Potential Molecular Dynamics Research Articles

Mechanism-Guided Computational Design Drives meso-Diaminopimelate Dehydrogenase to Efficient Synthesis of Aromatic d-amino Acids.

Aromatic d-amino acids (d-AAs) play a pivotal role as important chiral building blocks and key intermediates in fine chemical and drug synthesis. Meso-diaminopimelate dehydrogenase (DAPDH) serves as an excellent biocatalyst in the synthesis of d-AAs and their derivatives. However, its strict substrate specificity and the lack of efficient engineering methods have hindered its widespread application. Therefore, this study aims to elucidate the catalytic mechanism underlying DAPDH from Proteus vulgaris (PvDAPDH) through the examination of its crystallographic structure, computational simulations of potential energies and molecular dynamics simulations, and site-directed mutagenesis. Mechanism-guided computational design showed that the optimal mutant PvDAPDH-M3 increased specific activity and catalytic efficiency (kcat/Km) for aromatic keto acids up to 124-fold and 92.4-fold, respectively, compared to that of the wild type. Additionally, it expanded the substrate scope to 10 aromatic keto acid substrates. Finally, six high-value-added aromatic d-AAs and their derivatives were synthesized using a one-pot three-enzyme cascade reaction, exhibiting a good conversion rate ranging from 32 to 84% and excellent stereoselectivity (enantiomeric excess >99%). These findings provide a potential synthetic pathway for the green industrial production of aromatic d-AAs.

Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations.

Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2 slit-pores. We find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.

Temperature-dependence of beam-driven dynamics in graphene-fullerene sandwiches

C60 fullerenes encapsulated between graphene sheets were investigated by aberration-corrected high-resolution transmission electron microscopy at different temperatures, namely about 93 K, 293 K and 733 K, and by molecular dynamics simulations. We studied beam-induced dynamics of the C60 fullerenes and the encapsulating graphene, measured the critical doses for the initial damage to the fullerenes and followed the beam-induced polymerization. We find that, while the doses for the initial damage do not strongly depend on temperature, the clusters formed by the subsequent polymerization are more tubular at lower temperatures, while sheet-like structures are generated at higher temperatures. The experimental findings are supported by the results of first-principles and analytical potential molecular dynamics simulations. The merging of curved carbon sheets is clearly promoted at higher temperatures and proceeds at once over few-nm segments.

Interfacial Optimization for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dynamics Investigation of the Mechanical Properties.

AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, the poor adhesion at the AlN/diamond interface is a significant challenge that will lead to film delamination and device performance degradation. In this study, the uniaxial tensile failure of the AlN/diamond heterogeneous interfaces was investigated by molecular dynamics simulations based on a neuroevolutionary machine learning potential (NEP) model. The interatomic interactions can be successfully described by trained NEP, the reliability of which has been demonstrated by the prediction of the cleavage planes of AlN and diamond. It can be revealed that the annealing treatment can reduce the total potential energy by enhancing the binding of the C and N atoms at interfaces. The strain engineering of AlN also has an important impact on the mechanical properties of the interface. Furthermore, the influence of the surface roughness and interfacial nanostructures on the AlN/diamond heterostructures has been considered. It can be indicated that the combination of surface roughness reduction, AlN strain engineering, and annealing treatment can effectively result in superior and more stable interfacial mechanical properties, which can provide a promising solution to the optimization of mechanical properties, of ultrawide band gap semiconductor heterostructures.

Competing Reaction Mechanisms of Peptide Bond Formation in Water Revealed by Deep Potential Molecular Dynamics and Path Sampling.

The formation of an amide bond is an essential step in the synthesis of materials and drugs, and in the assembly of amino acids to form peptides. The mechanism of this reaction has been studied extensively, in particular to understand how it can be catalyzed, but a representation capable of explaining all the experimental data is still lacking. Numerical simulation should provide the necessary molecular description, but the solvent involvement poses a number of challenges. Here, we combine the efficiency and accuracy of neural network potential-based reactive molecular dynamics with the extensive and unbiased exploration of reaction pathways provided by transition path sampling. Using microsecond-scale simulations at the density functional theory level, we show that this method reveals the presence of two competing distinct mechanisms for peptide bond formation between alanine esters in aqueous solution. We describe how both reaction pathways, via a general base catalysis mechanism and via direct cleavage of the tetrahedral intermediate respectively, change with pH. This result contrasts with the conventional mechanism involving a single pathway in which only the barrier heights are affected by pH. We show that this new proposal involving two competing mechanisms is consistent with the experimental data, and we discuss the implications for peptide bond formation under prebiotic conditions and in the ribosome. Our work shows that integrating deep potential molecular dynamics with path sampling provides a powerful approach for exploring complex chemical mechanisms.

