Accurate and Compatible Force Fields for Molecular Oxygen, Nitrogen, and Hydrogen to Simulate Gases, Electrolytes, and Heterogeneous Interfaces

Reversible molecular simulation for training classical and machine-learning force fields

The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine-learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here, we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms and to train a machine-learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables.

[Bmpy] or [Bmim]: which is better for H2 sensing?

Ionic liquids (ILs) have been found to be a good type of electrolyte material to fabricate highly sensitive H2 sensors, accredited to their minimal vapor pressure and array of tunable physicochemical properties. Of the two IL molecules commonly used, [Bmpy][NTf2] and [Bmim][NTf2], experimental results reveal that [Bmim][NTf2] exhibits a higher ionic diffusivity and conductivity than [Bmpy][NTf2]. However, recent hydrogen sensing tests demonstrate that [Bmpy][NTf2] based sensors are more sensitive instead. Until now, this seemingly contradictory phenomenon has lacked a reasonable explanation because of the spatial and temporal limitations of current experimental techniques. Thus, molecular dynamics (MD) simulations were used in this work to examine the electric double layer (EDL) structure and H2 diffusion in the EDL for the two IL species. With the use of multiple descriptors like IL number distributions, orientation distributions, etc., the electrolyte|electrode heterostructure can be categorized into three distinct regions: the 1st EDL, the 2nd EDL, and the bulk phase. The self-diffusion coefficients of IL cations and anions for each region are then calculated and compared, which is, as per our knowledge, the first time that the diffusion-related differences in the different regions of the electrolyte|electrode interphase have been addressed. As compared to [Bmim], [Bmpy] cations demonstrate a more scattered orientation distribution within the 1st EDL, which allows more H2 transport pathways to the electrode and thus leads to a higher possibility of H2 redox reaction. Furthermore, H2 molecules show a slightly higher bulk solubility and higher probability density in the 1st EDL of the positive electrode (PE) in [Bmpy][NTf2] than in [Bmim][NTf2]. Collectively, these results provide insights into why [Bmpy][NTf2] is a more sensitive electrolyte material than [Bmim][NTf2].

Toward Molecular Simulation Guided Design of Next-Generation Membranes: Challenges and Opportunities

Membranes provide energy-efficient solutions for separating ions from water, ion-ion separation, neutral or charged molecules, and mixed gases. Understanding the fundamental mechanisms and design principles for these separation challenges has significant applications in the food and agriculture, energy, pharmaceutical, and electronics industries and environmental remediation. In situ experimental probes to explore Angstrom-nanometer length-scale and pico-nanosecond time-scale phenomena remain limited. Currently, molecular simulations such as density functional theory, ab initio molecular dynamics (MD), all-atom MD, and coarse-grained MD provide physics-based predictive models to study these phenomena. The status of molecular simulations to study transport mechanisms and state-of-the-art membrane separation is discussed. Furthermore, limitations and open challenges in molecular simulations are discussed. Finally, the importance of molecular simulations in generating data sets for machine learning and exploration of membrane design space is addressed.

Molecular Dynamics Simulations of Native Protein Charging in Electrosprayed Droplets with Experimentally Relevant Compositions

Electrospray ionization-mass spectrometry (ESI-MS) has been widely used to study proteins given its preservation of native-protein structure when transitioning to the gas-phase. Understanding the influence of experimental factors on ESI can provide insight into the resulting charge states, as well as the degree to which "native" structure is maintained. Experimentally, it is challenging to characterize nanometer-scale electrosprayed droplets; however, molecular dynamics (MD) simulations pose an attractive solution by providing a molecular perspective of protein-ion formation. By resolving many approximations used in past MD simulations of ESI, we demonstrate the capability of simulating electrosprayed droplets with experimentally relevant droplet compositions and behavior. This is accomplished by modeling proton transfers between all titratable molecules in simulated droplets under atmospheric conditions; thus, enabling simulated droplets containing ammonium acetate that form experimentally observed (de)protonated protein ions. Application of the proposed protocol to several native proteins in positive- and negative-ion mode ESI produced simulated weighted averages of charge-state distributions that differed by 0.14 compared to experimental values. Our simulations suggest that changes in residue basicity during the transition to the gas-phase play a significant role in moderating protein charging during native-ESI and can explain many experimentally observed trends. Ionic protein structures produced via the simulated model maintained, on average, 73% of their native contacts into the gas phase relative to solution-phase structures. While applied towards native proteins here, novel insights into effects of the transition to gas-phase enable a deeper understanding of the ESI process itself and thus, are informative regardless of analyte.

Solvation-mediated adsorption mechanism of solvated lithium ions at a charged solid-liquid interface for electrochemical energy storage: atomic scale investigation and insights

Ion encapsulation by solvent molecules significantly impacts ion transport and the adsorption mechanism in energy storage devices. The aim of this investigation is to analyse the adsorption mechanisms associated with the solvation shell of lithium ions near the electrode/electrolyte interface during the charging process. Simulations using molecular dynamics (MD) are conducted for LiPF6 salt in PC solvent confined in between two flat carbon electrodes. The thermodynamic and physical properties of the simulation show excellent agreement with experimental values. Results indicate that the lithium ion forms a strong tetrahedral solvation structure with PC solvent molecules. Orientation analysis reveals that the polar ends of the solvent molecules in the lithium ion solvation structure are anchored to the positive electrode, which is caused by strong attractive interactions, particularly for high surface charge densities. Meanwhile, the solvation structure and solvent molecules undergo rotation close to the negative electrode at high surface charge densities. These aforementioned phenomena lead to solvation-mediated electrostatic interactions between solvated lithium ions and the electrodes. Finally, the differential capacitance for both positive and negative electrodes decreases under these solvation-mediated electrostatic interactions. This study provides a unique intuitive image of possible implications of the solvation structure on the charging performance of energy storage devices, along with perspectives on developing electrolytes with favorable orientations.

