In this study, we explore the mechanisms underlying the exceptional intrinsic strength of face-centered cubic (FCC) Multi-Principal Element Alloys (MPEAs) using a multifaceted approach. Our methods integrate atomistic simulations, informed by both embedded-atom model and neural network potentials, with first-principles calculations, stochastic Peierls-Nabarro (PN) modeling, and symbolic machine learning. We identify a consistent, robust linear correlation between the strength of MPEAs and the standard deviation of the maximum stacking-fault restoring force (τ_max,sd) across various potentials. This finding is substantiated by comparing the experimental strengths of Cantor alloys’ subsystems and Ni_62.5V_37.5 against τ_max,sd values from high-throughput first-principle calculations. Our theoretical insights are derived from integrating the stochastic Peierls-Nabarro model with a shearable precipitation hardening framework, demonstrating that lattice distortion alone does not directly enhance intrinsic strength. Instead, τ_max,sd emerges as a critical determinant, capable of boosting the strength of MPEAs by up to tenfold. Our analysis reveals the critical role of the exponential form of the PN model in achieving substantial strength improvement by transforming the Gaussian-like distribution of τ_max into an exponential-like distribution of local Peierls stress. Additionally, using an advanced symbolic machine learning technique, the sure independence screening and sparsifying operator (SISSO) method, we derive interpretable relationships between MPEA strength, elastic properties, and τ_max statistics, offering new insights into the design and optimization of advanced MPEAs. These findings highlight that the nonlinear physics and atomic fluctuations characterizing MPEAs not only underpin their unconventional intrinsic strength but also contribute to other complex properties such as sluggish diffusion and cocktail effect.

In multiscale modeling methods (MMM), the integration of atomistic to continuum coupling is a common practice, where two regions have their own distinct length scales. The equilibrium configurations of such multiscale systems under given conditions are typically obtained through energy minimization algorithms (EMA). However, traditional EMAs, such as the conjugate gradient (CG) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithms, are unable to discern the diverse scales inherent in such systems. In this work, it is found that the convergence rate of energy minimization in multiscale simulations is significantly slower than that in full atomistic simulations, regardless of using CG, LBFGS algorithms or the latest fast inertial relaxation engine (FIRE). The lower efficiency emerges due to the coexistence of atoms and nodes with distinct length scales within the multiscale framework, yet the current EMAs fail to differentiate between them. It results in disparate convergence rates across different scales, which undermines both computational accuracy and efficiency. To address the issue, a multiscale FIRE algorithm which updates positions of atoms and nodes synchronously by employing appropriate effective mass is proposed. The optimal effective mass is determined by synchronizing the vibration of harmonic oscillators across different scales. By employing the multiscale FIRE algorithm, the computational efficiency increased by 24.4 and 23.7 times compared to the CG and LBFGS algorithms when used for multiscale nanoindentation simulations. These findings and the proposed algorithm provide valuable insights for structural relaxations of multiscale physical problems and are promising to further improve the computational accuracy and efficiency of MMMs.

Prone H reduction is considered an important factor in the poor corrosion resistance of Mg and its alloys, while the reduced H simultaneously impacts their mechanical properties whose mechanism is still unclear. It can be experimentally found that the elongation of Mg charged with atomic H is 2.76 % greater than that in air. To reveal the underlying physics, multi-scale modeling combining first-principle calculation, molecular dynamic/static (MD/MS) simulation, and crystal plasticity finite element method (CPFEM) is first employed to elaborate the influence of H on Mg at different length scales. The first-principle results show that the Prism-I {101¯0} exhibits the most corrosive nature with an effective H adsorption density that reaches 18 nm⁻² and its diffusion barrier is only 0.156 eV H⁻¹. Conversely, the Basal {0001} has the best surficial H resistance. After H infiltration into the Mg matrix, the generalized stacking fault energies of most twining planes decrease by 2.26 % ∼18.49 %. Especially for the Basal {0001}, the H not only lowers its stacking fault energy to -7.13 J m⁻², but also impedes its cleavage cracking along [101¯0] according to the MD/MS simulation. The presence of H within the grains induces early initiation of stacking fault and elevates the critical stress at the crack tips. The CPFEM modeling reveals that the difference in twining growth is concentrated within 4 % strain. The H addition promotes the twining of Mg, however, following 4 % strain, the relative activity of planes in the Mg/Mg-H models is consistent.

Periodic boundary condition along a dislocation line is commonly used in computing activation barriers or formation energies of kink pair of screw dislocation in BCC metals. Although the effect of periodic image interactions on the computation results is obvious, there had been no comprehensive analysis on such effect. In this work, we quantify it through combined nudged elastic band (NEB) simulations and theoretical analysis based on dislocation mechanics. The NEB calculation result demonstrates a non-negligible size dependence on the activation barrier at zero and low stresses. The theoretical analysis offers a practical approach to quantify such size effect without the need of time-consuming NEB simulations. Notably, a simple relationship between kink activation barrier and dislocation line length is derived at zero stress, offering a new approach to compute kink pair formation energy based on NEB simulation results.