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.@en

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.@en

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.

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Purpose: Prednisolone, a synthetic corticosteroid drug, is extensively utilized to treat inflammatory diseases and regulates metabolism and the immune system in cancer treatment. However, these drugs are toxic and cause severe side effects if administrated for long durations and in large doses. This work intends to study the atomistic interactions of popular polymeric carrier like PLGA with the drug and thereby provide insights into achieving better loading and a sustained release. Methods: Molecular dynamics (MD) simulations of prednisolone (drug) encapsulated in Poly Lactic-co-Glycolic acid (PLGA) are performed in this study. Grand Canonical Monte Carlo (GCMC) simulations with MD simulations are conducted to determine the water penetration in PLGA polymer and polymer stability in water. The investigations from this study encompasses structural and dynamical parameters, including the end-to-end distance, radius of gyration of polymer chains, interaction energy, and diffusion coefficient of the drug. Results: The polymer-drug interactions are studied and identified from the simulation data of PLGA(75:25) and PLGA(50:50) polymers with prednisolone in an aqueous medium for optimal drug carrying capacity and effective drug release. Also, the polymeric systems of PLGA(75:25) and PLGA(50:50) are analyzed with the water penetrant loading using the Grand Canonical Monte Carlo (GCMC) and MD simulations. Water loading analysis revealed that PLGA(75:25) has the highest swelling compared to PLGA(50:50). Conclusion: This study highlights the characteristics and critical parameters for developing an optimal drug delivery system by investigating polymer-drug interactions, drug encapsulation, and water uptake in polymers using MD and GCMC simulations.

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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).

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This study aims to identify photo-/electrocatalysts that can enhance the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR), which are of utmost importance in electro-/photochemical energy systems, such as solar energy, fuel cells, water electrolyzers, or metal-air batteries. Our study focused on investigating the 2D Ge₂Se₂P₄ monolayer and found that it exhibits a bifunctional photocatalyst with a very high solar-to-hydrogen efficiency. The two-dimensional (2D) Ge₂Se₂P₄ monolayer has superior HER activity compared to that of most 2D materials, and it also outperforms the reference catalysts IrO₂(110) and Pt(111) in terms of low overpotential values for ORR and OER mechanisms. Such superior catalytic performance in the 2D Ge₂Se₂P₄ monolayer can be attributed to its electron states, charge transfer process, and suitable band alignments referring to normal hydrogen electrodes. Overall, the study suggests that the Ge₂Se₂P₄ monolayer could be an excellent bifunctional catalyst for advancing photo-/electrochemical energy systems.

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Vapor-Liquid Equilibria (VLE) of hydrogen (H₂) and aqueous electrolyte (KOH and NaCl) solutions are central to numerous industrial applications such as alkaline electrolysis and underground hydrogen storage. Continuous fractional component Monte Carlo simulations are performed to compute the VLE of H₂ and aqueous electrolyte solutions at 298-423 K, 10-400 bar, 0-8 mol KOH/kg water, and 0-6 mol NaCl/kg water. The densities and activities of water in aqueous KOH and NaCl solutions are accurately modeled (within 2% deviation from experiments) using the non-polarizable Madrid-2019 Na⁺/Cl⁻ ion force fields for NaCl and the Madrid-Transport K⁺ and Delft Force Field of OH⁻ for KOH, combined with the TIP4P/2005 water force field. A free energy correction (independent of pressure, salt type, and salt molality) is applied to the computed infinite dilution excess chemical potentials of H₂ and water, resulting in accurate predictions (within 5% of experiments) for the solubilities of H₂ in water and the saturated vapor pressures of water for a temperature range of 298-363 K. The compositions of water and H₂ are computed using an iterative scheme from the liquid phase excess chemical potentials and densities, in which the gas phase fugacities are computed using the GERG-2008 equation of state. For the first time, the VLE of H₂ and aqueous KOH/NaCl systems are accurately captured with respect to experiments (i.e., for both the liquid and gas phase compositions) without compromising the liquid phase properties or performing any refitting of force fields.