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress-strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Slowing Down Peptide Translocation through MoSi2N4 Nanopores for Protein Sequencing

Precise identification and quantification of amino acids are crucial for numerous biological applications. A significant challenge in the development of high-throughput, cost-effective nanopore protein sequencing technology is the rapid translocation of protein through the nanopore, which hinders accurate sequencing. In this study, we explore the potential of nanopore constructed from a novel two-dimensional (2D) material MoSi2N4 in decelerating the velocity of protein translocation using molecular dynamics simulations. The translocation velocity of the peptide through the MoSi2N4 nanopore can be reduced by nearly an order of magnitude compared to the MoS2 nanopore. Systematic analysis reveals that this reduction is due to stronger interaction between the peptide and MoSi2N4 membrane surface, particularly for aromatic residues, as they contain aromatic rings composed of relatively nonpolar C-C and C-H bonds. By adjusting the proportion of aromatic residues in peptides, further control over peptide translocation velocity can be achieved. Additionally, the system validates the feasibility of using an appropriate nanopore diameter for protein sequencing. The theoretical investigations presented herein suggest a potential method for manipulating protein translocation kinetics, promising more effective and economical advancements in nanopore protein sequencing technology.

Toward Surface Passivation of Black Phosphorus via a Self-Assembled Ferrocene Molecular Layer

Black phosphorus (BP), a promising two-dimensional material, faces significant challenges for its applications due to its instability in air and water. Herein, molecular dynamics simulations reveal that a self-assembled ferrocene (FeCp2) molecular layer can form on BP surfaces and remain stable in aqueous environments, predicting its effectiveness for passivation. This theoretical finding is corroborated by X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, Raman spectroscopy, and optical microscopy observations. In addition, atomic force microscopy analysis confirms that ferrocene-passivated BP flakes with thicknesses of <10 nm exhibit minimal degradation over 25 days. Density functional theory calculations further show that ferrocene stabilizes BP and modulates its band gap, improving its electronic applicability. Notably, we find that the passivation of BP by metallocenes is universal because other metallocenes (VCp2, MnCp2, and NiCp2) exhibit similar adsorption behaviors. These findings underscore the potential of metallocenes as versatile protective layers for BP and other materials that are not stable in air.

Computational Study of the 1,3-Dipolar Cycloaddition between Criegee Intermediates and Linalool: Atmospheric Implications

In this research, we investigated the essential role of biogenic volatile organic compound emissions in regulating tropospheric ozone levels, atmospheric chemistry, and climate dynamics. We explored linalool ozonolysis and secondary organic aerosol formation mechanisms, providing key insights into atmospheric processes. Computational techniques, such as density functional theory calculations and molecular dynamics simulations, were employed for the analysis. Our study delves into the energetic and mechanistic aspects of the 1,3-dipolar cycloadditions involving linalool and its ozonolysis byproducts, known as Criegee intermediates. A total of 24 reactions were analyzed from the three possible Criegee intermediates formed, resulting from different reactant orientations and their endo/exo isomers. We found that only four of these reactions exhibit large rate constants that can compete with tropospheric reactions. This reactivity pattern was characterized by analyzing reactivity indices from conceptual density functional theory and determining that electron flux originates from linalool to the Criegee intermediates. Greater electrophilicity in the Criegee intermediates results in a lower reaction activation energy, confirmed by the global electrophilicity index. Furthermore, using the activation strain model and energy decomposition analysis, we found that differences in activation energies were primarily driven by nonorbital energy factors. Finally, molecular dynamics simulations showed that the final cycloaddition adducts of the most favorable 1,3-dipolar cycloaddition interact favorably with water molecules in an exergonic process, adsorbing up to 92% of the water molecules after 20 ns. Our findings provide insights that enhance our understanding of the interactions between natural emissions and atmospheric constituents.

PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases

Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H2 fromDesulfovibrio fructosovorans [NiFe] hydrogenase as a training set. [NiFe] hydrogenases are receiving increasing attention in biotechnology due to their high efficiency in the generation of H2, which is considered by many to be the fuel of the future. However, some of these enzymes are sensitive to O2 and CO. Many efforts have been made to rectify this problem and generate air-stable enzymes by introducing mutations that selectively regulate the access of specific gas molecules to the catalytic site. Herein, we showcase the performance of PathInHydro for the identification of unbinding paths in different test sets, including another gas molecule and a different [NiFe] hydrogenase, which demonstrates its feasibility for the trajectory analysis of a diversity of gas molecules along enzymes with mutations and sequence differences. PathInHydro allows the user to skip time-consuming manual analysis and visual inspection, facilitating data analysis for MD simulations of ligand unbinding from [NiFe] hydrogenases. The codes and data sets are available online: https://github.com/FarzinSohraby/PathInHydro.

