Atomic-level Insights Research Articles

Selective Methods Promote Protein Solid-State NMR.

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.

Bacterial Phosphorylation Suppresses Carbapenemase Activity of the Class-D β-Lactamase OXA-24/40 from Acinetobacter baumannii.

The resistance of Gram-negative bacteria to β-lactam antibiotics is mostly due to deactivation of the antibiotics by bacterial enzymes, β-lactamases. Disclosing the factors regulating β-lactamase activity is vital for developing therapies to combat multidrug-resistant pathogens, such as Acinetobacter baumannii. Recent A. baumannii studies have revealed post-translational phosphorylation of serine β-lactamases at the active site serine. However, the functional consequences of such phosphorylation are unclear. We have taken the first steps to define these consequences through studies of OXA-24/40, a carbapenem-hydrolyzing class D β-lactamase in A. baumannii. We generated OXA-24/40 phosphorylated at its active site serine, S81, and explored its effects via NMR and MS. Phosphorylation inhibits carbapenemase activity by altering the active site conformation and impeding the carboxylation of an active site lysine, a requirement for class D β-lactamase activity. The inhibition varies with the carbapenem side chain properties. Phosphorylation-induced chemical shift perturbations extend beyond the active site, suggesting allosteric effects. Our findings offer the first atomic-level insights into the functional consequences of serine phosphorylation of class D β-lactamases.

Structure of TnsABCD transpososome reveals mechanisms of targeted DNA transposition

Tn7-like transposons are characterized by their ability to insert specifically into host chromosomes. Recognition of the attachment (att) site by TnsD recruits the TnsABC proteins to form the transpososome and facilitate transposition. Although this pathway is well established, atomic-level structural insights of this process remain largely elusive. Here, we present the cryo-electron microscopy (cryo-EM) structures of the TnsC-TnsD-att DNA complex and the TnsABCD transpososome from the Tn7-like transposon in Peltigera membranacea cyanobiont 210A, a type I-B CRISPR-associated transposon. Our structures reveal a striking bending of the att DNA, featured by the intercalation of an arginine side chain of TnsD into a CC/GG dinucleotide step. The TnsABCD transpososome structure reveals TnsA-TnsB interactions and demonstrates that TnsC not only recruits TnsAB but also directly participates in the transpososome assembly. These findings provide mechanistic insights into targeted DNA insertion by Tn7-like transposons, with implications for improving the precision and efficiency of their genome-editing applications.

Cracking the chaperone code through the computational microscope.

The heat shock protein 90 kDa (Hsp90) chaperone machinery plays a crucial role in maintaining cellular homeostasis. Beyond its traditional role in protein folding, Hsp90 is integral to key pathways influencing cellular function in health and disease. Hsp90 operates through the modular assembly of large multiprotein complexes, with their composition, stability, and localization adapting to the cell's needs. Its functional dynamics are finely tuned by ligand binding and post-translational modifications (PTMs). Here, we discuss how to disentangle the intricacies of the complex code that governs the crosstalk between dynamics, binding, PTMs, and the functions of the Hsp90 machinery using computer-based approaches. Specifically, we outline the contributions of computational and theoretical methods to the understanding of Hsp90 functions, ranging from providing atomic-level insights into its dynamics to clarifying the mechanisms of interactions with protein clients, cochaperones, and ligands. The knowledge generated in this framework can be actionable for the design and development of chemical tools and drugs targeting Hsp90 in specific disease-associated cellular contexts. Finally, we provide our perspective on how computation can be integrated into the study of the fine-tuning of functions in the highly complex Hsp90 landscape, complementing experimental methods for a comprehensive understanding of this important chaperone system.

Atomic-Level Free Energy Landscape Reveals Cooperative Symport Mechanism of Melibiose Transporter.

