arXiv subjects

Reza Namakian

Publications and source records attributed to Reza Namakian.

5 recordsLinked to original sources

arXiv2026

Finite Temperature Stacking Fault Stability in Random and Locally Ordered CoCrNi beyond the Harmonic Approximation

Previous density functional theory (DFT) calculations for random solid solution (RSS) CoCrNi predict negative intrinsic stacking-fault energy (ISFE) at 0 K, contrary to experimental observations of finite stacking-fault widths. Two explanations have been proposed: finite-temperature stabilization of the RSS state, suggested by harmonic approximations showing increasing ISFE with temperature, and local chemical order (LCO), which shifts the ISFE to positive values at 0 K. Here, we compute temperature-dependent generalized stacking-fault free energies for RSS and LCO CoCrNi using a near-quantum-accuracy machine learning interatomic potential and the fully anharmonic projected average force integrator. Unlike harmonic approximations, our anharmonic calculations show that the RSS ISFE decreases with temperature and remains negative, indicating that RSS stacking faults are not thermally stabilized at elevated temperatures. By contrast, LCO maintains positive ISFE over 0-1000 K. Molecular dynamics simulations further confirm unbounded dislocation dissociation in RSS CoCrNi but finite stacking-fault widths in the LCO state.

cond-mat.mtrl-sci↗

arXiv2026

Compositional Complexity-Induced Ultralow Friction in Medium-Entropy MXenes

Two-dimensional MXenes are promising solid lubricants, but the roles of compositional complexity and surface chemistry in governing interfacial friction remain unclear. Here, we systematically investigate the adhesion and friction behaviors of medium-entropy (ME) MXenes, TiVNbMoC3 and TiVCrMoC3, and compare them with conventional titanium carbide MXenes, Ti2C and Ti3C2, using a SiO2 colloidal atomic force microscopy probe. Thermal annealing at 200 C converts OH surface terminations to O terminations, leading to pronounced reductions in adhesion energy and friction force across all MXenes studied. ME MXenes exhibit larger adhesion reductions because of their higher initial OH contents and more extensive OH-to-O conversion. In addition, their intrinsically higher out-of-plane bending stiffness suppresses energy dissipation during sliding, enabling ultralow friction. Notably, superlubricity is achieved in ME MXenes, with annealed TiVCrMoC3 exhibiting a coefficient of friction as low as 0.0022, outperforming graphene, MoSe2, and other MXenes evaluated using the same experimental approach. These findings identify compositional complexity as a powerful strategy for engineering MXenes with exceptional tribological performance and establish ME MXenes as a new class of solid lubricants.

cond-mat.mtrl-sci↗

arXiv2025

Amorphization-Mediated Si-I to Si-V Phase Transition and Reversible Amorphous-Si-V Phase Memory in Silicon Nanoparticles

Molecular dynamics simulations using a Gaussian Approximation Potential (GAP) reveal a stress triaxiality driven, two-step Si-I (diamond cubic) to Si-V (simple hexagonal) phase transition pathway in a spherical Si nanoparticle with a 10 nm diameter under triaxial compression. A transient amorphous phase first forms at the surface and propagates inward around Si-I core, where stress triaxiality is low (shear-dominated). Within the amorphous shell, the material recrystallizes into Si-V at locations of elevated stress triaxiality and hydrostatic pressure. The resulting Si-V structure transforms into a fully amorphous state upon unloading. A subsequent loading-unloading cycle applied to this amorphous nanoparticle reveals a reversible amorphous to Si-V transformation, demonstrating a nanoscale phase memory effect.

cond-mat.mtrl-sci↗

arXiv2025

Kinetics of Vacancy-Assisted Reversible Phase Transition in Monolayer MoTe$_2$

We investigate the kinetics of phase transition between the 2H and 1T$^\prime$ phases in monolayer MoTe$_2$ using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory to interpret the underlying mechanisms. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of 1T$^\prime$ phase is initiated by the coalescence of neighboring Te monovacancies into divacancies, which are found to be mobile and can interact with other Te vacancies to form small triangular 1T$^\prime$ islands. Growth of these islands proceeds either by incorporating pre-existing vacancies at the phase boundaries or, in their absence, by absorbing divacancies that migrate from the surrounding lattice. Once a critical island size is reached, vacancy-free growth becomes possible although with a higher activation barrier. Upon removal of external stimuli, the system reverts to 2H phase, during which Te vacancies reorganize into three-fold spoke-like vacancy lines at the island center. This reverse process and the subsequent 1T$^\prime$$\leftrightarrow$2H reversible transitions are diffusionless, rapid, do not require additional vacancies and can be driven by mild external stimuli. Although our analysis focuses on strain-induced transitions, the kinetic mechanisms are expected to be generalizable to other types of stimuli.

cond-mat.mtrl-sci↗

arXiv2024

Adaptive Loss Weighting for Machine Learning Interatomic Potentials

Training machine learning interatomic potentials often requires optimizing a loss function composed of three variables: potential energies, forces, and stress. The contribution of each variable to the total loss is typically weighted using fixed coefficients. Identifying these coefficients usually relies on iterative or heuristic methods, which may yield sub-optimal results. To address this issue, we propose an adaptive loss weighting algorithm that automatically adjusts the loss weights of these variables during the training of potentials, dynamically adapting to the characteristics of the training dataset. The comparative analysis of models trained with fixed and adaptive loss weights demonstrates that the adaptive method not only achieves a more balanced predictions across the three variables but also improves overall prediction accuracy.

physics.comp-ph↗