Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus failing to prevent its accumulation at the critical regions. Experimentally, it is challenging to expeditiously identify and generate phases causing strengthening and acting as strong H traps. Here, we demonstrate a computation-based design strategy to generate precipitates strongly trapping H. Based on the quantum machine learning Al-Sc-Cu potential, the optimal processing parameters of strong H trapping phases are determined, even though they are metastable in nature. Elemental mapping in electron microscope and atom probe tomography confirms the presence of Cu in Al₃Sc and its strong interaction with H. Hence, we envisage the proposed strategy will accelerate the design of HE-resistant microstructures of various technologically relevant materials via identification of desirable phases.

Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.

Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is crucial for enhancing hydrogen transport and storage technologies. However, the vast compositional space and complex chemical environments of MPEAs pose significant challenges. We develop highly accurate machine learning force field and neural network-driven kinetic Monte Carlo simulations to investigate H diffusion in body-centered cubic (BCC) MoNbTaW MPEAs. H diffusion exhibits super-Arrhenius behavior in MPEAs, dominated by the low percentile of the H solution energy spectrum. Robust analytical models are derived via machine learning symbolic regression to predict H diffusivity across general BCC MPEAs. Additionally, it is revealed that chemical short-range order (SRO) generally does not impact H diffusion in MoNbTaW MPEAs, except it enhances diffusion when H-favoring elements are present in low concentrations. These insights not only deepen our understanding of H diffusion dynamics in MPEAs but also guide the strategic development of advanced MPEAs for hydrogen-related applications by manipulating element type, composition, and SRO.

Multi-principal element alloys (MPEAs) are renowned for their enhanced mechanical strength relative to their constituent metals, as evidenced by various experimental techniques such as tension/compression tests and instrumental indentation. Nevertheless, atomistic simulations sometimes produce conflicting results, casting doubt on the consistently superior mechanical properties of MPEAs. In this study, machine-learning interatomic potentials (MLIPs) with first-principles accuracy were developed for body-centered cubic refractory MoNbTaW MPEAs, enabling systematic atomistic simulations under various deformation scenarios. The new MLIPs are supported by a comprehensive dataset encompassing extensive defects, and the established embedded-atom model (EAM) potential was benchmarked against both this dataset and the new MLIP. Simulations covering diverse compositions confirm that both MLIPs and EAM accurately capture the critical strengthening mechanisms in MoNbTaW MPEAs. It is revealed that MPEAs generally exhibit superior mechanical strength compared to their constituent metals in macroscale specimens, primarily due to solid solution strengthening during dislocation motion. However, at the nanoscale—where plasticity is predominantly governed by dislocation nucleation and grain boundary deformation—the constituent metals may outperform MPEAs. A critical length scale is identified above which MPEAs demonstrate enhanced mechanical strength relative to their constituent elements; below this scale, the advantage diminishes, underscoring a significant size-dependent effect that is crucial for optimizing MPEA applications, particularly at the nanoscale.

Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. Due to the inadequate accuracy of conventional stress-strain machine learning (ML) models caused by corrosion factors, a novel reinforcement self-learning ML algorithm combined with calculated features (accuracy R² >0.92) is developed. Based on the ML models, calculated work functions and mechanical moduli, a Computation Designed Corrosion-Resistant Al alloy is fabricated and verified. The performance (elongation reaches ∼30 %) is attributed to the H trapping Al-Sc-Cu phases (-1.44 eV H⁻¹) and Cu-modified η/η' precipitates inside the grain boundaries (GBs).

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.

Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%.