Microstructure and composition dependence of mechanical characteristics of nanoimprinted AlCoCrFeNi high-entropy alloys

On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.

Atomic investigations on the tension-compression asymmetry of AlxFeNiCrCu (x = 0.5, 1.0, 1.5, 2.0) high-entropy alloy nanowires

The tension and compression of high-entropy alloy (HEA) nanowires (NWs) are remarkably asymmetric, but the micro mechanism is still unclear. In this research, the tension-compression asymmetry of Al_xFeNiCrCu HEA NWs (x = 0.5, 1.0, 1.5, 2.0) was quantitatively characterized via molecular dynamics simulations, focusing on the influences of the NW diameter, the Al content, the crystalline orientation, and the temperature, which are significant for applying HEAs in nanotechnology. The increased NW diameter improves the energy required for stacking faults nucleating, thus strengthening AlFeNiCrCu HEA NWs. A few twins during stretching weaken the strengthening effects, thereby decreasing the tension-compression asymmetry. The increased Al content raises the tension-compression asymmetry by promoting the face-centered cubic to body-centered cubic phase transition during stretching. The tension along the [001] crystalline orientation is stronger than the compression, while the [110] and [111] crystalline orientations are entirely the opposite, and the tension-compression asymmetry along the [111] crystalline orientation is the minimum. The diversities in the tension-compression asymmetry depend on the deformation mechanism. Compressing along the [001] crystalline orientation and stretching along the [110] crystalline orientation induces twinning. Deformation along the [111] crystalline orientation only leaves stacking faults in the NWs. Therefore, the tension and compression along the [111] crystalline orientation exhibit minimal asymmetry. As the temperature rises, the tension-compression asymmetry along the [001] and [111] crystalline orientations increases, while that along the [110] crystalline orientation decreases.