The Major Facilitator Superfamily (MFS) transporters are an essential class of secondary active transporters involved in various physiological and pathological processes. The melibiose permease (MelB), which catalyzes the stoichiometric symport of the disaccharide melibiose and monovalent cations (e.g., Na+, H+, or Li+), is a key model for understanding the cation-coupled symport mechanisms. Extensive experimental data has established that positive cooperativity between the cargo melibiose and the coupling cation is central to the symport mechanism. However, the structural and energetic origins of this cooperativity remain unclear at the atomistic level for MelB and most other coupled transporters. Here, leveraging recently resolved structures in inward- and outward-facing conformations, we employed the string method and replica-exchange umbrella sampling simulation techniques to comprehensively map the all-atom free energy landscapes of the Na+-coupled melibiose translocation across the MelB in Salmonella enterica serovar Typhimurium (MelBSt), in comparison with the facilitated melibiose transport in a uniporter mutant. The simulation results unravel asymmetrical free energy profiles of melibiose translocation, which is tightly coupled to protein conformational changes in both the N- and C-terminal domains. Notably, the cytoplasmic release of the melibiose induces the simultaneous opening of an inner gate, resulting in a high-energy state of the system. Periplasmic sugar binding and cytoplasmic melibiose released are dynamically coupled with changes in the internal gating elements along the translocation pathway. The outward-facing sugar-bound state is thermodynamically most stable, while the occluded state is a transient state. The binding of Na+ facilitates melibiose translocation by increasing the melibiose-binding affinity and decreasing the overall free energy barrier and change. The cooperative binding of the two substrates results from the allosteric coupling between their binding sites instead of direct electrostatic interaction. These findings add substantial new atomic-level details into how Na+ binding facilitates melibiose translocation and deepen the fundamental understanding of the molecular basis underlying the symport mechanism of cation-coupled transporters.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Friction Mechanism of Sulfur-Containing Lubricant Additives Confined between Fe(100) Substrates.

Sulfur-containing lubricant additives can chemically react with metal surfaces under extreme conditions, such as high temperature and high pressure, forming protective films on the surfaces. However, the formation mechanisms and the friction-reducing and antiwear properties of these films remain unclear. In this study, we investigated the friction process of sulfur-containing additives confined between two iron surfaces using reactive molecular dynamics simulations. Our research revealed that in systems with a higher S/C ratio, an iron sulfide layer formed on the iron surfaces with Fe-S-Fe bridging bonds at the interface, resulting in relatively smaller friction and wear even under higher loads. However, in systems with lower S/C ratios, the presence of numerous interfacial Fe-Cn-Fe bridging bonds, caused by the hydrocarbon radicals released from the additives, led to the formation of thick amorphous shearing bands at the interface between the two substrates. In this case, the distributed sulfur atoms also exhibited some effect in reducing the shear resistance of the amorphous shearing bands due to the weak strength of S-Fe bonds compared to the strength of C-Fe bonds. These atomic-level insights help understand the antiwear characteristics of sulfur-containing lubricant additives confined between iron substrates.

Atomic-level insights into sensing performance of toxic gases on the InSe monolayer decorated with Pd and Pt under humid environment

The timely detection and monitoring of toxic gases are crucial for preserving the ecological environment and preventing their adverse effects on human health. In this study, we investigate the adsorption characteristics and sensitivity performance of Pd- and Pt-decorated InSe monolayers, referred to as Pd-InSe and Pt-InSe, towards five toxic gases (CO, NO, NH3, H2S, and SO2) using first-principle calculations. The results show that the doping of Pd and Pt atoms significantly improves the conductivity, adsorption capabilities, and sensing properties of InSe monolayer to these toxic gases. Both Pd-InSe and Pt-InSe monolayers exhibit chemical adsorption of CO, NO, NH3, and H2S, with adsorption energies ranging from –0.56 eV to –1.04 eV, leading to noticeable changes in the bandgap (8.84 % ∼ 31.03 %). Furthermore, the CO/Pd-InSe and H2S/Pt-InSe systems demonstrate excellent sensitivity, with high sensing response values of 1.54×104 and 66.20, respectively, and their sensitivity remains unaffected under humid environments. Importantly, Pt-InSe exhibits a fast recovery time of 0.05 s at 298 K, making it highly promising for the recyclable detection of H2S at room temperature. In contrast, Pd-InSe can be utilized as a reusable gas sensor for CO at 448 K with a good recovery time of 0.5 s. This work can provides theoretical guidance for the design and fabrication of InSe-based gas sensors for the detection of toxic gases.