Dehydrogenase versus oxidase function: the interplay between substrate binding and flavin microenvironment

Redox enzymes, mostly equipped with metal or organic cofactors, can vary their reactivity with oxygen by orders of magnitudes. Understanding how oxygen reactivity is controlled by the protein milieu remains an open issue with broad implications for mechanistic enzymology and enzyme design. Here, we address this problem by focusing on a widespread group of flavoenzymes that oxidize phenolic compounds derived from microbial lignin degradation, using either oxygen or a cytochrome c as electron acceptors. A comprehensive phylogenetic analysis revealed conserved amino acid motifs in their flavin-binding site. Using a combination of kinetics, mutagenesis, structural, and computational methods, we examined the role of these residues. Our results demonstrate that subtle and localized changes in the flavin environment can drastically impact on oxygen reactivity. These effects are afforded through the creation or blockade of pathways for oxygen diffusion. Substrate binding plays a crucial role by potentially obstructing oxygen access to the flavin, thus influencing the enzyme's reactivity. The switch between oxidase and dehydrogenase functionalities is thereby achieved through targeted, site-specific amino acid replacements that finely tune the microenvironment around the flavin. Our findings explain how very similar enzymes can exhibit distinct functional properties, operating as oxidases or dehydrogenases. They further provide valuable insights for the rational design and engineering of enzymes with tailored functions.

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure-Property Relationships in Nanoporous 2D Materials

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO2, N2, and O2 gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO2/N2, O2/CO2, and O2/N2 gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.

Revisiting Numerical Solutions of Weakly Bound Noble Gases' Vibrational Energy Levels Modeled by the Improved Lennard-Jones Potential

We revisit the numerical solutions of vibrational eigenstates of weakly bound homonuclear and heteronuclear noble gas pairs by applying a Fortran program based on the Numerov method. The harmonic, Lennard-Jones (LJ), Morse, Tang-Toennies (TT), and Improved Lennard-Jones (ILJ) potential models have been implemented to represent the potential energy curves (PECs). The obtained vibrational energies spectrum was tested on the experimental data and accurate ab initio calculations at CCSD(T)/CBS level. The vibrational eigenvalues and eigenfunctions can be reproduced accurately within the ILJ potential model. Moreover, considering the calculated lifetime of van der Waals (vdW) complexes, the implementation of ILJ rather than standard LJ potential model has a significant impact on the systems dynamics by providing more representative atomic trajectories when the function is incorporated in force fields for molecular dynamics (MD) simulations. Overall, the ILJ function is the best suited potential model for the representation of vibrational motions and the determination of vibrational energy levels of weakly bound systems, both at equilibrium and non-equilibrium conditions.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

Wetting Preference of Silica Surfaces in the Context of Underground Hydrogen Storage: A Molecular Dynamics Perspective

The growing interest in large-scale underground hydrogen (H2) storage (UHS) emphasizes the need for a comprehensive understanding of the fundamental characteristics of subsurface environments. The wetting preference of subsurface rock is a crucial parameter influencing the H2 flow behavior during storage and withdrawal processes. In this study, we utilized molecular dynamics simulation to evaluate the wetting preference of the silica surface in subsurface hydrogen systems, with the aim of addressing disparities observed in experimental results. We conducted an initial comprehensive assessment of potential models, comparing the wettability of five common silica surfaces with different surface morphologies and hydroxyl densities in CO2-H2/water/silica systems against experimental data. After introducing the INTERFACE force field as the most accurate potential model for the silica surface, we evaluated the wetting behavior of the α-quartz (101) surface with a hydroxyl density of 5.9 number/nm2 under the impact of actual geological storage conditions (333-413 K and 10-30 MPa), the coexistence of cushion gases (i.e., CO2, CH4, and N2) at various mole fractions, and pH levels ranging from 2 to 11 characterized through considering the negative charges of 0 to -0.12 C/m2 via deprotonation of silanol on the silica surface. Our results indicate that neither pressure nor temperature has a significant impact on the wetness of the silica in the case of pure H2 (single component UHS operations). However, when CO2 coexists with H2, especially at higher mole fractions, an increase in pressure and a decrease in temperature lead to higher contact angles. Moreover, when the mole fraction of cushion gas ranges from 0 to 1, the contact angle increases 20, 9.5, and 4.5° for CO2, CH4, and N2, respectively, on the neutral silica substrate. Interestingly, at higher pH conditions where the silica surface carries a negative charge, the contact angle considerably reduces where surface charges of -0.03 and -0.06 C/m2 result in an average reduction of 20 and 80% in the contact angle, respectively. More importantly, at a pH of ∼11 (-0.12 C/m2), a 0° contact angle is observed for the silica surface under all temperatures, pressures, types of cushion gases, and varying mole fractions.

Singlet oxygen-mediated photochemical cross-linking of an engineered fluorescent flavoprotein iLOV

Genetically encoded photoactive proteins are integral tools in modern biochemical and molecular biological research. Within this tool box, truncated variants of the phototropin two light-oxygen-voltage flavoprotein have been developed to photochemically generate singlet oxygen (1O2) in vitro and in vivo, yet the effect of 1O2 on these genetically encoded photosensitizers remains underexplored. In this study, we demonstrate that the "improved" light-oxygen-voltage flavoprotein is capable of photochemical 1O2 generation. Once generated, 1O2 induces protein oligomerization via covalent cross-linking. The molecular targets of protein oligomerization by cross-linking are not endogenous tryptophans or tyrosines, but rather primarily histidines. Substitution of surface-exposed histidines for serine or glycine residues effectively eliminates protein cross-linking. When used in biochemical applications, such protein-protein cross-links may interfere with native biological responses to 1O2, which can be ameliorated by substitution of the surface exposed histidines of improved" light-oxygen-voltage or other 1O2-generating flavoproteins.

