl alloys.
Contribution
The paper develops and benchmarks a novel moment tensor potential for Ti-Al alloys, achieving unprecedented accuracy in modeling defect properties and dislocation cores.
Findings
The MTP accurately predicts lattice parameters, elastic constants, and surface energies.
Dislocation core structures modeled by MTP align with DFT and experimental data.
The potential enables realistic simulations of deformation and fracture in TiAl alloys.
Abstract
Dual-phase -TiAl and -TiAl alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughness at room temperature. Experimental studies show unusual plastic behaviour associated with ordinary and superdislocations, making it necessary to gain a detailed understanding on their core properties in individual phases and at the two-phase interfaces. Unfortunately, extended superdislocation cores are widely dissociated beyond the length scales practical for routine first-principles density-functional theory (DFT) calculations, while extant interatomic potentials are not quantitatively accurate to reveal mechanistic origins of the unusual core-related behaviour in either phases. Here, we develop a highly-accurate moment tensor potential (MTP) for the binary Ti-Al alloy system using a DFT dataset…
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Taxonomy
TopicsHydrogen Storage and Materials · Thermodynamic and Structural Properties of Metals and Alloys · Chemical and Physical Properties of Materials