Atomic-level insights into CeO2 performance: Chemical interactions in CMP explored through CeO2-SiO2 studies

This study explores the effects of atomic-level factors on the performance of CeO2 when interacting with SiO2 films. It specifically examines how different precursors, the ratio of Ce to OH, and reaction temperatures influence outcomes. Our findings reveal that smaller particles, around 5 nm in size, created using a Ce4+ precursor, are more effective at removing SiO2 films compared to larger, more crystalline particles from a Ce3+ precursor. This is due to their increased interaction with the SiO2 film during the polishing process, challenging the conventional emphasis on mechanical abrasion in chemical mechanical planarization (CMP) and highlighting the crucial role of chemical interactions. Further analysis through FT-IR and TGA-FTIR techniques showed distinct functional group profiles on the ceria surfaces. Ceria derived from Ce4+ exhibited a higher presence of OH and NO3 groups, enhancing adsorption capabilities, as verified by CHN analysis. Importantly, first-principles calculations identified these surface groups as key to improving adhesion to SiO2 films. Surfaces of amorphous CeO2, rich in -OH and -NO3 groups, showed significantly higher adhesion levels than their crystalline counterparts, connecting crystallinity to chemical functionality. This new insight leads to the possibility of designing next-generation CeO2 abrasives with increased chemical activity.

Molecular dynamics and experimental analysis of energy behavior during stress relaxation in magnetorheological elastomers

The diverse applications of magnetorheological elastomer (MRE) drive efforts to understand consistent performance and resistance to failure. Stress relaxation can lead to molecular chain deterioration, degradation in stiffness and rheological properties, and ultimately affect the life cycle of MRE. However, quantifying the energy and molecular dynamics during stress relaxation is challenging due to the difficulty of obtaining atomic-level insights experimentally. This study employs molecular dynamics (MD) simulation to elucidate the stress relaxation in MRE during constant strain. Magnetorheological elastomer models incorporating silicone rubber filled with varying magnetic iron particles (50–80 wt%) were constructed. Experimental results from an oscillatory shear rheometer showed the linear viscoelastic region of MRE to be within 0.001–0.01% strain. The simulation results indicated that stress relaxation has occurred, with stored energies decreased by 8.63–52.7% in all MRE models. Monitoring changes in energy components, the highest final stored energy (12,045 kJ) of the MRE model with 80 wt% Fe particles was primarily attributed to stronger intramolecular and intermolecular interactions, revealed by higher potential energy (3262 kJ) and van der Waals energy (− 2717.29 kJ). Stress relaxation also altered the molecular dynamics of this MRE model as evidenced by a decrease in kinetic energy (9362 kJ) and mean square displacement value (20,318 Å2). The MD simulation provides a promising quantitative tool for elucidating stress relaxation, preventing material failure and offering insights for the design of MRE in the nanotechnology industry.

Atomic-level insight into the carburization process of iron-based catalysts: A ReaxFF molecular dynamics study

Transition metal carbide catalysts have garnered widespread attention due to their outstanding catalytic performance. The carbide catalysts, such as FexC and MoxC are typically obtained from their oxide carburization. The reduction and carburization processes are key steps during the activation of oxide precursors, which determine the activity and stability. An understanding of the carburization process mechanism is very important for the optimization of carbide catalysts. Iron-based catalysts are widely used in the large-scale coal-to-liquid industry because of their low price and high activity. In this paper, the carburization process of iron oxide nanoparticles was simulated by the Reactive force field (ReaxFF) molecular dynamics method. The results illustrate that the competition between CO dissociation and reduction of oxides occurs at the 100 ps time scale, which can be tuned by particle size and oxygen vacancy. We have explained the counter-intuitive finding that small oxide particles are more difficult to achieve fully carburization than larger ones; this is because rapid carbon accumulation on the surface blocks the oxygen diffusion channels from bulk to surface. The atomic distribution heat map was carried out to demonstrate the carbon and oxygen penetration processes. Meanwhile, the volumetric strain analysis reveals the driving force comes from the strong tendency to form Fe-C bonds. The dependence on surface orientation was further systematically investigated. This work provides microscopic insights into the carburization process of iron-based materials and the fundamental knowledge for the optimization of catalyst synthesis procedures.

