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.

Universal machine-learning interatomic potentials (uMLIPs) are emerging as foundation models for atomistic simulation, offering near-ab initio accuracy at far lower cost. Their safe, broad deployment is limited by the absence of reliable, general uncertainty estimates. We present a unified, scalable uncertainty metric, U, built from a heterogeneous ensemble that reuses existing pretrained MLIPs. Across diverse chemistries and structures, U strongly tracks true prediction errors and robustly ranks configuration-level risk. Using U, we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels: for tungsten, we match full density-functional-theory (DFT) training using 4% of the DFT data; for MoNbTaW, a dataset distilled by U supports high-accuracy potential training. By filtering numerical label noise, the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data. This framework provides a practical reliability monitor and guides data selection and fine-tuning, enabling cost-efficient, accurate, and safer deployment of foundation models.

Iron oxidation is a complex process involving critical atomistic events, such as atomic adsorption, diffusion, and surface reconstruction, understanding of which is significant for both surface science and coating technology. Atomistic simulation serves as an useful tool to investigate the processes, where description of interatomic interactions is required. However, selecting appropriate force field or interatomic potential is not only difficult, but also essential for getting accurate result. In this work, we present a detailed benchmark of reactive force fields (ReaxFFs) and universal machine learning interatomic potentials (uMLIPs) against density functional theory (DFT) calculations of oxygen adsorption on various α-iron surfaces, which is the first yet crucial step towards oxidation. The comparisons show the coverage-dependent performance and improvable accuracy of both ReaxFFs and uMLIPs at reproducing DFT results, with ReaxFFs outperforming uMLIPs. Subsequently, iron oxidation is simulated using ReaxFF and uMLIP. The results reveal the strong capability of ReaxFF and poor stability of uMLIP for describing reactive process, i.e., the formation of iron oxide. This may be attributed to the suitable functional form of ReaxFF for the description of bond changes. The insights presented here not only provide an example of benchmarking force field or interatomic potential for system of interest, but also highlight the applicability of ReaxFF and scopes of improvement of uMLIP.

The high thermal stability of a thermoelectric material, which maintains a stable conversion efficiency under prolonged heat exposure, is essential for sustainable thermoelectric applications. Despite the well-known relationship between thermal degradation and microstructural evolution, their underlying interplay remains unclear, with contradictory findings reported in the literature owing to the complex dependence of microstructural changes on the material composition. Herein, the effect of Sb doping on the thermal stability of NbCoSn half-Heusler compounds is investigated in detail by comprehensively analyzing their microstructural evolution. The results reveal that introducing 3.3 at.% Sb into NbCoSn markedly enhances the thermal stability, by preserving the lattice thermal conductivity after heat exposure. Advanced techniques, including atom probe tomography, scanning transmission electron microscopy, and neutron diffraction, show that this improvement is driven by the evolution of Sb-induced complementary point defects. Although heat exposure significantly reduces lattice disorder in intrinsic NbCoSn, NbCoSn_0.9Sb_0.1 retains its lattice disorder by forming alternative point defects, thereby maintaining its lattice thermal conductivity. This detailed experimental work, corroborated by ab initio calculations, highlights the pivotal role of the point defect dynamics in achieving robust thermoelectric performances in half-Heusler compounds for high-temperature applications.

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.

Group contribution methods (GCMs) provide a practical and computationally efficient approach for predicting thermodynamic properties of hydrocarbons, especially when experimental data are scarce. This review evaluates the evolution of GCMs from classical first-order schemes (e.g. Lydersen method, Joback method) to more advanced second-order frameworks (e.g. CG94, Sharma method), hybrid extensions, and emerging machine learning integrations. While first-order models are simple and widely used, these models struggle with branched and long-chain molecules. Second-order approaches significantly improve structural sensitivity and predictive accuracy, achieving deviations below 2–3% for critical properties and within 1 kcal/mol for formation enthalpies of branched alkanes. Nevertheless, challenges remain in extrapolating to highly complex molecules, underrepresented functional groups, and extreme conditions. Promising directions include reinforcement of second-order GCMs with molecular theory, systematic expansion of experimental and quantum-based datasets, and hybrid GCM–machine learning models that retain interpretability while improving generalisability. We recommend prioritising models that balance accuracy, robustness, simplicity, and transferability to accelerate sustainable process and product designs, particularly in applications such as fuel upgrading including hydroisomerisation, separation processes, and green chemical development.

Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction (HER), which faces challenges of slow kinetics and high overpotential. Efficient electrocatalysts, particularly single-atom catalysts (SACs) on two-dimensional (2D) materials, are essential. This study presents a few-shot machine learning (ML) assisted high-throughput screening of 2D septuple-atomic-layer Ga2CoS4−x supported SACs to predict HER catalytic activity. Initially, density functional theory (DFT) calculations showed that 2D Ga2CoS4 is inactive for HER. However, defective Ga2CoS4−x (x = 0–0.25) monolayers exhibit excellent HER activity due to surface sulfur vacancies (SVs), with predicted overpotentials (0–60 mV) comparable to or lower than commercial Pt/C, which typically exhibits an overpotential of around 50 mV in the acidic electrolyte, when the concentration of surface SV is lower than 8.3%. SVs generate spin-polarized states near the Fermi level, making them effective HER sites. We demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D Ga2CoS4−x. Using DFT data from 18 SACs, an ML model with high prediction accuracy and reduced computation time was developed. An intrinsic descriptor linking SAC atomic properties to HER overpotential was identified. This study thus provides a framework for screening SACs on 2D materials, enhancing catalyst design.

