Exploring the effects of surface geometry, chassis flexibility, and wheel configuration on nanocar mobility: A molecular dynamic study for efficient molecular transport and manipulation

In the theory of open quantum systems, spectral densities are key quantities for modeling the dynamics and spectroscopic properties of the system under investigation. In the case of light-harvesting complexes, they encode the frequency-dependent coupling of electronic excitations in pigment molecules to their environment, reflecting contributions from both intrinsic vibrational modes and the protein surrounding. In particular, the low-frequency components of the spectral densities are crucial for exciton transfer between pigment molecules. Apparently, slow internal modes of bacteriocholophyll molecules in the gas phase are less well represented by common force fields based on classical molecular dynamics simulations. Here, we demonstrate that Born-Oppenheimer molecular dynamics (BOMD) based on the numerically efficient density functional-based tight-binding approach can accurately recover these low-frequency features, whereas normal mode analysis captures them only partially. In contrasting approaches for determining spectral densities, the low-frequency region of the spectral densities obtained is only associated with protein fluctuations; the usage of BOMD, however, also captures the low-frequency contributions arising from slow intramolecular vibrations of the pigment molecules themselves. Notably, this behavior is consistently observed for both the flexible B800 and the more rigid B850 rings in light-harvesting 2 (LH2) complexes of purple bacteria, as well as in the Fenna-Matthews-Olson complex of green sulfur bacteria. Interestingly, we also find that the spectral densities of the pigments in the B850 ring of LH2 are not influenced by the environment, i.e., the gaps between the ground and first excited states are not changed significantly by the fluctuations of the protein environment.

Nanoplastics (NPLs), generated through widespread plastic use and subsequent degradation, present emerging risks to the environment and human health due to their capacity to interact with biomolecular systems and affect the structure and function of biological molecules. Investigating the interactions between NPLs and biomolecules remains challenging due to the limitations of experimental techniques in characterizing nanoparticles in diverse biological environments, as well as the complexity and variability of NPL chemistries and properties. Physics based computational simulations employing quantum mechanical and/or classical molecular dynamics methods offer atomically resolved insights into NPL-biomolecule interactions in physiologically relevant environments not yet achievable empirically. This perspective summarizes recent simulation studies aiming to elucidate the mechanisms by which NPLs interact with proteins, nucleic acids, lipid membranes, and pharmaceutical drug molecules and develop strategies to mitigate the impacts of plastic pollution.

The integration of machine learning (ML) with molecular dynamics (MD) significantly enhances the design of nanoparticles (NPs) across four key areas: synthesis optimization, advanced characterization, ML-based force fields (MLFFs), and property prediction using surrogate models. This review focuses on discrete NPs, excluding extended NPs. Traditional MD FFs often overlook essential polarization and many-body effects, while quantum methods are impractical for larger systems. In contrast, MLFFs bridge the gap between accuracy and scalability, achieving near-DFT precision for trained NP systems at computational costs approaching those of classical MD. Recent studies indicate that ML characterization can provide high morphological accuracy based on experimental imaging, and MLFFs demonstrate a close alignment with quantum reference data. However, challenges like data scarcity, model transferability, and interpretability call for collaborative efforts within the community, including the establishment of standardized benchmarks and open model repositories. This cohesive ML-MD approach enables computationally guided NP discovery for a range of applications in catalysis, energy storage, and biomedicine.

Under confinement, water changes its structure and dynamics, displaying new properties with respect to the bulk. We studied water confined in naphthalene diimide (NDI)-based molecular crystals via classical molecular dynamics simulations. We examined NDIs functionalized with either hydrophilic linear triethylene glycol side chains (NDI-TEG) or hydrophobic n-hexyl chains (NDI-C6), increasing the hydration (i.e., amount of water molecules within each crystal) up to a 1:3 NDI:water molar ratio. Static and dynamical properties of confined water are compared to those of room-temperature and supercooled (200K) bulk water, which serve as references for liquid and glassy states, respectively. The impact of confinement is analyzed through structural order parameters, time-dependent correlation functions, and hydrogen-bond (HB) analyses. At low hydration (≈1:1 NDI:water), confined water assumes a structural order that is far from the tetrahedral one, showing different spatial organizations when inserted within hydrophilic NDI-TEG or hydrophobic NDI-C6 crystals. By increasing the hydration level (≈1:3 NDI:water), the structure of confined water clusters shifts toward a more bulk-like (liquid) arrangement, while the chemical nature of the NDI side chains plays a marginal role. Notably, fast (sub-ps) and slow (hundreds of ns) water dynamics are not much influenced by the hydrophilicity/hydrophobicity of the side chains but rather by confinement effects. The analysis of the HB network autocorrelation functions highlights how finite-size effects and restricted connectivity are the main factors controlling HB dynamics. Our study paves the way toward an atomistic understanding of both structural and dynamical functions of water confined in molecular crystals, opening new paths for the rationalization of transport phenomena in the emerging class of organic mixed ionic-electronic conductors.