Exploitation of Pore Structure for Increased CO2 Selectivity in Type 3 Porous Liquids

CO2 capture requires materials with high adsorption selectivity and an industrial ease of implementation. To address these needs, a new class of porous materials was recently developed that combines the fluidity of solvents with the porosity of solids. Type 3 porous liquids (PLs) composed of solvents and metal-organic frameworks (MOFs) offer a promising alternative to current liquid carbon capture methods due to the inherent tunability of the nanoporous MOFs. However, the effects of MOF structural features and solvent properties on CO2-MOF interactions within PLs are not well understood. Herein experimental and computational data of CO2 gas adsorption isotherms were used to elucidate both solvent and pore structure influences on ZIF-based PLs. The roles of the pore structure including solvent size exclusion, structural environment, and MOF porosity on PL CO2 uptake were examined. A comparison of the pore structure and pore aperture was performed using ZIF-8, ZIF-L, and amorphous-ZIF-8. Adsorption experiments here have verified our previously proposed solvent size design principle for ZIF-based PLs (1.8× ZIF pore aperture). Furthermore, the CO2 adsorption isotherms of the ZIF-based PLs indicated that judicious selection of the pore environment allows for an increase in CO2 selectivity greater than expected from the individual PL components or their combination. This nonlinear increase in the CO2 selectivity is an emergent behavior resulting from the complex mixture of components specific to the ZIF-L + 2'-hydroxyacetophenone-based PL.

Surface molecular pump enables ultrahigh catalyst activity

The performance of electrocatalysts is critical for renewable energy technologies. While the electrocatalytic activity can be modulated through structural and compositional engineering following the Sabatier principle, the insufficiently explored catalyst-electrolyte interface is promising to promote microkinetic processes such as physisorption and desorption. By combining experimental designs and molecular dynamics simulations with explicit solvent in high accuracy, we demonstrated that dimethylformamide can work as an effective surface molecular pump to facilitate the entrapment of oxygen and outflux of water. Dimethylformamide disrupts the interfacial network of hydrogen bonds, leading to enhanced activity of the oxygen reduction reaction by a factor of 2 to 3. This strategy works generally for platinum-alloy catalysts, and we introduce an optimal model PtCuNi catalyst with an unprecedented specific activity of 21.8 ± 2.1 mA/cm2 at 0.9 V versus the reversible hydrogen electrode, nearly double the previous record, and an ultrahigh mass activity of 10.7 ± 1.1 A/mgPt.

Thermal transport properties of gas-filled silica aerogels

Silica aerogel (SA), recognized as an efficient insulating material, is characterized by its extremely low thermal conductivity (TC) and high porosity, presenting extensive application potential in aerospace and building energy conservation. In this study, the thermal transport properties of gas-filled SA are explored using molecular dynamics (MD) methods. It is found that an increase in porosity leads to a significant decrease in TC, primarily due to enhanced phonon scattering and reduced material stiffness. Additionally, the TC of SA influenced by gas exhibits a pattern of initial decrease, followed by an increase, and then a decrease again, driven by complex interactions between gas molecules and pore walls, phonon localization, and scattering mechanisms. At a gas concentration of 80%, the TC in confined spaces is significantly increased by nitrogen, attributed to enhanced intermolecular interactions and increased collision frequency. The impact of gases on the TC of gas-solid coupled composite materials is also investigated, revealing that gas molecules serve as a "bridge" for phonons, playing a crucial role in reducing interfacial scattering and enhancing low-frequency vibrational modes, thus further enhancing heat transfer efficiency. The TC of these composite materials is primarily regulated by the gas-phase TC in response to temperature, while the response to strain is predominantly governed by variations in the solid-phase TC. These results provide essential theoretical support and design guidelines for the development and design of new high-efficiency insulating materials.

Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase

[NiFe] hydrogenases can act as efficient catalysts for hydrogen oxidation and biofuel production. However, some [NiFe] hydrogenases are inhibited by gas molecules present in the environment, such as O2 and CO. One strategy to engineer [NiFe] hydrogenases and achieve O2- and CO-tolerant enzymes is by introducing point mutations to block the access of inhibitors to the catalytic site. In this work, we characterized the unbinding pathways of CO in the complex with the wild-type and 10 different mutants of [NiFe] hydrogenase from Desulfovibrio fructosovorans using τ-random accelerated molecular dynamics (τRAMD) to enhance the sampling of unbinding events. The ranking provided by the relative residence times computed with τRAMD is in agreement with experiments. Extensive data analysis of the simulations revealed that from the two bottlenecks proposed in previous studies for the transit of gas molecules (residues 74 and 122 and residues 74 and 476), only one of them (residues 74 and 122) effectively modulates diffusion and residence times for CO. We also computed pathway probabilities for the unbinding of CO, O2, and H2 from the wild-type [NiFe] hydrogenase, and we observed that while the most probable pathways are the same, the secondary pathways are different. We propose that introducing mutations to block the most probable paths, in combination with mutations to open the main secondary path used by H2, can be a feasible strategy to achieve CO and O2 resistance in the [NiFe] hydrogenase from Desulfovibrio fructosovorans.