Photocatalytic water splitting represents a promising approach for sustainable hydrogen production, with two-dimensional Janus materials offering unique advantages through intrinsic electric fields that enhance charge separation. We present a comprehensive first-principles investigation of Janus AlXY₂ (X = Ga, In; Y = S, Se, Te) monolayers using density functional theory and ab initio molecular dynamics simulations. All six systems exhibit excellent structural, thermal, and mechanical stability with HSE06 bandgaps of 2.029–2.969 eV suitable for UV-light absorption. The asymmetric structure generates strong intrinsic electric fields of 5.391–6.437 V perpendicular to the monolayer plane, significantly enhancing photogenerated charge carrier separation. While pristine monolayers show poor hydrogen evolution reaction (HER) activity with Gibbs free energies of 1.937–2.371 eV, strategic introduction of metal vacancies dramatically improves performance, reducing ΔG_H values to −0.371 to ＋0.607 eV and approaching optimal catalytic conditions. These findings demonstrate the potential of defect-engineered 2D Janus AlXY₂ materials for efficient photocatalytic hydrogen production.

The development of advanced catalysts with innovative nanoarchitectures is critical for addressing energy and environmental challenges such as the electrochemical CO₂ reduction reaction (CO₂ RR). Herein, the synthesis of an innovative copper–sulfur planar structure, Cu–S–BDC, within a metal–organic framework (MOF) catalyst is presented, which demonstrates 100% selectivity toward formate as the sole carbon product. Structural analysis and surface characterizations reveal that Cu–S–BDC exhibits quasi-2D inorganic building units, with Cu bonded to two S-CH (Formula presented.) groups and one BDC linker, while carboxylate groups adopt a bridging coordination mode. This unique arrangement not only imparts remarkable structural stability but also enhances the electronic properties of the MOF, as evidenced by a narrow bandgap of 1.203 eV that facilitates efficient charge transfer and increased electrochemical current density in CO (Formula presented.) RR. Notably, it offers a Faradaic efficiency of 92% for formate at an overpotential as low as −0.4 V versus the reversible hydrogen electrode (RHE) in an aqueous electrolyte of 1 m KOH, as well as a current density of −25.8 mA cm² at −0.9 V versus RHE, averaged over 24 h of electrolysis. This study highlights a fresh perspective in the field of MOF electrocatalysts by demonstrating that engineering the metal coordination environment can significantly enhance the electronic properties and consequently improve the electrocatalytic performance of these materials.

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.

Accurate prediction of thermodynamic properties of hydrocarbons is essential for chemical process modelling. Conventional group contribution methods often are used to predict these properties. However, these methods often require extensive parameter sets to handle structural complexities. A refined group contribution method for predicting thermodynamic properties of hydrocarbon isomers with reduced complexity and improved accuracy is presented and discussed. By combining the structural framework of Constantinou and Gani (CG94) with a sensitivity-based selection of second-order groups, a reduced yet highly effective set of twelve second-order groups is identified. This reduced set retains the predictive power comparable to more complex models while significantly reducing the number of parameters. Linear regression is applied to model enthalpies and Gibbs free energies of formation for a wide temperature range. To test broader applicability, the model is further extended to properties that require nonlinear regression, including critical temperatures, critical pressures, acentric factors, and liquid densities. For all cases, the proposed model achieves high predictive accuracy, demonstrating its robustness and generalizability. This methodology balances interpretability, efficiency, and performance, making it suitable for both research and industrial thermodynamic modelling.

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.

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.

Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). These models offer broad applicability across the periodic table, achieving first-principles accuracy at a fraction of the computational cost of traditional DFT calculations. In this study, we demonstrate that state-of-the-art pretrained uMLIPs can effectively replace DFT for accurately modeling complex defects in a wide range of metals and alloys. Our investigation spans diverse scenarios, including grain boundaries and general defects in pure metals, defects in high-entropy alloys, hydrogen-alloy interactions, and solute-defect interactions. Remarkably, the latest EquiformerV2 models achieve DFT-level accuracy on comprehensive defect datasets, with root mean square errors below 5 meV atom−1 for energies and 100 meV Å−1 for forces, outperforming specialized machine learning potentials such as moment tensor potential and atomic cluster expansion. We also present a systematic analysis of accuracy versus computational cost and explore uncertainty quantification for uMLIPs. A detailed case study of tungsten (W) demonstrates that data on pure W alone is insufficient for modeling complex defects in uMLIPs, underscoring the critical importance of advanced machine learning architectures and diverse datasets, which include over 100 million structures spanning all elements. These findings establish uMLIPs as a robust alternative to DFT and a transformative tool for accelerating the discovery and design of high-performance materials.