Molecular dynamics (MD) simulations are a widely applied tool to investigate systems of varying complexity, from isolated molecules to biomolecules consisting of many thousands of atoms. Extracting mechanistic insights from the high-dimensional data sets frequently presents a challenge. Here, we introduce the publicly available MOlecules aNd Internal Cluster Analysis of Molecular Dynamics simulations (MonicaMD) program package, a versatile and efficient tool that targets the analysis of molecules and molecular clusters in atomistic classical and semiclassical trajectories. MonicaMD provides modular access to structural information, with a focus on internal and collective variables. A further key functionality is the extraction of electrostatic information. MonicaMD offers a user-friendly workflow including dimensionality reduction, automatic feature-space generation, and a templating functionality for generated grids in order to be readily used in conjunction with quantum chemical software and machine learning frameworks. The functionality of MonicaMD offers the user a convenient and efficient bridge between classical MD and higher-accuracy quantum mechanics simulations. This synergy enabled by MonicaMD is demonstrated by the investigation of conformational analysis in a protein-ligand complex, structural and electrostatic effects of DNA intercalation, and the excited-state isomerization of a photoswitch. Additional examples include reactive coordinates of a transition-metal-catalyzed C-N coupling reaction and of the light-initiated generation of free diazoalkane, as well as an analysis of chlorophyll binding sites in a photosynthetic complex.

It has long been suggested that organic solvents disrupt the solvation shell around phosphate ions, which might facilitate calcium phosphate (CaP) nucleation. This would explain recent experimental findings where organic solvents─ethanol, isopropanol, and acetone─produce a denser CaP coating when synthesized on marble for conservation applications. In this work, computational methods are used to investigate the solvation shell of phosphate ions in mixed organic-aqueous solutions. Relative one-to-one interaction energies between the phosphates and solvents were calculated with density functional theory (DFT) and suggest that ethanol and isopropanol could displace water in the hydration sphere of PO43-, HPO42-, and H2PO4-. Classical molecular dynamics simulations with models benchmarked to these DFT interaction energies were then used to investigate solvation under bulk solvent conditions. In mixed organic-aqueous conditions, we find that the behavior in the phosphate solvation shell is dependent on the solvent and protonation state. More specifically, none of the three organic solvents consistently disrupts the hydration shell of the phosphates, which would correlate with the experimental findings. Ultimately, this suggests that the influence of the organic solvent on the solvation shell of the phosphate ions may not contribute significantly to the improved synthesis of CaP.

Biodiesel has emerged as a sustainable and viable alternative to fossil fuels to meet the growing global energy demand. However, crude biodiesel produces glycerol as a major byproduct, which adversely affects its quality and engine performance. Additionally, the combustion of biodiesel in the presence of glycerol emits harmful pollutants, making the effective removal of glycerol a critical step in biodiesel purification. Liquid-liquid extraction using conventional organic solvents has been explored for glycerol removal from biodiesel, but its applicability is limited by high volatility, flammability, poor selectivity, and losses of biodiesel, which compromise process safety and energy efficiency. Consequently, recent attention has shifted toward sustainable solvents, including deep eutectic solvents, which offer low volatility, reusability, and improved separation performance. In this study, deep eutectic solvents are explored as promising extraction solvents for the selective removal of glycerol from biodiesel. In this study, classical molecular dynamics simulations were performed with the GROMACS package and the OLPS-AA force field to investigate the molecular interactions governing glycerol extraction from biodiesel using deep eutectic solvents. The DESs studied included choline chloride:urea (1:2), choline chloride:ethylene glycol (1:2), and choline chloride:ethylene glycol (1:3). Structural and dynamical properties were analyzed using radial distribution functions, hydrogen-bond analysis, and density profiles to quantify intermolecular interactions and preferential solvation behavior. The molecular-level insights obtained from these simulations were used to assess the affinity of deep eutectic solvents for glycerol by determining the glycerol distribution coefficient between the DES-rich and biodiesel-rich phases.

