Insight into point defects and impurities in titanium from first principles

Abstract

Titanium alloys find extensive use in the aerospace and biomedical industries due to a unique combination of strength, density, and corrosion resistance. Decades of mostly experimental research has led to a large body of knowledge of the processing-microstructure-properties linkages. But much of the existing understanding of point defects that play a significant role in the mechanical properties of titanium is based on semi-empirical rules. In this work, we present the results of a detailed self-consistent first-principles study that was developed to determine formation energies of intrinsic point defects including vacancies, self-interstitials, and extrinsic point defects, such as, interstitial and substitutional impurities/dopants. We find that most elements, regardless of size, prefer substitutional positions, but highly electronegative elements, such as C, N, O, F, S, and Cl, some of which are common impurities in Ti, occupy interstitial positions.

Defect Engineering in Two Common Types of Dielectric Materials for Electromagnetic Absorption Applications

“…As can be seen from the NEXAFS spectra in Figure b, the presence of a pronounced B peak indicates structural changes with the increased sp 3 ‐like structure, while the decreased intensity of A peaks demonstrates a weakening of the CC π* network. The changes of microscopic chemical environments/electrical states indicate the influence of nitrogen doping on the dielectric polarization process in graphitized carbon …”

High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses

“…Moreover, the synergy among multidisciplinary sciences, along with rapid advancements in the electronic-structure methods, computational resources, and experimental techniques has made the process of materials design faster and far more efficient in some cases. As a result, the community is gradually migrating towards systematic computation-driven materials selection paradigms 9,10,[13][14][15][16][17][18][19][28][29][30][31][32][33][34][35] , where functional materials are screened by establishing a direct link between the macroscopic functionality and the atomic-scale nature of the material. We are in a data-rich, modeling-driven era where trial and error approaches are gradually being replaced by rational strategies 9,[36][37][38] , which couple predictions not only from specific electronic-structure calculations of a given property but also by learning from the existing data using machine learning.…”

A study of the interaction of oxygen with the α2 phase in the model alloy Ti–7wt%Al

“…% in the α 2 phase [21,22] and ~800 at.% ppm in the γ phase [23]. DFT simulations show that oxygen prefers to occupy octahedral interstitial sites that are surrounded by titanium atoms, known as [OTi6] sites [22][23][24][25][26]. Consideration of the number of [OTi6] sites present in each phase can explain the difference in solubility between the γ, α 2 and α phases.…”