Rechargeable lithium–sulfur batteries (LiSBs) assembled with earth-abundant and safe Li anodes are less prone to form dendrites on the surface, and sulfur-containing cathodes offer considerable potential for achieving high energy densities. Nevertheless, suitable sulfur host materials and their interaction with electrolytes are at present key factors that retard the commercial introduction of these batteries. Here we propose a two-dimensional metallic carbon phosphorus framework, namely, 2D CP3, as a promising sulfur host material for inhibiting the shuttle effect and improving electronic conductivity in high-performance Li–S batteries. The good electrical conductivity of CP3 eliminates the insulating nature of most sulfur-based electrodes. The dissolution of lithium polysulfides (LiPSs) into the electrolyte is largely prevented by the strong interaction between CP3 and LiPSs. In addition, the deposition of Li2S on CP3 facilitates the kinetics of the LiPS redox reaction. Therefore, the use of CP3 for Li–S battery cathodes is expected to suppress the LiPS shuttle effect and to improve the overall performance, which is ideal for the practical application of Li–S batteries.

Titanium dioxide (TiO₂) has been widely used as a photocatalyst in CO₂ reduction reaction (CO₂RR) due to its low cost, high stability, and strong absorption in the close-to-visible ultra-violet (UV) range. However, TiO₂ films suffer from poor selectivity in CO₂ reduction due to their unfavorable electronic properties. In this work, we address this challenge by fabricating ultra-thin (14 nm) defective TiO₂ films (TiO₂-DTF) to enhance the selectivity of CO₂RR towards formate. TiO₂ sol was prepared using a facile and reproducible sol-gel method and directly deposited onto the surface of the electrode, forming a uniform, ultra-thin TiO₂ layers with a high number of defects. The activity of the TiO₂-DTF catalyst was studied in both photochemical and photoelectrochemical CO₂RR, indicating that the applied potential increases both the yield and selectivity of CO₂RR to formate. The TiO₂-DTF photocathode exhibited remarkable formate production during CO₂ reduction, achieving exceptional Faradaic efficiencies of up to 45 %. To elucidate the mechanism of photoelectrochemical CO₂RR on TiO₂-DTF, an in-situ attenuated total reflection Fourier-transform infrared spectroscopy (in-situ ATR-FTIR) was used and experimental results were supported by density functional theory (DFT) calculations. This study demonstrates that ultra-thin highly defective TiO₂ film, prepared using the cost-effective and environmentally friendly sol-gel method, can be used as photoelectrocatalyst for CO₂ reduction.

One of the most promising energy carriers for transport applications are hydrogen-based energy carriers. NaBH₄ is a hydrogen energy carrier and produces hydrogen bubbles when it is dissolved in water. The formation of hydrogen bubbles hinders experimental measurements of the thermodynamic and transport properties of aqueous NaBH₄ solutions at elevated temperatures. Accurate knowledge of these properties is essential for the NaBH₄ hydrolysis reactor modeling and design. Molecular dynamics (MD) simulations provide the option to study the thermodynamic and transport properties of NaBH₄ aqueous solutions without hindering hydrogen bubble formation. In this work, a new force field is developed for BH₄^-, namely, the Delft force field of BH₄^- (DFF/BH₄^-), which, combined with additional force fields, can accurately describe experimental densities and viscosities of 0 to 5 m (mol salt/kg water) NaBH₄, 0 to 3 m NaB(OH)₄, and 1 m NaOH aqueous solutions at 295 K within 1.8% and 10.8% maximum deviation, respectively. Empirical fitting correlations are created for densities, viscosities, and self-diffusivities obtained from the MD simulations of 0 to 5 m NaBH₄, 0 to 5 m NaB(OH)₄, and 0 to 1 m NaOH aqueous solutions at 295-363 K for NaBH₄ hydrolysis reactor modeling and design purposes.

Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH– is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations.

This extensive review highlights the central role of classical molecular simulation in advancing hydrogen (H2) technologies. As the transition to a sustainable energy landscape is urgently needed, the optimization of H2 processes, spanning production, purification, transportation, storage, safety, and utilization is essential. To this end, accurate prediction of thermodynamic, transport, structural, and interfacial properties is important for overcoming engineering challenges across the entire H2 value chain. Experimental measurements, despite being the traditional way of obtaining these properties, can be limited by the distinctive nature of H2, harsh operating conditions, safety constraints, and extensive parameter spaces. Free from such limitations, classical molecular simulations, in the general frameworks of Monte Carlo and Molecular Dynamics, provide an optimal balance between computational efficiency and accuracy, bridging the gap between quantum mechanical calculations and macro-scale modeling. This review also systematically covers molecular simulation methods and force fields for computing key properties of H2 systems, such as phase and adsorption equilibria and transport coefficients. Beyond property prediction, we explore how molecular simulation reveals fundamental mechanisms governing hydrate formation and dissociation, membrane permeations, and H2 embrittlement. When possible, data from multiple sources are compared and critically assessed, while effort is put on evaluating the force fields used and methodological approaches followed in the literature. Finally, this review aims at identifying research gaps and future opportunities, emphasizing emerging approaches, such as molecular simulation in the era of artificial intelligence.