Double-Layer Distribution of Hydronium and Hydroxide Ions in the Air-Water Interface.

The acid-base nature of the aqueous interface has long been controversial. Most macroscopic experiments suggest that the air-water interface is basic based on the detection of negative charges at the interface that indicates the enrichment of hydroxides (OH-), whereas microscopic studies mostly support the acidic air-water interface with the observation of hydronium (H3O+) accumulation in the top layer of the interface. It is crucial to clarify the interfacial preference of OH- and H3O+ ions for rationalizing the debate. In this work, we perform deep potential molecular dynamics simulations to investigate the preferential distribution of OH- and H3O+ ions at the aqueous interfaces. The neural network potential energy surface is trained based on density functional theory calculations with the SCAN functional, which can accurately describe the diffusion of these two ions both in the interface and in the bulk water. In contrast to the previously reported single ion enrichment, we show that both OH- and H3O+ surprisingly prefer to accumulate in interfaces but at different interfacial depths, rendering a double-layer ionic distribution within ∼1 nm near the Gibbs dividing surface. The H3O+ preferentially resides in the topmost layer of the interface, but the OH-, which is enriched in the deeper interfacial layer, has a higher equilibrium concentration due to the more negative free energy of interfacial stabilization [-0.90 (OH-) vs -0.56 (H3O+) kcal/mol]. The present finding of the ionic double-layer distribution may qualitatively offer a self-consistent explanation for the long-term controversy about the acid-base nature of the air-water interface.

Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model

Nanoporous carbon-based supercapacitors are established energy storage devices, that complement Li-ion batteries in high-power applications. So far, the study of these systems through simulations required drastic simplification of the electrolyte and/or of the electrode. In this work, we use state-of-the-art constant potential classical molecular dynamics simulations, where both the electrode and the electrolyte are polarizable, to study complex carbide-derived carbons with 1-ethyl-3-methylimidazolium bis(trifluoromethane)sulfonyl imide ionic liquid electrolyte. We show that using this set-up allows to obtain integral capacitances (i.e. the main property to characterize supercapacitors) in quantitative agreement with experiments. The simulations, performed in a range of applied potentials of 1V to 4V, then provide microscopic details on the charging mechanism, revealing that it is not symmetrical between the positive and negative electrodes. A pure exchange of ions is observed on the positive electrode, while an ion exchange combined with cation adsorption takes place on the negative one. Our work shows that accounting for the coupling of electrode and electrolyte polarization improves the accuracy of the results, opening the way toward a more quantitative comparison with experiments.

Experimental and theoretical investigation of novel ammonium-derived dihydroxyl ionic liquid as corrosion inhibitor for mild steel in 1 M HCl: Effects of dihydroxyl and head groups

A novel ammonium-derived dihydroxyl ionic liquid ([C16DPOA]Br) and a control ionic liquid ([C16DPA]Br) are synthesized to reveal the effect of dihydroxyl and head groups on their inhibition property via the weight-loss method, surface analysis, electrochemical measurements, and adsorption isotherm studies. [C16DPOA]Br exhibits the optimal inhibition ability (93.47 %) for mild steel in 1 M HCl in contrast to [C16DPA]Br (88.03 %) and the traditional cationic surfactant CTAB (92.34 %). Based on the experimental results, it can be inferred that the short alkyl chain (propyl) impedes the tight alignment of [C16DPA]Br on the metal surface and the interactions between hydroxyl and iron atoms enable [C16DPOA]Br to overcome the negative effects of the short alkyl chain on the inhibition property. Finally, molecular electrostatic potential (MEP) distribution and molecular dynamics (MD) simulation verify the reactive sites and adsorption capacity of corrosion inhibitors from the microscopic scale, which further confirms the proposed mechanism. This study focuses on the significance of dihydroxyl and head groups on the inhibition property and provides unique insights into the development of efficient ionic liquid corrosion inhibitors.

Investigation of the Degradation of LiPF6- in Polar Solvents through Deep Potential Molecular Dynamics.

