New MedeA Application Note
Because of its stability, corrosion resistance, and high strength-to-weight ratio titanium is of great technological importance for many applications, e.g., in the automotive and aeronautics industries, as well as in biomaterials and structural alloys. It is therefore essential to be able to understand and predict the properties of this key element under service conditions which may range from ambient to elevated temperatures and pressures.
This application note describes the generation of a machine-learned potential (MLP) using the highly automated MedeA Machine-Learned Potential Generator (MLPG). Our MLPG inputs a training set consisting of results from a set of MedeA VASP calculations selected by the user and generates a machine-learned potential for use with MedeA LAMMPS. This MLP can be tailored by the MedeA user to calculate structural, mechanical, energetic, or thermodynamic properties of interest for the selected material system, which may be an element, a set of related compounds, or an alloy system. In this example application, we use an MLP to investigate the transition of titanium at about 1150 K from the hexagonal closed-packed structure, the so-called α-phase, to the body-centered cubic β-phase. These calculations predict values in good agreement with measurements for the α-β transition and melting temperatures, as well as the temperature-dependent specific heat and the latent heats of the α-β and melting transitions.
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