To design materials for extreme applications, it is important to understand and predict phase transitions and their influence on material properties under high pressures and temperatures. Atomistic modeling can be a useful tool to assess these behaviors. However, this can be difficult due to the lack of fidelity of the interatomic potentials in reproducing this high pressure and temperature extreme behavior. Here, a hybrid EAM-R—which is the combination of embedded atom method (EAM) and rapid artificial neural network potential—for Tin (Sn) is described which is capable of accurately modeling the complex sequence of phase transitions between different metallic polymorphs as a function of pressure. This hybrid approach ensures that a basic empirical potential like EAM is used as a lower energy bound. By using the final activation function, the neural network contribution to energy must be positive, assuring stability over the whole configuration space. This implementation has the capacity to reproduce density functional theory results at 6 orders of magnitude slower than a pair potential for molecular dynamics simulation, including elastic and plastic characteristics and relative energies of each phase. Using calculations of the Gibbs free energy, it is demonstrated that the potential precisely predicts the experimentally observed phase changes at temperatures and pressures across the whole phase diagram. At 10.2 GPa, the present potential predicts a first-order phase transition between body-centered tetragonal (BCT) $\beta$-Sn and another polymorph of BCT-Sn. This structure transforms into body-centered cubic near the experimentally reported value at 33 GPa. Thus, the Sn potential developed in this paper can be used to study complex deformation mechanisms under extreme conditions of high pressure and strain rates unlike existing potentials. Moreover, the framework developed in this paper can be extended for different material systems with complex phase diagrams.

Twin–twin interactions are an important component of the microstructural evolution of hexagonal close-packed metals undergoing plasticity. These interactions are prevalent because of the predominance of twinning due to limited easy slip modes. Despite their importance, the complexities of the atomic-scale behavior of interacting twins has limited robust characterization. Using interfacial defect theory, we developed a three-dimensional model of twin–twin interactions, double twinning and other complex interfacial reactions that occur between twins acting on different interface planes. Using molecular dynamics, {1122} and {1121} twins in titanium were activated and produced facets, twin–twin interactions and double twins that we characterized with our model. The results showed excellent agreement between the molecular dynamics results and the model. Surprisingly, some highly ordered and mobile boundaries can be produced by these complex reactions, which could provide important insights for higher scale models of plasticity.

A Moment Tensor Potential (MTP) is developed for equiatomic CoCrNi and CoCrFeNi alloys to enable large-scale simulations of chemical short-range order (CSRO) and defect energetics with near density functional theory (DFT) accuracy. The training database includes unary to quaternary configurations as well as DFT derived short-range ordered structures, ensuring accurate representation of thermodynamically relevant chemical environments. The MTP reproduces DFT energies, forces, stresses, and elastic constants across diverse compositions and phases. Hybrid Monte Carlo/molecular dynamics simulations recover key CSRO trends, including Cr--Cr and Fe--Fe repulsion and Ni--Cr ordering, with correct sign structure across temperature. Intrinsic stacking fault (ISF) energies are predicted to be $54\phantom{\rule{0.16em}{0ex}}\mathrm{mJ}/{\mathrm{m}}^{2}$ for CoCrNi and $36\phantom{\rule{0.16em}{0ex}}\mathrm{mJ}/{\mathrm{m}}^{2}$ for CoCrFeNi, consistent with DFT trends and sensitive to local chemical fluctuations. The potential further enables simulations of solute segregation at grain boundaries while preserving structural units. The developed MTP enables transferable, physically consistent simulations of thermodynamic stability and defect mediated mechanical response in fcc medium entropy alloys across extended length and timescales.

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.