The nonaqueous electrolyte based on lithium hexafluorophosphate (LiPF6) is the dominant liquid electrolyte in lithium-ion batteries (LIBs). However, trace protic impurities, including H3O+, alcohols, and hydrofluoric acid (HF), can trigger a series of side reactions that lead to rapid capacity fading in high energy density LIBs. It is worth noting that this degradation process is highly dependent on the polarity of the solvents. In this work, a deep potential (DP) model is trained with a certain commercial electrolyte formula through a machine learning method. H3O+ is anchored with polar solvents, making it difficult to approach the PF6-, and suppressing the degradation process quickly at room temperature. Control experiments and simulations at different temperatures or concentrations are also performed to verify it. This work proposes a precise model to describe the solvation structure quantitatively and offers a new perspective on the degradation mechanism of PF6- in polar solvents.

Theoretical Study on Ion Diffusion Mechanism in W-Doped K3SbS4 as Solid-State Electrolyte for K-Ion Batteries.

The development of a solid-state electrolyte (SSE) is crucial for overcoming the side reactions of metal potassium anodes and advancing the progress of K-ion batteries (KIBs). Exploring the diffusion mechanism of the K ion in SSE is important for deepening our understanding and promoting its development. In this study, we conducted static calculations and utilized deep potential molecular dynamics (DeepMD) to investigate the behavior of cubic K3SbS4. The original K3SbS4 exhibited poor ionic conductivity, but we discovered that introducing heterovalent tungsten doping created vacancies, which significantly reduced the activation energy to 0.12 eV and enhanced the ionic conductivity to 1.80 × 10-2 S/cm. The diffusion of K-ions in K3SbS4 primarily occurs through the exchange of positions with K vacancies. This research provides insights into the design of SSE with high ionic conductivity. Furthermore, it highlights the effectiveness of DeepMD as a powerful tool for studying the SSE.

Ab initio generalized Langevin equation

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

Comprehensive Analysis of Coupled Proline Cis-Trans States in Bradykinin Using ωBP-REMD Simulations.

It is well-known that proline (Pro) cis-trans isomerization plays a decisive role in the folding and stabilization of proteins. The conformational coupling between isomerization states of different Pro residues in proteins during conformational adaptation processes is not well understood. In the present work, we investigate the coupled cis-trans isomerization of three Pro residues using bradykinin (BK), a partially unstructured nonapeptide hormone, as a model system. We use a recently developed enhanced-sampling molecular dynamics method (ω-bias potential replica exchange molecular dynamics; ωBP-REMD) that allows us to exhaustively sample all combinations of Pro isomer states and obtain converged probability densities of all eight state combinations within 885 ns ωBP-REMD simulations. In agreement with experiment, the all-trans state is seen to be the preferred isomer of zwitterionic aqueous BK. In about a third of its structures, this state presents the characteristic C-terminal β-turn conformation; however, other isomer combinations also contribute significantly to the structural ensemble. Unbiased probabilities can be projected onto the peptide bond dihedral angles of the three Pro residues. This unveils the interdependence of the individual Pro isomerization states, i.e., a possible coupling of the different Pro isomers. The cis/trans equilibrium of a Pro residue can change by up to 2.5 kcal·mol-1, depending on the isomerization state of other Pro residues. For example, for Pro7, the simulations indicate that its cis state becomes favored compared to its trans state when Pro2 is switched from the trans state to the cis state. Our findings demonstrate the efficiency of the ωBP-REMD methodology and suggest that the coupling of Pro isomerization states may play an even more decisive role in larger folded proteins subject to more conformational restraints.

S.O.S: Shape, orientation, and size tune solvation in electrocatalysis.

Current models to understand the reactivity of metal/aqueous interfaces in electrochemistry, e.g., volcano plots, are based on the adsorption free energies of reactants and products, which are often small hydrophobic molecules (such as in CO2 and N2 reduction). Calculations played a major role in the quantification and comprehension of these free energies in terms of the interactions that the reactive species form with the surface. However, solvation free energies also come into play in two ways: (i) by modulating the adsorption free energy together with solute-surface interactions, as the solute has to penetrate the water adlayer in contact with the surface and get partially desolvated (which costs free energy); (ii) by regulating transport across the interface, i.e., the free energy profile from the bulk to the interface, which is strongly non-monotonic due to the unique nature of metal/aqueous interfaces. Here, we use constant potential molecular dynamics to study the solvation contributions, and we uncover huge effects of the shape and orientation (on top of the already known size effect) of small hydrophobic and amphiphilic solutes on their adsorption free energy. We propose a minimal theoretical model, the S.O.S. model, that accounts for size, orientation, and shape effects. These novel aspects are rationalized by recasting the concepts at the base of the Lum-Chandler-Weeks theory of hydrophobic solvation (for small solutes in the so-called volume-dominated regime) into a layer-by-layer form, where the properties of each interfacial region close to the metal are explicitly taken into account.

