Multi-phase dataset for bulk Ti and the Ti-6Al-4V alloy

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, MLIPs are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the α, β, ω and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by DFT using the PBE functional. We have also utilised a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species for Ti-6Al-4V. These configurations may be used to fit models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models using the GAP and ACE frameworks.

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Publication Details

Journal
Scientific Data
Published
2025-12-05
DOI
https://doi.org/10.1038/s41597-025-05302-3
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Multi-phase dataset for bulk Ti and the Ti-6Al-4V alloy

Colin Allen, Albert P. Bartók
1 citations
Scientific Data
Machine Learning in Materials Science
0.52
article

Multi-phase dataset for bulk Ti and the Ti-6Al-4V alloy

Colin Allen, Albert P. Bartók
article en
1 citations

Abstract

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, MLIPs are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the α, β, ω and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by DFT using the PBE functional. We have also utilised a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species for Ti-6Al-4V. These configurations may be used to fit models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models using the GAP and ACE frameworks.

Scientific DataVol. 12(1)
University of Warwick (GB)
UK Research and Innovation, University of Warwick, Atomic Weapons Establishment, European Commission, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 30%
Machine Learning in Materials Science
0.52
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