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Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials’ gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.@en

The technological landscape for industrial processes handling asphaltene is evolving at a rapid pace due to the increase in the extraction of heavy crude oil. The main underlying challenges in this regard are the flow assurance, the recovery of the spent solvent, and the sophisticated extractor setup required to develop the process to an industrial scale. The number of studies focused on the handling of the asphaltene at the atomic and molecular scales is growing enormously in order to identify new sustainable solvents for the effective extraction of asphaltene from heavy crude oil or oil-bearing sands. This Perspective focuses on the importance of density functional theory and molecular dynamics simulations to explore the broader range of asphaltene inhibitors, e.g., nanoparticles, ionic liquids, and deep eutectic solvents, to prevent asphaltene precipitation. We provide a concise overview of the major accomplishments, analyze the aspects that require attention, and highlight the path-breaking studies having a significant impact on the process of chemical enhanced oil recovery from heavy crude oil reservoirs primarily based on atomistic and molecular simulations.

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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.

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Rapid developments in the field of hydrogen energy have prompted the need for safe and efficient hydrogen transportation and storage. Steels form the backbone of the current energy infrastructure and thus offer a fast and cost-effective solution. Their excellent mechanical properties are attributed to the underlying microstructure which comprises of finely dispersed nano-precipitates. However, one major factor restricting their application is their susceptibility to Hydrogen Embrittlement (HE). In the past decade, experimental and theoretical works have been carried out to understand if the nano-sized carbides can aid in reducing the susceptibility to HE along with providing strengthening. Within this ab-inito study, we investigated the effectiveness of fully coherent nano-carbides (i.e. TiC, VC and NbC) to limit the diffusible hydrogen content in bcc Fe. Our study revealed that the interplay between hydrogen and carbon vacancies, local atomic environment at interface as well as elastic strain fields at the interface can lead to significantly increased hydrogen solubilities. While in TiC, the deepest traps were found to be in the bulk of carbides, in VC and NbC, the elastic strain fields around the interface led to the strongest trapping. Further, the formation of a two-hydrogen-vacancy complex was found to be favourable in VC. Finally, the migration barriers for hydrogen trapping in bulk TiC as well as across the Fe/TiC coherent interface indicate that these deep traps in the form of carbon vacancies are fairly accessible.

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Non-polarizable force fields fail to accurately predict free energies of aqueous electrolytes without compromising the predictive ability for densities and transport properties. A new approach is presented in which (1) TIP4P/2005 water and scaled charge force fields are used to describe the interactions in the liquid phase and (2) an additional Effective Charge Surface (ECS) is used to compute free energies at zero additional computational expense. The ECS is obtained using a single temperature-independent charge scaling parameter per species. Thereby, the chemical potential of water and the free energies of hydration of various aqueous salts (e.g., NaCl and LiCl) are accurately described (deviations less than 5% from experiments), in sharp contrast to calculations where the ECS is omitted (deviations larger than 20%). This approach enables accurate predictions of free energies of aqueous electrolyte solutions using non-polarizable force fields, without compromising liquid-phase properties.@en

Advanced (ultra)high-strength steels that utilise bcc-fcc microstructures are appealing solutions for producing a combination of high strength and deformability. However, they are also susceptible to hydrogen embrittlement (HE). As larger less stable retained austenite (RA) can impair mechanical performance, its size and morphology are critical factors for achieving and maintaining the desired properties. Here, we present a combined experimental–density functional theory (DFT) study on HE with medium-carbon direct-quenched and partitioned (DQ&P) martensitic steels with varying vol% and film-thickness of RA, showing significantly improved HE resistance as a function of bcc-enrichment and increasing RA film-thickness. DFT reveals low attraction of hydrogen in bcc-Fe with Al, implying a stronger push towards fcc. Furthermore, the solubility of hydrogen increases dramatically with the carbon-enrichment of fcc-Fe when hydrogen and carbon are second neighbours. The theoretical results explain the observed differences in hydrogen diffusion, trap density, and the consecutively lower HE in Al-DQ&P over Si-DQ&P. Our combined experimental and theoretical study thus highlights the important interplay of bcc and fcc phases and H-uptake within austenite-containing carbon steels.

