Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity

Abstract

One of the intriguing features exhibited by the layered MAX phase compounds, is the nonlinear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigation remained a challenge due to the associated length and time scales of the phenomena. In the present work, we adopt a data driven approach to develop a machine learned interatomic potential for the MAX compound Ti _2 AlC following the moment tensor potential protocol. The constructed potential is validated in lattice constant, formation energy, elastic constant, and stacking fault energies. Finally, applying machine learned potential in classical molecular dynamics provides a faithful representation of the experimentally observed nonlinear elasticity for Ti _2 AlC. The generated atomic configurations confirm the proposal of formation of ripplocations which allow atomic layers to glide relative to each other without breaking the in-plane bonds. We find common defects, like Al vacancy, strongly influence the hysteresis properties of the stress–strain curve, paving the route to defect-engineered nonlinear elasticity.

Keywords

• machine learning
• classical molecular dynamics
• elasticity