Unveiling the Morphology of Carbon-Supported Ru Nanoparticles by Multiscale Modeling.

Simulating the behavior of metal nanoparticles on supports is crucial for boosting their catalytic performance and various nanotechnology applications; however, such simulations are limited by the conflicts between accuracy and efficiency. Herein, we introduce a multiscale modeling strategy to unveil the morphology of Ru supported on pristine and N-doped graphene. Our multiscale modeling started with the electronic structures of a supported Ru single atom, revealing the strong metal-support interaction around pyridinic nitrogen sites. To determine the stable configurations of Ru2-13 clusters on three different graphene supports, global energy minimum searches were performed. The sintering of the global minimum Ru13 clusters on supports was further simulated by ab initio molecular dynamics (AIMD). The AIMD data set was then collected for deep potential molecular dynamics to study the melting of Ru nanoparticles. This study presents comprehensive descriptions of carbon-supported Ru and develops modeling approaches that bridge different scales and can be applied to various supported nanoparticle systems.

Accelerating the Reaction Kinetics of CO2 Reduction to Multi-Carbon Products by Synergistic Effect between Cation and Aprotic Solvent on Copper Electrodes.

Improving the selectivity of electrochemical CO2 reduction to multi-carbon products (C2+ ) is an important and highly challenging topic. In this work, we propose and validate an effective strategy to improve C2+ selectivity on Cu electrodes, by introducing a synergistic effect between cation (Na+ ) and aprotic solvent (DMSO) to the electrolyte. Based on constant potential ab initio molecular dynamics simulations, we first revealed that Na+ facilitates C-C coupling while inhibits CH3 OH/CH4 products via reducing the water network connectivity near the electrode. Furthermore, the water network connectivity was further decreased by introducing an aprotic solvent DMSO, leading to suppression of both C1 production and hydrogen evolution reaction with minimal effect on *OCCO* hydrogenation. The synergistic effect enhancing C2 selectivity was also experimentally verified through electrochemical measurements. The results showed that the Faradaic efficiency of C2 increases from 9.3 % to 57 % at 50 mA/cm2 under a mixed electrolyte of NaHCO3 and DMSO compared to a pure NaHCO3 , which can significantly enhance the selectivity of the C2 product. Therefore, our discovery provides an effective electrolyte-based strategy for tuning CO2 RR selectivity through modulating the microenvironment at the electrode-electrolyte interface.

Influence of functional groups on the microporous development and CO2-CH4 adsorption: A molecular simulation investigation

In this study, the influence of different functional groups (carbonyl, ether, carboxyl, and hydroxyl) on microporous development and competitive adsorption for CO2-CH4 was investigated using Connolly potential theory, molecular mechanics, molecular dynamics, and grand canonical Monte Carlo based on the macromolecular representation of coal vitrinite (CV). Results indicated that microporous diameter induced from the carboxyl (COOH) and hydroxyl (OH) was more sensitive to increasing temperature than carbonyl (CO) and ether (C2O). The COOH and OH could induce more micropores than the other functional groups. The adsorption capacities of OH-CV and COOH-CV were significantly higher than C2O-CV, CO-CV, and C-CV (coal vitrinite purified by carbon atoms) and they all decrease with the decreasing temperature. Both the calculation results of Locate- and Isotherm task suggested that OH and COOH have a significantly higher adsorption amount than other coal macromolecules and the others have a close adsorption amount. The adsorption selectivities of CO2 over CH4 ( SA/B) for all the functional groups were higher than 1 here, indicating the adsorption preference of CO2 over CH4. For the pressure dependence, the SA/B first decreases significantly for the pressure of < 4 MPa and then slightly for the pressures of >4 MPa with the increasing pressure, indicating that high pressure was not conductive to the replacement of CH4 by CO2. For almost all the pressures here, the SA/B follows the order of COOH-CV > OH-CV≈C2O-CV≈Ori-CV≈CO-CV≈C-CV, indicating the highest adsorption preference of CO2 over CH4 for COOH, and OH-CV.