Molecular Simulations of Hydrogen Sorption in Semicrystalline High-Density Polyethylene: The Impact of the Surface Fraction of Tie-Chains

The modeling of the barrier properties of semicrystalline polymers has gained interest following the possible application of such materials as protective liners for the safe supply of pressurized hydrogen. The mass transport in such systems is intimately related to the complex intercalation between the crystal and amorphous phases, which was approached in this work through an all-atom representation of high-density polyethylene structures with a tailored fraction of amorphous-crystalline connections (tie-chains). Simulations of the polymer pressure-volume-temperature data and hydrogen sorption were performed by means of molecular dynamics and the Widom test particle insertion method. The discretization of the simulation domains of the semicrystalline structures allowed us to obtain profiles of density, degree of order, and gas solubility. The results indicated that the gas sorption in the crystalline regions is negligible and that the confinement of the amorphous phase between crystals induces a significant increase in density and a drop in the sorption capacity, even in the absence of tie-chains. Adding ties between the crystal and the amorphous phase results in further densification, an increase of the lamella tilt angle, and a decrease in the degree of crystallinity and hydrogen sorption coefficient, in agreement with several literature references.

Machine Learning-Based Interfacial Tension Equations for (H2 + CO2)-Water/Brine Systems over a Wide Range of Temperature and Pressure

Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H2-brine interfacial tension (IFT) is a crucial parameter in structural trapping in underground geological locations and gas-water two-phase flow in subsurface porous media. On the other hand, cushion gas, such as CO2, is often co-injected with H2 to retain reservoir pressure. Therefore, it is imperative to accurately predict the (H2 + CO2)-water/brine IFT under UHS conditions. While there have been a number of experimental measurements on H2-water/brine and (H2 + CO2)-water/brine IFT, an accurate and efficient (H2 + CO2)-water/brine IFT model under UHS conditions is still lacking. In this work, we use molecular dynamics (MD) simulations to generate an extensive (H2 + CO2)-water/brine IFT databank (840 data points) over a wide range of temperature (from 298 to 373 K), pressure (from 50 to 400 bar), gas composition, and brine salinity (up to 3.15 mol/kg) for typical UHS conditions, which is used to develop an accurate and efficient machine learning (ML)-based IFT equation. Our ML-based IFT equation is validated by comparing to available experimental data and other IFT equations for various systems (H2-brine/water, CO2-brine/water, and (H2 + CO2)-brine/water), rendering generally good performance (with R2 = 0.902 against 601 experimental data points). The developed ML-based IFT equation can be readily applied and implemented in reservoir simulations and other UHS applications.

Comprehensive Molecular Dynamics Study of Oxygen Diffusion in Carbon Mesopores: Insights for Designing Fuel-Cell Catalyst Supports

Mesoporous carbon is often used as a support for platinum catalysts in polymer electrolyte fuel-cell catalyst layers. Mesopores in the carbon support improve the performance of fuel cells by inhibiting the adsorption of ionomer onto the catalyst particles. However, the mesopores may impair mass transport. Hence, understanding molecular behaviors in the pores is essential to optimizing the mesopore structures. Specifically, it is crucial to understand the oxygen transport in the high-current region. In this study, the diffusion coefficients of oxygen molecules in carbon mesopores were calculated for various pore lengths, pore diameters, filling rates, and water contents in the ionomer via molecular dynamics simulations. The results show that oxygen diffusion slows by 2 orders of magnitude because of pore occlusion, and it slows down by an additional 1 or 2 orders of magnitude if ionomers are present in the pores. The occlusion can be theoretically predicted by considering the surface free energy. This theory provides some insight into mesoporous carbon designs; for instance, the theory suggests that narrow pores should be shortened to prevent occlusion. Slow diffusion in the presence of ionomers was attributed to the localization of oxygen at the dense ionomer-carbon interface. Thus, to improve oxygen transport properties, carbon surfaces and ionomer structures may be designed in such a manner as to prevent densification at the interface.

Osteocalcin: Promoter or Inhibitor of Hydroxyapatite Growth?

Osteocalcin is the most abundant noncollagenous bone protein and the functions in bone remineralization as well as in inhibition of bone growth have remained unclear. In this contribution, we explain the dual role of osteocalcin in the nucleation of new calcium phosphate during bone remodeling and in the inhibition of hydroxyapatite crystal growth at the molecular scale. The mechanism was derived using pH-resolved all-atom models for the protein, phosphate species, and hydroxyapatite, along with molecular dynamics simulations and experimental and clinical observations. Osteocalcin binds to (hkl) hydroxyapatite surfaces through multiple residues, identified in this work, and the fingerprint of binding residues varies as a function of the (hkl) crystal facet and pH value. On balance, the affinity of osteocalcin to hydroxyapatite slows down crystal growth. The unique tricalcium γ-carboxylglutamic acid (Gla) domain hereby rarely adsorbs to hydroxyapatite surfaces and faces instead toward the solution. The Gla domain enables prenucleation of calcium phosphate for new bone formation at a slightly acidic pH of 5. The growth of prenucleation clusters of calcium phosphate continues upon increase in pH value from 5 to 7 and is much less favorable, or not observed, on the native osteocalcin structure at and above neutral pH values of 7. The results provide mechanistic insight into the early stages of bone remodeling from the molecular scale, help inform mutations of osteocalcin to modify binding to apatites, support drug design, and guide toward potential cures for osteoporosis and hyperosteogeny.

