Hexagonal close-packed (hcp) metals rely on twinning to accommodate plastic deformation at room temperature. These twins often impede dislocation mobility and can lead to brittle failure. Significant research has been done to identify methods to mitigate this brittle failure; however, a gap exists in the work on the mechanisms behind the brittle failure. All hcp twin modes require atomic shuffling to place sheared atoms in the correct lattice sites. While shuffling for the $\left\{10\overline{1}2\right\}$ twin is well defined, most of the shuffle processes for twinning in hcp are not defined, and the displacement field due to the introduction of a twin has not been detailed. This work serves to present new mechanical equations to compute atomic shuffles and introduce an algorithm to calculate the displacement map and insert an arbitrarily shaped twin into a defect-free lattice using those shuffle vectors. We substantiate the algorithm by placing $\left\{10\overline{1}2\right\}$, $\left\{10\overline{1}1\right\}$, and $\left\{2\overline{1}\overline{1}2\right\}$ twins in Mg and Ti and show that the twins undergo glissile transformation using molecular dynamics.

The microstructure of the Ti-Al binary system is an area of great interest, as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Modified Embedded Atom Method (MEAM) perform adequately for modeling a dilute Al solute within Ti’s $\alpha$ phase, they struggle with accurately predicting plasticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the $\alpha$-to-$\beta$ phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not accurately capture the $\alpha$-to-$\beta$ or $\alpha$-to-D0₁₉ phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 30% Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, the RANN potential accurately predicts the $\alpha$-to-$\beta$ and $\alpha$-to-D0₁₉ phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L1₀ and D0₁₉ phases.