Boosting acidic hydrogen peroxide electrosynthesis on 2D metal-organic framework nanosheets based on cobalt porphyrins

The 2-electron electrocatalytic oxygen reduction reaction (2e− ORR) to hydrogen peroxide (H2O2) represents a promising strategy to resolve the high energy consumption and increasing environmental concerns inherent in the traditional anthraquinone process. The acidic 2e− ORR has emerged as an exciting alternative for industrial-level H2O2 production, whereas is hampered by the inferior H2O2 selectivity due to the uncontrollable proton-coupled electron transfer processes in an acidic environment. Herein, an ultrathin 2D metal–organic frameworks (MOFs) nanosheet based on cobalt tetra(4-carboxyphenyl) porphine (Co-TCPP NSs) is designed to promote H2O2 selectivity up to 96.5 %, accompanied with a remarkable H2O2 generation rate of 4677.42 mg·L−1·h−1. Of note, the Co-TCPP NSs also demonstrate its potential for the electro-Fenton process with a cumulative H2O2 concentration of 1.21 wt%, highlighting its practical potential in portable H2O2 generation electrochemical devices for distributed applications. Our findings demonstrated that the efficient H2O2 electrosynthesis could be attributed to the attenuated *OOH adsorption over Co-N4 moiety on the Co-TCPP NSs, which consequently suppresses its further reduction to form H2O. This work highlights the potential of 2D MOF architecture for the 2e− ORR and provides an atomic-level insight into the enhanced H2O2 selectivity.

Elucidating Pathways of Methanol Oxidation on Oxygen-Vacant NiOOH Sites Using a Synergistic in Situ Attenuated Total Reflectance Surface-Enhanced Infrared Absorption Spectroscopy and DFT Study

A thorough comprehension of the mechanism underlying the methanol oxidation reaction (MOR) on Ni-based catalysts is critical for electrocatalysis design and development of Ni-based alloys. However, the mechanism of MOR on these materials remains a matter of controversy. There have been few publications on employing surface enhanced spectroscopy to adequately capture the surface chemistry of MOR on NiOOH while the existing theoretical chemistry studies on the reaction mechanism presents a multitude of inconsistent and incomplete results. Here, we combine in situ surface-enhanced infrared absorption spectroscopy (SEIRAS) and density functional theory (DFT) to determine the mechanism and product distribution of MOR on monometallic Ni-based catalysts in alkaline media. Critically, the DFT calculations include the role of O-vacancy of NiOOH, which is argued to be the active site for methanol oxidation. The SEIRAS results show that two bands corresponding to nas(O=C–O) of HCOO- and nas(C–O) of HCO3 -/CO3 2- appears after the commencement of MOR and varies as a function of potentials. These spectroscopic results are in good agreement with the DFT-based energetics of reaction, suggesting that the MOR mainly proceeds in the following pathway: The early consumption of methanol yields HCOO- as the major product while increasing potentials stimulate further oxidation of adsorbed HCOO* to form HCO3 -/CO3 2-. On the other hand, we show a parallel pathway for the generation of HCO3 -/CO3 2- at high potentials that bypasses the formation of HCOO*. The two main pathways presented in this study are thermodynamically more feasible than the one predominantly reported in literature for MOR on NiOOH. The two former pathways circumvent CHO and/or CO to produce HCO3 -/CO3 2-, whereas the latter involves these species as intermediates. These DFT-based hypotheses are backed up by the spectroscopic evidence that no band associated with CHO and CO can be detected by SEIRAS. This study has the potential to offer invaluable atomic-level insights into MOR on NiOOH as a stepping stone to development of high-performance multi-metallic Ni-based catalysts. Figure 1

In Situ Soft XAS Study of Electrocatalysts with an Ultrahigh Vacuum-Compatible Liquid Flow Cell