Nanoconfined fluid is central to many engineering applications such as shale energy production, carbon sequestration, and molecular separations. While classical molecular dynamics (MD) simulation provides essential atomistic detail, its prohibitive computational cost severely limits accessible time and length scales. Hybrid MD-Monte Carlo (MDMC) methods accelerate sampling but lack generality beyond their trained conditions. In this work, we introduce an AI-assisted MDMC framework that overcomes this limitation by learning local, conditional transition statistics directly from MD trajectories. Our method encodes molecular motion into a compact set of neural network-predicted displacement actions, preserving MD-level accuracy within a drastically reduced dimensionality. This approach enables efficient sampling with robust generality. We systematically demonstrate the framework's accuracy and transferability across diverse thermodynamic conditions (temperature, pressure), spatial scales, and complex nano-scale geometries, establishing a versatile path for simulating confined fluid phenomena relevant to engineering applications.

Understanding how dioxygen accesses buried catalytic centers in metalloenzymes is critical for elucidating enzymatic kinetics and guiding strategies to modulate catalytic activity. Here, we report over 20 μs of classical molecular dynamics simulations of the PHD2 oxygenase, a metalloenzyme regulating hypoxia signaling via HIF-1α hydroxylation. Our extended simulations reveal multiple dynamic dioxygen transport routes from solvent-exposed regions through the cupin fold to the metal active site, capturing transient interconverting channels and kinetic heterogeneity inaccessible to prior short-time scale studies. Dioxygen transport occurs on widely differing time scales, from rapid exchange (∼250 ps) to long residence times within internal hydrophobic cavities lasting hundreds of nanoseconds. These internal cavities act as dynamic reservoirs, modulating dioxygen availability and potentially contributing to the high Km and slow oxidative turnover by PHD2. Analysis of cavity-lining residues identifies hydrophobic positions that may be targeted to tune catalytic rates. Collectively, our results refine the mechanistic model of dioxygen access in PHD2 and demonstrate how high-resolution simulations can uncover functionally relevant kinetic landscapes, providing principles applicable to the design and regulation of molecular catalysts.

ABSTRACT The atomic mechanism of the melting process of the hexagonal GaN monolayer is studied using a comprehensive MD simulation. Three cases are considered: the free-standing GaN, zigzag GaN nanoribbon (ZGaNNR) and armchair GaN nanoribbon (AGaNNR). The interactions between Ga and N are based on the Stillinger-Weber potential. Because the armchair (or zigzag) edge of the GaN monolayer is fixed to create ZGaNNR (or AGaNNR), leading to the boundary effects on the melting process. Some results are below: The free-standing GaN, the AGaNNR and the ZGaNNR exhibit a first-order-like melting transition. The melting point of the free-standing GaN (3790 K) is lower than that of AGaNNR (4120 K) and ZGaNNR (4200 K), respectively. The threshold value of the Lindemann criterion of the free-standing GaN is about 0.092, while the ones of the AGaNNR and the ZGaNNR are 0.134 and 0.1, respectively.

This study investigates the influence of boron (B) doping on the electrical and thermal transport properties of double-walled carbon nanotubes (DWCNT) with chiral indices (8,0) @ (17,0) over a wide temperature range. Boron incorporation modulates the partial charge distribution, enhancing p-type semiconducting behavior at low doping concentrations, while higher doping levels induce substitutional disorder and defect formation, leading to reduced electrical conductivity. Thermal transport is also affected, as defect-induced phonon scattering and mass-difference effects suppress phonon propagation at elevated doping levels. The results highlight the critical role of both dopant concentration and temperature in controlling charge redistribution, phonon scattering, and overall transport efficiency in DWCNT. All simulations were performed using classical molecular dynamics (MD) techniques. Double-walled carbon nanotube structures with chiral indices (8,0)@(17,0) were constructed and doped with boron at concentrations ranging from 0 to 9.65%. Partial atomic charges were analyzed to study charge redistribution, and non-equilibrium MD simulations were employed to compute thermal conductivity. Temperature-dependent behavior was evaluated by performing simulations across a broad temperature range. The interactions between carbon and boron atoms were modeled using validated force fields suitable for covalent systems, and phonon scattering effects were analyzed to quantify the impact of doping on thermal transport.