Kallistatin as an inhibitory protein against colorectal cancer cells through binding to LRP6

Kallistatin (KL) is a member of the serine proteinase inhibitor (serpin) family regulating oxidative stress, vascular relaxation, inflammation, angiogenesis, cell proliferation, and invasion. The heparin-binding site of Kallistatin has an important role in the interaction with LRP6 leading to the blockade of the Wnt signaling pathway. In this study, we aimed to explore the structural basis of the Kallistatin-LRP6E1E4 complex using in silico approaches and evaluating the anti-proliferative, apoptotic, and cell cycle arrest activities of Kallistatin in colon cancer lines. The molecular docking showed Kallistatin could bind to the LRP6E3E4 much stronger than LRP6E1E2. The Kallistatin-LRP6E1E2 and Kallistatin-LRP6E3E4 complexes were stable during Molecular Dynamics (MD) simulation. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) showed that the Kallistatin-LRP6E3E4 has a higher binding affinity compared to Kallistatin-LRP6E1E2. Kallistatin induced higher cytotoxicity and apoptosis in HCT116 compared to the SW480 cell line. This protein-induced cell-cycle arrest in both cell lines at the G1 phase. The B-catenin, cyclin D1, and c-Myc expression levels were decreased in response to treatment with Kallistatin in both cell lines while the LRP6 expression level was decreased in the HCT116 cell line. Kallistatin has a greater effect on the HCT116 cell line compared to the SW480 cell line. Kallistatin can be used as a cytotoxic and apoptotic-inducing agent in colorectal cancer cell lines.

Effect of the Temperature on Interfacial Properties of CO2/H2 Mixtures Contacting with Brine and Hydrophilic Silica by Molecular Dynamics Simulations

Underground H2 storage (UHS) is a promising technology to achieve large-scale, long-term H2 storage. Using CO2 as a cushion gas to maintain the pressure of the reservoir and withdraw stored H2 in the saline aquifer simultaneously enables the implementation of UHS and underground CO2 storage (UCS). The difference in the molecular properties of CO2 and H2 leads to distinct interfacial behavior when in contact with the brine and rock, thereby affecting the flow patterns and trapping mechanisms of gases in geological formations. Accurate prediction of the interfacial properties of CO2, H2, and the mixtures when interacting with brine and rock is crucial to minimizing the uncertainties in UHS and UCS projects. In this study, molecular dynamics (MD) simulations are performed to predict the interfacial tension, surface excess, bubble evolution, and contact angle of CO2, H2, and the mixtures at 10 MPa and 300-400 K. The MD results show that the interaction of CO2 with H2O and hydrophilic silica is considerably stronger than that of H2. The interfacial tension reduces linearly with the temperature in H2-dominated mixture systems, and the surface adsorption of H2 can diminish in a CO2-dominated system or at high-temperature conditions. The hydrophilic silica is more CO2-wet than H2-wet, and the attached CO2 bubble is more easily disconnected. Ions and the temperature play different roles in the contact angle.

Accurate Force Fields for Atomistic Simulations of Oxides, Hydroxides, and Organic Hybrid Materials up to the Micrometer Scale

The simulation of metals, oxides, and hydroxides can accelerate the design of therapeutics, alloys, catalysts, cement-based materials, ceramics, bioinspired composites, and glasses. Here we introduce the INTERFACE force field (IFF) and surface models for α-Al2O3, α-Cr2O3, α-Fe2O3, NiO, CaO, MgO, β-Ca(OH)2, β-Mg(OH)2, and β-Ni(OH)2. The force field parameters are nonbonded, including atomic charges for Coulomb interactions, Lennard-Jones (LJ) potentials for van der Waals interactions with 12-6 and 9-6 options, and harmonic bond stretching for hydroxide ions. The models outperform DFT calculations and earlier atomistic models (Pedone, ReaxFF, UFF, CLAYFF) up to 2 orders of magnitude in reliability, compatibility, and interpretability due to a quantitative representation of chemical bonding consistent with other compounds across the periodic table and curated experimental data for validation. The IFF models exhibit average deviations of 0.2% in lattice parameters, <10% in surface energies (to the extent known), and 6% in bulk moduli relative to experiments. The parameters and models can be used with existing parameters for solvents, inorganic compounds, organic compounds, biomolecules, and polymers in IFF, CHARMM, CVFF, AMBER, OPLS-AA, PCFF, and COMPASS, to simulate bulk oxides, hydroxides, electrolyte interfaces, and multiphase, biological, and organic hybrid materials at length scales from atoms to micrometers. The nonbonded character of the models also enables the analysis of mixed oxides, glasses, and certain chemical reactions, and well-performing nonbonded models for silica phases, SiO2, are introduced. Automated model building is available in the CHARMM-GUI Nanomaterial Modeler. We illustrate applications of the models to predict the structure of mixed oxides, and energy barriers of ion migration, as well as binding energies of water and organic molecules in outstanding agreement with experimental data and calculations at the CCSD(T) level. Examples of model building for hydrated, pH-sensitive oxide surfaces to simulate solid-electrolyte interfaces are discussed.