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Two-dimensional (2D) Janus monolayers, distinguished by their intrinsic vertical electric fields, emerge as highly efficient and eco-friendly materials for advancing the field of hydrogen evolution reactions (HER). In this study, we explore, for the first time, the potential viability of the oxygenation phase of two-dimensional Janus transition metal dichalcogenides MoOX (X = S, Se, and Te) monolayers as an exceptionally efficient photocatalyst for hydrogen production. Based on first-principles computations, we demonstrate that all three monolayers exhibit semiconductor behavior, characterized by a band gap ranging from 0.66 to 1.55 eV. This narrow band gap renders the proposed materials highly efficient at absorbing light within the visible region. Excitingly, the introduction of an electrostatic potential difference ΔΦ has granted us the ability to surpass the conventional bandgap limit (E_g≥1.23). Consequently, all monolayers exhibit favorable band alignment with respect to the vacuum level. Moreover, the calculated solar-to-hydrogen efficiency for the envisaged monolayer exceeds the established theoretical limit. Particularly, the MoOTe monolayer emerges as an infrared-light-driven photocatalyst, demonstrating a remarkable solar-to-hydrogen efficiency limit of up to 25,21% when considering the entire solar spectrum. A thorough examination of the Gibbs free energy differences across these monolayers has revealed that the values during the oxygenation phase are significantly smaller and approach the optimum, in contrast to the parental two-dimensional Janus transition metal dichalcogenides. Our results conclusively establish that the proposed materials exhibit exceptional efficiency as photocatalysts for hydrogen evolution reactions. Notably, their efficacy is demonstrated even in the lack of co-catalysts or sacrificial agents.

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The introduction of pharmaceuticals into aquatic ecosystems can lead to the generation of antibiotic-resistant bacteria. This paper employed molecular dynamics simulations to examine the interactions between cationic/anionic surfactants and two antibiotics or drugs, namely, ciprofloxacin and azithromycin. The analysis focused on many factors to elucidate the mechanism by which the surfactant bilayer molecular structure affects the selected antibiotics. These factors include the tilt angle, rotational angle of the surfactants, electrostatic potential, and charge density along the bilayers. Our molecular-level investigation of the adsorption mechanisms of hydrophobic (azithromycin) and hydrophilic (ciprofloxacin) drugs on the cationic/anionic surfactant bilayer offers a crucial understanding for comprehending the optimal selection of surfactants for effectively separating antibiotics.

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All-solid electrolytes could lead to a technological breakthrough in the performance of all-solid-state batteries when combined with a lithium-metal anode. However, the use of a lithium-metal anode presents several challenges, such as dendrite growth, interface electrochemical stability, formation and propagation of cracks, and delamination of the electrode/electrolyte interfaces. This work aims to explore the effectiveness of using newly synthesized 2D graphyne-based membranes (namely graphyne, graphdiyne, and graphtriyne) for electrode protection in a solid polymer electrolyte battery through first-principle calculations, nudged elastic band method, and classical molecular dynamics simulation. Specifically, we aim to investigate the effectiveness of these membranes in mitigating the aforementioned challenges. A high external electric field of up to 0.5 V/Å, 0.75 V/Å, and 1 V/Å was applied to accelerate the ions diffusion process. The adsorption energies, charge transfer, and in-plane/out-plane diffusion of single lithium on graphyne-based surfaces were investigated. Afterward, we calculated and compared the Li⁺ permeability, the electrolyte molecules’ rejection efficiency, and the intrinsic properties of graphyne-based nanoporous membranes. Our findings show that both graphyne and graphdiyne surfaces effectively permit Li⁺ intercalation while preventing other electrolyte molecules from reaching the electrodes.