Salicylate 1,2-dioxygenase (SDO), a ring-fission nonheme dioxygenase enzyme, catalyzes the regioselective oxidation of gentisic acid (GTQ) and salicylic acid (SAL) in the presence of molecular oxygen. Wild-type SDO from Pseudaminobacter salicylatoxidans exhibits higher catalytic efficiency with GTQ compared to SAL. To elucidate the factors underlying this difference, classical molecular dynamics simulations were performed on wild-type SDO complexed with GTQ and SAL. The simulations revealed that a water molecule anchored by the ARG127 residue adjacent to molecular oxygen is present with the GTQ substrate, unlike with SAL. Further, hybrid quantum mechanics/molecular mechanics calculations indicated that alkylperoxo intermediate formation is more favorable with GTQ, highlighting the crucial role of the 5́'-OH group, which is absent in SAL, in SDO's differential catalytic activity.

Reaction models are essential for understanding chemical reactions, but modeling them is a time-demanding process. Automated reaction space exploration techniques, such as ChemTraYzer-TAD, can simplify this process. However, finding transition states (TS) remains a hurdle. TS geometries are crucial for calculating reaction rate constants. Quantum mechanical methods are computationally expensive for TS geometry searches, while reactive molecular mechanics, like ReaxFF, offer faster calculations. Accurate TS searches require second derivatives of energy. State-of-the-art ReaxFF implementations can provide these derivatives only through finite differentiation (FD), which introduces noise. Automatic differentiation (AD) can provide more accurate second derivatives. Hence, this work integrates AD into the classical molecular dynamics code LAMMPS for calculations of second derivatives, presenting ADfied LAMMPS. By interfacing ADfied LAMMPS with the Gaussian computational chemistry suite, LMP-Gau is developed, enabling efficient geometry optimization, frequency calculations, and reaction path following for any force field. LMP-Gau demonstrates improved energy minimization for stable molecules in comparison to standard LAMMPS methods. It is also used successfully to find transition states in 1,3-dioxolane oxidation, demonstrating improved convergence with AD compared to FD.

The metal-organic framework (MOF) MIL-53(Al) has a framework geometry with an unconstrained wine-rack mode that enables it to concertina between large-pore (lp) and narrow-pore (np) structures either under pressure or with the uptake of adsorbates. This article presents the results of equilibrium classical molecular dynamics simulations that show that, in these breathing MOFs, the thermal conductivity─an important property for gas sorption in high-surface-area materials─also changes between open and closed conformations. The observed conductivity change differs significantly from that of a network of resistors undergoing the same geometric change. The dispersion relations and phonon group velocities in both the lp and np configurations of MIL-53(Al) are computed using self-consistent-charge density functional-based tight binding (SCC-DFTB). These exhibit rattler-mode behavior and phonon-focusing effects. To provide a mechanistic understanding of these features, a reduced order model of the MOF architecture is presented that captures the lattice dynamics and the band avoidance of acoustic and rattler phonon bands. Moreover, it is shown that the observed phonon focusing is caused by band bending from the rattler modes.

Hybrid metallic nanoalloys combining plasmonic and catalytic metals are essential for the development of advanced photocatalysts. Gold core–satellites—nanostructures featuring a gold core decorated with smaller transition metal clusters—are shown to offer high photocatalytic activity. Notwithstanding, their morphological formation and stability remain poorly explored. Using classical molecular dynamics simulations at relatively high temperatures, we study the morphological evolution of an Au core with Rh, Pt, or Pd satellites. A time scale of ns is long enough to detect the main morphological changes. We introduce a clustering approach and a Spearman analysis to identify correlations among geometrical descriptors. Chemical reordering of AuRh, AuPt, and AuPd core–satellites is not correlated with structural changes, and nanoalloys never undergo any solid–liquid transitions. Among them, AuRh core–satellite morphology exhibits the highest stability, with the two metals maintaining distinct domains. In contrast, only 25 of AuPt nanoalloys preserve a core–satellite morphology, and less than 15% in the case of AuPd. AuPt and AuPd rearrange into single, often icosahedral aggregates within ns. AuPt forms an Au shell with a non‐negligible interdiffusion of Pt within the subsurface for Pt seeds smaller than 561 atoms. AuPd shows a significant Pd interdiffusion and mixing.

