Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential

Alexander V. Shapeev, Evgeny V. Podryabinkin, Konstantin Gubaev, Ferenc Tasnádi, Igor A. Abrikosov

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A combination of quantum mechanics calculations with machine learning techniques can lead to a paradigm shift in our ability to predict materials properties from first principles. Here we show that on-the-fly training of an interatomic potential described through moment tensors provides the same accuracy as state-of-the-art ab initio molecular dynamics in predicting high-temperature elastic properties of materials with two orders of magnitude less computational effort. Using the technique, we investigate high-temperature bcc phase of titanium and predict very weak, Elinvar, temperature dependence of its elastic moduli, similar to the behavior of the so-called GUM Ti-based alloys (Sato et al 2003 Science 300 464). Given the fact that GUM alloys have complex chemical compositions and operate at room temperature, Elinvar properties of elemental bcc-Ti observed in the wide temperature interval 1100-1700 K is unique.

Original languageEnglish
Article number113005
JournalNew Journal of Physics
Volume22
Issue number11
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Ab initio molecular dynamics
  • Bcc titanium
  • Elinvar effect
  • Machine learning

Fingerprint

Dive into the research topics of 'Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential'. Together they form a unique fingerprint.

Cite this