Synchrotron X-ray absorption spectroscopy offers a unique tool for investigating the element-specific electronic structure of materials in chemistry and quantum physics fields. In particular, in situ/operando X-ray absorption spectroscopy (XAS) has yielded valuable atomic-level insight into electrocatalysts in operation. While most studies have employed "hard" X-rays that are higher than ~5 keV in energy, "soft" X-rays with energies around or below 1-2 keV have the unique capability to probe core-level excitations of light elements such as oxygen or p→d excitations of 1st-row transition metals. The low-energy soft X-rays easily become absorbed and scattered by ambient air, however, requiring ultrahigh vacuum (UHV) environments. To acquire soft XAS spectra of electrocatalysts in action within a UHV chamber, we employ home-fabricated SiNx membranes and a sealed electrochemical liquid flow cell. We demonstrate the use of our technique with canonical water oxidation catalysts RuO2 and MnOx.

A Theoretical Study on Doped-NiOOH Catalysts for Enhanced Urea Oxidation Reaction

Urea is a common waste specie in agricultural runoff water and has been considered as a promising oxidizable molecule for substitution of oxygen evolution reaction (OER) to save energy. However, the overpotential of electrochemical urea oxidation reaction (UOR) is still high due to the complicated six-electron transfer process on most metal catalysts and the competition with OER further limits catalyst options for UOR. The most promising and studied catalysts for UOR are Ni-based catalysts. Previous theoretical and experimental research works have focused on NiOOH step-edges as active sites for UOR. In this work, we focus on gaining atomic-level insight into the role of basal-type sites in UOR, which represent the largest surface area of NiOOH catalysts. We study the effects of vacancies as well as metal doping on the transformation from Ni(OH)2 to NiOOH active phase and the UOR pathway using density functional theory (DFT) calculations. By investigating various Ni(OH)2 and NiOOH (001) surfaces with vacancies and dopants such as Fe, Co and Cu, we are able to examine how the active sites influence the Ni-catalysts transformation and the urea oxidation. This work sheds light on the structure-property relationship of Ni(OH)2/NiOOH in urea oxidation and offers design principles for functional Ni-based materials, which will help accelerate the development of efficient UOR catalysts.

Intersystem Crossing Control of the Nb+ + CO2 → NbO+ + CO Reaction.

The transfer of an oxygen atom from carbon dioxide (CO2) to a transition metal cation in the gas phase offers atomic level insights into single-atom catalysis for CO2 activation. Given that these reactions often involve open-shell transition metals, they may proceed through intersystem crossing between different spin manifolds. However, a definitive understanding of such spin-forbidden reaction requires dynamical calculations on multiple global potential energy surfaces (PESs) coupled by spin-orbit couplings. In this work, we report global PESs and spin-orbit couplings for three low-lying spin (quintet, triplet, and singlet) states for the reaction between the niobium cation (Nb+) and CO2, which are used to investigate the nonadiabatic reaction dynamics and kinetics. Comparison with experimental data of kinetics and collision dynamics shows satisfactory agreement. This reaction is found to be very similar to that between Ta+ + CO2. Specifically, our theoretical findings suggest that the rate-limiting step in this reaction is intersystem crossing, rather than potential barriers.

“In Silico” prediction of antibiotics biodegradation by Ganoderma lucidum GILCC 1 laccase

