Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential

Ferenc Tasnádi, Florian Bock, Johan Tidholm, Alexander V. Shapeev, Igor A. Abrikosov

Research output: Contribution to journalArticlepeer-review

Abstract

High-temperature thermal stability, elastic moduli and anisotropy are among the key properties, which are used in selecting materials for cutting and machining applications. The high computational demand of ab initio molecular dynamics (AIMD) simulations in calculating elastic constants of alloys promotes the development of alternative approaches. Machine learning concept grasped as hybride classical molecular dynamics and static first principles calculations have several orders less computational costs. Here we prove the applicability of the concept considering the recently developed moment tensor potentials (MTP), where moment tensors are used as material's descriptors which can be trained to predict the elastic constants of the prototypical hard coating alloy, Ti0.5Al0.5N at 900 K. We demonstrate excellent agreement between classical molecular dynamics simulations with MTPs and AIMD simulations. Moreover, we show that using MTPs one overcomes the inaccuracy issues present in approximate AIMD simulations of elastic constants of alloys.

Original languageEnglish
Article number138927
JournalThin Solid Films
Volume737
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • Alloys
  • Elastic tensor
  • Finite temperature
  • Interatomic potential
  • Machine learning

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