Accelerating the Reaction Kinetics of CO2 Reduction to Multi‐Carbon Products by Synergistic Effect between Cation and Aprotic Solvent on Copper Electrodes

AbstractImproving the selectivity of electrochemical CO2 reduction to multi‐carbon products (C2+) is an important and highly challenging topic. In this work, we propose and validate an effective strategy to improve C2+ selectivity on Cu electrodes, by introducing a synergistic effect between cation (Na+) and aprotic solvent (DMSO) to the electrolyte. Based on constant potential ab initio molecular dynamics simulations, we first revealed that Na+ facilitates C−C coupling while inhibits CH3OH/CH4 products via reducing the water network connectivity near the electrode. Furthermore, the water network connectivity was further decreased by introducing an aprotic solvent DMSO, leading to suppression of both C1 production and hydrogen evolution reaction with minimal effect on *OCCO* hydrogenation. The synergistic effect enhancing C2 selectivity was also experimentally verified through electrochemical measurements. The results showed that the Faradaic efficiency of C2 increases from 9.3 % to 57 % at 50 mA/cm2 under a mixed electrolyte of NaHCO3 and DMSO compared to a pure NaHCO3, which can significantly enhance the selectivity of the C2 product. Therefore, our discovery provides an effective electrolyte‐based strategy for tuning CO2RR selectivity through modulating the microenvironment at the electrode‐electrolyte interface.

Unravelling abnormal in-plane stretchability of two-dimensional metal-organic frameworks by machine learning potential molecular dynamics.

Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed a highly stable NiF2(pyrazine)2 2D MOF in silico with a two-dimensional periodic wine-rack architecture. Extensive first-principles calculations and molecular dynamics (MD) simulations based on a newly developed machine learning potential (MLP) revealed that this 2D MOF exhibits huge in-plane Poisson's ratio anisotropy. This results in anomalous negative in-plane stretchability, as evidenced by an uncommon decrease in its in-plane area upon the application of uniaxial tensile strain, which makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications. We further demonstrated the unique capability of MLP to accurately predict the finite-temperature properties of MOFs on a large scale, exemplified by MLP-MD simulations with a dimension of 28.2 × 28.2 nm2, relevant to the length scale experimentally attainable for the fabrication of MOF films.

Properties of aqueous electrolyte solutions at carbon electrodes: effects of concentration and surface charge on solution structure, ion clustering and thermodynamics in the electric double layer.

Surfaces are able to control physical-chemical processes in multi-component solution systems and, as such, find application in a wide range of technological devices. Understanding the structure, dynamics and thermodynamics of non-ideal solutions at surfaces, however, is particularly challenging. Here, we use Constant Chemical Potential Molecular Dynamics (CμMD) simulations to gain insight into aqueous NaCl solutions in contact with graphite surfaces at high concentrations and under the effect of applied surface charges: conditions where mean-field theories describing interfaces cannot (typically) be reliably applied. We discover an asymmetric effect of surface charge on the electric double layer structure and resulting thermodynamic properties, which can be explained by considering the affinity of the surface for cations and anions and the cooperative adsorption of ions that occurs at higher concentrations. We characterise how the sign of the surface charge affects ion densities and water structure in the double layer and how the capacitance of the interface-a function of the electric potential drop across the double layer-is largely insensitive to the bulk solution concentration. Notably, we find that negatively charged graphite surfaces induce an increase in the size and concentration of extended liquid-like ion clusters confined to the double layer. Finally, we discuss how concentration and surface charge affect the activity coefficients of ions and water at the interface, demonstrating how electric fields in this region should be explicitly considered when characterising the thermodynamics of both solute and solvent at the solid/liquid interface.

Study of the solvent-dependent crystal shape of theophylline using constant chemical potential molecular dynamics simulations.

The crystal habit of an organic crystalline material impacts its properties, processing, and performance, especially in pharmaceuticals. In solution crystallization, solvents play a crucial role in modulating the crystal habits by interacting with the different growing facets - either enhancing or inhibiting the growth of specific facets. Thus, an in-depth understanding of the role of the solvent in crystal shape selection is of paramount importance for the design and growth of crystals. In this work, we used constant chemical potential molecular dynamics (CμMD) to simulate the growth of theophylline crystals in solvents with decreasing polarity, i.e. water, isopropyl alcohol, and dimethylformamide. Our results indicate that as the polarity of the solvent increases, theophylline crystallizes into rod-shaped crystals; the aspect ratio is modulated by the relative growth of the (001) and (010) facets. Furthermore, solvent profile analyses revealed that the desolvation of the crystal facets plays a major role in the growth process.