O-Ti

Abstract: The segregation energy of oxygen interstitial solutes to grain boundaries (GBs) in hexagonal close-packed (HCP) titanium is investigated through atomistic simulations based on a machine-learning interatomic potential (MLIP). The Ti-O MLIP is developed for titanium with interstitial oxygen solutes up to a concentration of 20 at.%. It is based on the formalism of the atomic cluster expansion (ACE), trained on an extensive dataset of density functional theory calculations exploring over 200,000 atomic environments for Ti and O interstitials. The ACE MLIP is used to compute oxygen GB segregation energies in 4685 different symmetric tilt GBs with [0001], [1-100] and [1-210] tilt axes. The segregation energies span a range of -0.2 eV to 2.0 eV, and over 90 % of the 4685 GBs display one or more sites with negative (attractive) segregation energies. The lowest-energy sites are found to be those with coordination numbers and Voronoi indices most similar to that of the equilibrium octahedral site in the bulk HCP Ti structure. We further explore the efficacy of crystal-graph convolutional neural network ML models for predicting segregation energies based solely on information about the local atomic environment.

Abstract: An interatomic potential model that can simultaneously describe metallic, covalent, and ionic bonding is suggested by combining the second nearest-neighbor modified embedded-atom method (2NNMEAM) and the charge equilibration (Qeq) method, as a further improvement of a series of existing models. Paying special attention to the removal of known problems found in the original Qeq model, a mathematical form for the atomic energy is newly developed, and carefully selected computational techniques are adapted for energy minimization, summation of Coulomb interaction, and charge representation. The model is applied to the Ti-O and Si-O binary systems selected as representative oxide systems for a metallic element and a covalent element. The reliability of the present 2NNMEAM+Qeq potential is evaluated by calculating the fundamental physical properties of a wide range of titanium and silicon oxides and comparing them with experimental data, density functional theory calculations, and other calculations based on (semi-)empirical potential models.

Abstract: Modeling oxygen interstitials in titanium requires a new empirical potential. We optimize potential parameters using a fitting database of first-principle oxygen interstitial energies and forces. A new database optimization algorithm based on Bayesian sampling is applied to obtain an optimal potential for a specific testing set of density functional data. A parallel genetic algorithm minimizes the sum of logistic function evaluations of the testing set predictions. We test the transferability of the potential model against oxygen interstitials in HCP titanium, transition barriers between oxygen interstitial sites, and oxygen in the titanium prismatic stacking fault. The potential predicts that the interaction between oxygen and a screw dislocation core is weak and short-ranged.