The targeting of nucleic acid platforms is of particular interest in biochemistry and pharmaceutical applications. Among nucleic-based structures, aptamers, short, synthetic oligonucleotides, stand out because of their tunable sequences, enabling highly selective recognition of molecules of different sizes. However, an accurate evaluation of aptamers' affinity toward their targets remains elusive, as results obtained from different experimental techniques are often inconsistent. In this context, computational methods provide an appealing alternative for characterizing aptamer binding and structure. To this end, we selected two ochratoxin A binding aptamers as a case study to assess the ability of molecular dynamics simulations and alchemical free energy calculations to model the conformational dynamics and binding thermodynamics of these systems. Extensive classical molecular dynamics simulations were performed to characterize the aptamers' structures in the absence of their ligand, which could not be determined experimentally due to the intrinsic flexibility of these sequences. Additional simulations on aptamer-ligand complexes provided atomistic details of the interactions underlying the corresponding aptamers' preferential binding to their target compared with an analogue ligand that differs by a single atom. Lastly, alchemical free energy calculations were employed to estimate aptamers' selectivity for the target over its analogue, expressed as relative binding free energy. Our estimates are in good agreement with experimental data. We expect these computational strategies to contribute to future protocols for aptamer design and evaluation, enabling a more rigorous assessment of their binding to biochemically relevant molecules.

Lysophosphatidic acids (LPAs) are bioactive lipids that regulate numerous physiological functions in humans. Cell signaling by LPAs is mediated mainly via six LPA receptors (LPA1-6), class A G protein-coupled receptors (GPCRs). Among these, LPA1 is recognized to play an essential role in cell proliferation, survival, migration, and tumorigenesis. Despite the structural similarity, 18:0-LPA and 18:1-LPA exhibit distinct functional responses in cell lines overexpressing LPA1. Specifically, our in vitro studies show that 18:1-LPA induces greater Erk activation than 18:0-LPA in PC-3 human prostate cancer cells. The structural basis underlying this differential receptor activation has not been previously studied. Using classical molecular dynamics and enhanced sampling techniques, we examined the access and binding mechanisms of the two LPA species to the active state LPA1 receptor. The results show that 18:0-LPA and 18:1-LPA adopt distinct and dynamic poses in the orthosteric pocket despite their similar starting configurations. Mainly, the alkyl chains of the ligands exhibit distinct orientations and residue interactions, leading to differential conformational changes in key activation switches on the conserved CWxP and PIF structural motifs of the receptor. Also, there are significant differences in interhelical interactions at the intracellular end of the transmembrane helices 1, 3, 6, and 7. These distinct arrangements lead to striking differences in LPA1 interactions with the Gα-helix of the heterotrimeric Gi-protein. Notably, 18:0-LPA and 18:1-LPA exhibit similar membrane partitioning characteristics and receptor entry processes through aqueous paths. Our comprehensive in-silico studies offer valuable structural insights into the observed differences in functional responses by 18:0- and 18:1-LPA.

Intermediate Thermal Equilibrium Stages in Molecular Dynamics Simulations of two Bodies in Contact

A systematic comparison was carried out to assess the influence of representative thermostat methods in constant-temperature molecular dynamics simulations. The thermostat algorithms considered include the Nosé-Hoover thermostat and its chain generalization, the Bussi velocity rescaling method, and several implementations of the Langevin dynamics. Using a binary Lennard-Jones liquid as a model glass former, we investigated how the sampling of physical observables, such as particle velocities and potential energy, responds to changes in time step across these thermostats. While the Nosé-Hoover chain and Bussi thermostats provide reliable temperature control, a pronounced time-step dependence was observed in the potential energy. Among the Langevin methods, the Grønbech-Jensen-Farago scheme provided the most consistent sampling of both temperature and potential energy. Nonetheless, Langevin dynamics typically incurs approximately twice the computational cost due to the overhead of random number generation and exhibits a systematic decrease in diffusion coefficients with increasing friction. This study presents a broad comparison of thermostat methods, offering practical guidance for the choice of thermostats in classical molecular dynamics simulations. These findings provide useful insights for diverse applications, including glass transition, phase separation, and nucleation.