Structural and Electromagnetic Signatures of Anatase and Rutile NTs and Sheets in Three Different Water Models under Different Temperature Conditions

Experimental studies of TiO2 nanotubes have been conducted for nearly three decades and have revealed the remarkable advantages of this material. Research based on computer simulations is much rarer, with research using density functional theory (DFT) being the most significant in this field. It should be noted, however, that this approach has significant limitations when studying the macroscopic properties of nanostructures such as nanosheets and nanotubes. An alternative with great potential has emerged: classical molecular dynamics simulations (MD). MD Simulations offer the possibility to study macroscopic properties such as the density of phonon states (PDOS), power spectra, infrared spectrum, water absorption and others. From this point of view, the present study focuses on the distinction between the phases of anatase and rutile TiO2. The LAMMPS package is used to study both the structural properties by applying the radial distribution function (RDF) and the electromagnetic properties of these phases. Our efforts are focused on exploring the effect of temperature on the vibrational properties of TiO2 anatase nanotubes and an in-depth analysis of how the phononic softening phenomenon affects TiO2 nanostructures to improve the fundamental understanding in different dimensions and morphological configurations. A careful evaluation of the stability of TiO2 nanolamines and nanotubes at different temperatures is performed, as well as the adsorption of water on the nanosurface of TiO2, using three different water models.

Comparison of Eight Classical Lennard-Jones-Based H2 Molecular Models in the Gas Phase at Temperatures and Pressures Relevant to Hydrogen On-Board Storage Tanks

This work compares eight classical H₂ molecular models in the gas phase taken from the existing literature. All models are based on Lennard-Jones (LJ) 12-6 terms for the van der Waals interactions and hence easier to transfer to multiphase molecular simulations than more sophisticated potentials. The H₂ potentials tested include one-site, two-site, three-site, and five-site models, with the sites being either the H atoms, the center-of-mass of the H₂ molecule, or massless sites. For the multisite models, high-frequency H-H stretching modes can lead to poor equipartition of the kinetic energy, and the timestep for molecular dynamics (MD) simulations should be reduced to maintain a stable numerical integration of the equations of motion. As such, only those models with rigid bonds are considered. In the present case, 600 MD simulations of H₂ gas were carried out over a large range of temperatures (-50 to +90 °C) and at densities corresponding to a pressure range of 50 to 2000 bar, which include the operating conditions of on-board storage tanks in hydrogen-fueled vehicles. Most of the models under study were found to reproduce reasonably well the experimental pVT phase diagram as well as the solubility. Discrepancies only became significant at the highest densities tested, and these could be used to rank the different models. All model diffusion coefficients were essentially indistinguishable from experimental results, and as such, kinetically dominated dynamic properties could not be used as a criterion for the choice of model. Among the eight models tested, two of them, i.e., the two-site model of Yang and Zhong and the one-site model derived from Buch performed very well over the range of conditions tested. They represent a good compromise between realism, simplicity, and computational efficiency.

Computational Modeling of the Interaction of Molecular Oxygen with the miniSOG Protein—A Light Induced Source of Singlet Oxygen

Interaction of molecular oxygen 3O2 with the flavin-dependent protein miniSOG after light illumination results in creation of singlet oxygen 1O2 and superoxide O2●−. Despite the recently resolved crystal structures of miniSOG variants, oxygen-binding sites near the flavin chromophore are poorly characterized. We report the results of computational studies of the protein−oxygen systems using molecular dynamics (MD) simulations with force-field interaction potentials and quantum mechanics/molecular mechanics (QM/MM) potentials for the original miniSOG and the mutated protein. We found several oxygen-binding pockets and pointed out possible tunnels bridging the bulk solvent and the isoalloxazine ring of the chromophore. These findings provide an essential step toward understanding photophysical properties of miniSOG—an important singlet oxygen photosensitizer.

Quantum-based Modeling of Protein-ligand Interaction: The Complex of RutA with Uracil and Molecular Oxygen

Modern quantum-based methods are employed to model interaction of the flavin-dependent enzyme RutA with the uracil and oxygen molecules. This complex presents the structure of reactants for the chain of chemical reactions of monooxygenation in the enzyme active site, which is important in drug metabolism. In this case, application of quantum-based approaches is an essential issue, unlike conventional modeling of protein-ligand interaction with force fields using molecular mechanics and classical molecular dynamics methods. We focus on two difficult problems to characterize the structure of reactants in the RutA-FMN-O₂ -uracil complex, where FMN stands for the flavin mononucleotide species. First, location of a small O₂ molecule in the triplet spin state in the protein cavities is required. Second, positions of both ligands, O₂ and uracil, must be specified in the active site with a comparable accuracy. We show that the methods of molecular dynamics with the interaction potentials of quantum mechanics/molecular mechanics theory (QM/MM MD) allow us to characterize this complex and, in addition, to surmise possible reaction mechanism of uracil oxygenation by RutA.