Antibiotics present a pressing environmental challenge as emerging pollutants due to their persistence and role in promoting antibiotic-resistant bacteria. To model the utilization of Ganoderma lucidum GlLCC1 laccase in degrading antibiotics, a 3D homology model of GILCC1, based on Lentinus tigrinus mushroom laccase, was utilized. Five broad-spectrum WHO-designated antibiotics with molecular weights between 100 and 500 Da were selected. Molecular dynamics simulations were conducted at pH 3.0 and 7.0 to evaluate the interactions between GILCC1 and antibiotics in a TIP3P water box, with system behaviour assessed at 300 °K using an NPT assembly. ABTS (2,2ʹ-Azino-bis (3-ethylbenzthiazoline-6-sulfonic Acid)) served as the comparison molecule. The binding free energy indicated a strong affinity between 3D GILCC1 and various ligands. At pH 3.0, GILCC1 exhibited significant Gibbs free energy (ΔG), indicating a high affinity for Levofloxacin (LVX; −8.2 kcal mol−1), Sulfisoxazole (SFX; −7.8 kcal mol−1), Cefuroxime (CXM; −7.5 kcal mol−1), Cephradine (CFD; −7. 5 kcal mol−1), ABTS (−7.6 kcal mol−1), and Tetracycline (TE; −7.5 kcal mol−1), attributed to pocket topology and interactions such as hydrogen bonds and van der Waals forces. Electron transfer in GILCC1 involved a chain of residues, including His395 and Phe239. Although the affinity decreased at pH 7.0, the potential of GILCC1 to degrade antibiotics remained plausible. This study accurately predicted the behaviour of the laccase-antibiotic system, providing atomic-level insights into molecular interactions and emphasizing the importance of experimental assays and assessments of antibiotic degradation in wastewater, considering various chemical compounds. The use of ABTS as a mediator was suggested to enhance molecule affinity.Graphical abstract

Atomic-Level Insights of Polypyrrole Grafted InGaZnO Structure for ppb-Level Ozone Gas Sensing at Low Operating Temperature.

This study explores the utilization of the organic conductive molecule Polypyrrole (PPy) for the modification of Indium Gallium Zinc Oxide (IGZO) nanoparticles, aiming to develop highly sensitive ozone sensors. Pyrrole (Py) molecules undergo polymerization, resulting in the formation of extended chains of PPy that graft onto the surface of IGZO nanoparticles. This interaction effectively diminishes oxygen vacancies on the IGZO surface, thereby promoting the crystallization of the IGZO (1114) facets. The resultant structure exhibits promising potential for achieving high-performance wideband semiconductor gas sensors. The IGZO/PPy device forms a Straddling Gap heterojunction, facilitating enhanced electron transfer between IGZO and ozone molecules. Notably, the adsorption and desorption of ozone gas occur efficiently at a low temperature of approximately 25 °C, obviating the need for additional energy typically associated with wide bandgap semiconductor materials. Density Functional Theory (DFT) calculations attribute this efficiency to the enhanced number of active sites for ozone adsorption, facilitated by hydrogen bonds. The substantial conductivity of PPy, combined with its planar ring structure, induces positively charged polarization on the IGZO side upon ozone adsorption. The resultant device exhibits exceptional sensitivity, boasting a 4-fold improvement compared to sensors reliant solely on IGZO. Additionally, the response time is significantly reduced by a factor of 10, underscoring the practical viability and enhanced performance of the IGZO/PPy sensor field.

Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science

Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials.

Multiscale Modeling of CO2 Electrochemical Reduction on Copper Electrocatalysts: A Review of Advancements, Challenges, and Future Directions.

Although CO2 contributes to global warming, it also offers potential as a raw material for the production of hydrocarbons (CH4, C2H4 and CH3OH). Electrochemical CO2 reduction reaction (eCO2RR) is an emerging technology that utilizes renewable energy to convert CO2 into valuable fuels, solving environmental and energy problems simultaneously. Insights gained at any individual scale can only provide a limited view of that specific scale. Multiscale modeling, which involves coupling atomistic-level insights (DFT) and (MD), with mesoscale (KMCand MK) and macroscale (CFD) simulations, has received significant attention recently. While multiscale modeling of eCO2RR on electrocatalysts across all scales is limited due to its complexity, this review offers an overview of recent works on single scales and the coupling of two and three scales, such as "DFT+MD", "DFT+KMC", "DFT+MK", "KMC/MK+CFD" and "DFT+MK/KMC+CFD", focusing particularly on Cu-based electrocatalysts. This sets it apart from other reviews that solely focus exclusively on a single scale or only on a combination of DFT and MK/KMC scales. Furthermore, this review offers a concise overview of machine learning (ML) applications for eCO2RR, an emerging approach that has not yet been reviewed. Finally, this review highlights the key challenges, research gaps and perspectives of multiscale modeling for eCO2RR.