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Through a density functional theory-driven survey, a comprehensive investigation of two-dimensional (2D) Janus aluminum-based monochalcogenides (Al₂XY with X/Y = S, Se, and Te) has been performed within this study. To begin with, it is established that the examined phase, in which the Al-atoms are located at the two inner planes while the (S, Se, and Te)-atoms occupy the two outer planes in the unit cell, are energetically, mechanically, dynamically, and thermally stable. To address the electronic and optical properties, the hybrid function HSE06 has been employed. It is at first revealed that all three monolayers display a semiconducting nature with an indirect band gap ranging from 1.82 to 2.79 eV with a refractive index greater than 1.5, which implies that they would be transparent materials. Furthermore, the monolayers feature strong absorption spectra of around 10⁵ cm⁻¹ within the visible and ultraviolet regions, suggesting their potential use in optoelectronic devices. Concerning the photocatalytic performance, the conduction band-edge positions straddle the hydrogen evolution reaction redox level. Also, it is observed that the computed Gibbs free energy is around 1.15 eV, which is lower and comparable to some recently reported 2D-based Janus monolayers. Additionally, the thermoelectric properties are further investigated and found to offer a large thermal power as well as a high figure of merit (ZT) around 1.03. The aforementioned results strongly suggest that the 2D Janus Al-based monochalcogenide exhibits suitable characteristics as a potential material for high-performance optoelectronic and thermoelectric applications.

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Data for several key thermodynamic and transport properties needed for technologies using hydrogen (H₂), such as underground H₂ storage and H₂O electrolysis are scarce or completely missing. Force field-based Molecular Dynamics (MD) and Continuous Fractional Component Monte Carlo (CFCMC) simulations are carried out in this work to cover this gap. Extensive new data sets are provided for (a) interfacial tensions of H₂ gas in contact with aqueous NaCl solutions for temperatures of (298 to 523) K, pressures of (1 to 600) bar, and molalities of (0 to 6) mol NaCl/kg H₂O, (b) self-diffusivities of infinitely diluted H₂ in aqueous NaCl solutions for temperatures of (298 to 723) K, pressures of (1 to 1000) bar, and molalities of (0 to 6) mol NaCl/kg H₂O, and (c) solubilities of H₂ in aqueous NaCl solutions for temperatures of (298 to 363) K, pressures of (1 to 1000) bar, and molalities of (0 to 6) mol NaCl/kg H₂O. The force fields used are the TIP4P/2005 for H₂O, the Madrid-2019 and the Madrid-Transport for NaCl, and the Vrabec and Marx for H₂. Excellent agreement between the simulation results and available experimental data is found with average deviations lower than 10%.

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In this work, we computed electrical conductivities under ambient conditions of aqueous NaCl and KCl solutions by using the Einstein-Helfand equation. Common force fields (charge q = ±1 e) do not reproduce the experimental values of electrical conductivities, viscosities, and diffusion coefficients. Recently, we proposed the idea of using different charges to describe the potential energy surface (PES) and the dipole moment surface (DMS). In this work, we implement this concept. The equilibrium trajectories required to evaluate electrical conductivities (within linear response theory) were obtained by using scaled charges (with the value q = ±0.75 e) to describe the PES. The potential parameters were those of the Madrid-Transport force field, which accurately describe viscosities and diffusion coefficients of these ionic solutions. However, integer charges were used to compute the conductivities (thus describing the DMS). The basic idea is that although the scaled charge describes the ion-water interaction better, the integer charge reflects the value of the charge that is transported due to the electric field. The agreement obtained with experiments is excellent, as for the first time electrical conductivities (and the other transport properties) of NaCl and KCl electrolyte solutions are described with high accuracy for the whole concentration range up to their solubility limit. Finally, we propose an easy way to obtain a rough estimate of the actual electrical conductivity of the potential model under consideration using the approximate Nernst-Einstein equation, which neglects correlations between different ions.

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