Adsorption and Diffusion of Oxygen on Pure and Partially Oxidized Metal Surfaces in Ultrahigh Resolution

The interaction of gas molecules with metal and oxide surfaces plays a critical role in corrosion, catalysis, sensing, and heterogeneous materials. However, insights into the dynamics of O₂ from picoseconds to microseconds have remained unavailable to date. We obtained 3D potential energy surfaces for adsorption of O₂ on 11 common pristine and partially oxidized (hkl) surfaces of Ni and Al in picometer resolution and high accuracy of 0.1 kcal/mol, identified binding sites, and surface mobility from 25 to 300 °C. We explain relative oxidation rates and parameters for oxide growth. We employed over 150 000 molecular mechanics and molecular dynamics simulations with the interface force field (IFF) using structural data from X-ray diffraction (XRD) and low-energy electron diffraction (LEED). The methods reach 10 to 50 times higher accuracy than possible before and are suited to analyze gas interactions with metals up to the micrometer scale including defects and irregular nanostructures.

Patterning of Self-Assembled Monolayers of Amphiphilic Multisegment Ligands on Nanoparticles and Design Parameters for Protein Interactions

Functionalization of nanoparticles with specific ligands is helpful to control specific diagnostic and therapeutic responses such as protein adsorption, cell targeting, and circulation. Precision delivery critically depends on a fundamental understanding of the interplay between surface chemistry, ligand dynamics, and interaction with the biochemical environment. Due to limited atomic-scale insights into the structure and dynamics of nanoparticle-bound ligands from experiments, relationships of grafting density and ligand chemistry to observable properties such as hydrophilicity and protein interactions remain largely unknown. In this work, we uncover how self-assembled monolayers (SAMs) composed of multisegment ligands such as thioalkyl-PEG-(N-alkyl)amides on gold nanoparticles can mimic mixed hydrophobic and hydrophilic ligand coatings, including control of patterns, hydrophilicity, and specific recognition properties. Our results are derived from molecular dynamics simulations with the INTERFACE-CHARMM36 force field at picometer resolution and comparisons to experiments. Small changes in ligand hydrophobicity, via adjusting the length of the N-terminal alkyl groups, tune water penetration by multiples and control superficial ordering of alkyl chains from 0 to 70% regularity. Further parameters include the grafting density of the ligands, curvature of the nanoparticle surfaces, type of solvent, and overall ligand length, which were examined in detail. We explain the thermodynamic origin of the formation of heterogeneous patterns of multisegment ligand SAMs and illustrate how different degrees of ligand order on the nanoparticle surface affect interactions with bovine serum albumin. The resulting design principles can be applied to a variety of ligand chemistries to customize the behavior of functionalized nanoparticles in biological media and enhance therapeutic efficiency.

Structure-guided affinity maturation of a novel human antibody targeting the SARS-CoV-2 nucleocapsid protein

The continuous mutation of SARS-CoV-2 has presented enormous challenges to global pandemic prevention and control. Recent studies have shown evidence that the genome sequence of SARS-CoV-2 nucleocapsid proteins is relatively conserved, and their biological functions are being confirmed. There is increasing evidence that the N protein will not only provide a specific diagnostic marker but also become an effective treatment target. In this study, 2G4, which specifically recognizes the N protein, was identified by screening a human phage display library. Based on the computer-guided homology modelling and molecular docking method used, the 3-D structures for the 2G4 scFv fragment (VH-linker-VL structure, with (G4S)3 as the linker peptide in the model), SARS-CoV-2 N protein and its complex were modelled and optimized with a suitable force field. The binding mode and key residues of the 2G4 and N protein interaction were predicted, and three mutant antibodies (named 2G4-M1, 2G4-M2 and 2G4-M3) with higher affinity were designed theoretically. Using directed point mutant technology, the three mutant antibodies were prepared, and their affinity was tested. Their affinity constants of approximately 0.19 nM (2G4-M1), 0.019 nM (2G4-M2) and 0.075 nM (2G4-M3) were at least one order of magnitude lower than that of the parent antibody (3 nM; 2G4, parent antibody), as determined using a biolayer interferometry (BLI) assay. It is expected that high-affinity candidates will be used for diagnosis and even as potential therapeutic drugs for the SARS-CoV-2 pandemic.

The behavior of zeolites wairakite and phillipsite at high P-T parameters

In situ investigation of mineral behavior in water medium at simultaneously high P-T parameters can be applied to modelling of mineral transformation processes in lithospheric plates. The behavior of zeolites wairakite and phillipsite under the P-T conditions of «cold» slab subduction, corresponding to the start of oceanic plate diving or ocean floor near geothermal sources, was studied by in situ Raman spectroscopy. During compression in water medium, phillipsite initial phase is stable up to T = 350 °C, P = 1.7 GPa and with further increase of P-T parameters, phillipsite undergoes amorphization and partially dissolves in water. Wairakite compressed in water medium has a polymorphic transformation at T ≈ 300 °C and P ≈ 0.4 GPa. At 300-450 °C and P = 1 GPa the Raman spectrum almost disappears due to the amorphization of wairakite. Zeolite wairakite partially dissolves, and other zeolite phillipsite grows out of the fluid at T = 450 °C and P = 1 GPa. This transformation indicates the higher stability of phillipsite in comparison to wairakite. The in situ observed high P-T stability of phillipsite, which does not transform to other zeolites, and its formation from wairakite may indicate ф possible widespread distribution of this zeolite in marine sediments. By using the plane-wave pseudo-potential method, ab initio DFT calculations of Raman and FTIR spectra of wairakite were carried out. Comparing theoretical and experimental spectra, interpretation of the vibrational spectra of both zeolites was suggested.