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The approximate second order coupled-cluster method based on a size-consistent Brillouin–Wigner partitioning
We present a variant of the approximate second order coupled-cluster method (CC2) with a two-parameter size-consistent Brillouin–Wigner (BW-s) partitioning instead of a Møller–Plesset (MP) partitioning for the unperturbed Hamiltonian, which we refer to as BWs-CC2. The computational complexity of this model scales identically to CC2 with molecular size. Conventional CC2 and its regularized BWs-CC2 variants, as well as conventional MP2 and two of its regularized BW-s2 variants, were assessed on a 535 element database spanning thermochemistry, non-covalent interactions, barrier heights, and isomerization energies. To ensure a well-defined model chemistry, the assessment was performed using internally stable spin-polarized Hartree–Fock (HF) orbitals in the finite aug-cc-pVQZ basis without counterpoise corrections. As a result of using stable orbitals, contrary to conventional wisdom, we find that CC2 substantially outperforms MP2 on molecules with significantly spin contaminated reference orbitals without a significant increase in error on systems with a spin-pure reference, showing the value of its single substitutions. While no single choice of regularization parameters can be optimal for all datasets, we find that BWs-CC2 generally outperforms both CC2 and BW-s2 with a single judicious parameter choice. Additional tests on dipole moments and bond lengths of diatomics provide further support for the utility of this choice. The main outliers and poorest performing cases are associated with large amounts of spin-contamination in the HF reference, which is indicative of systems with either strong correlation or extensive artificial symmetry breaking. Overall, these findings argue that the perception of the quality of the CC2 ground state should be reevaluated and that it can be further improved upon by the soundly based BWs-CC2 variant with the recommended parameter choice.
Vibrational recognition of local structures in hydrogen boride sheets
We present a comprehensive vibrational analysis of hydrogen boride sheets through first-principles calculations, focusing on a variety of local structural configurations, including surfaces, edges, and bilayer models—some of which contain vacancy and substitutional defects. A database of vibrational modes and simulated infrared spectra was constructed, revealing distinct spectral features that serve as characteristic fingerprints for different bonding environments. By analyzing mode distributions with respect to coordination environments, we identify the distinct contributions of terminal and bridging hydrogen atoms to specific spectral regions. The results show that structural modifications, such as interlayer stacking and edge termination, have a significant influence on vibrational characteristics, particularly in the intermediate and high frequency regions. The vibrational database enables the precise interpretation of infrared spectra and offers a reliable reference for model selection and peak analysis in both experimental and theoretical studies on hydrogen boride sheets and related materials.
Cesium exhibits different mesoscale segregation and ion pairing than lithium, sodium, or potassium in concentrated alkaline aqueous nitrite solutions
Understanding ion pairing in concentrated alkaline electrolytes facilitates the prediction and control of chemical processes in nuclear waste. While sodium is the dominant alkali metal in such systems, cesium often exhibits distinct bulk behavior relative to lighter alkali cations. Aqueous mixtures of cesium hydroxide and sodium nitrite were compared to alkali hydroxide analogs using small-angle x-ray scattering, revealing that cesium disrupts the sodium nitrite electrolyte structure, forming cesium-rich domains. This mesoscale segregation contrasts with the near-ideal mixing observed in other mixed hydroxide–nitrite systems. Raman spectroscopy indicates cesium–nitrite ion pairing, evidenced by vibrational shifts distinct from those associated with sodium. Multinuclear magnetic resonance spectroscopy further supports cesium–nitrite and cesium–hydroxide association, revealing a distinct local environment for nitrite in cesium-containing solutions. Together, these findings show that cesium promotes a unique solution structure dominated by specific ion pairing within segregated domains. This structural organization may influence radical generation pathways in high-ionic-strength alkaline media relevant to nuclear waste processing and management.
Structural insights into nitrile–methanol hydrogen-bonded complexes based on mass-selected infrared spectroscopy and theoretical calculations
Hydrogen bonds, crucial for the formation and stabilization of complex organic molecules, present intriguing possibilities for the existence of such molecular complexes in extraterrestrial environments. To investigate the microscopic hydrogen-bonding networks in nitrile–methanol clusters—potential candidates for interstellar organic complexes—we recorded infrared spectra of neutral XN-M (XN = nitriles; M = methanol) heterodimers in the 2600-3900 cm−1 range using infrared-vacuum ultraviolet (IR-VUV) spectroscopy. Quantum chemical calculations based on harmonic approximations are typically used to simulate such spectral peaks; however, they often fail to fully reproduce the experimental features. To overcome these limitations, we employed ab initio anharmonic algorithms and ab initio molecular dynamics (AIMD) simulations. Anharmonic algorithms successfully assigned elusive spectral features by accounting for vibrational couplings (e.g., Fermi resonances and combination bands), and AIMD proved essential for accurately assigning characteristically broad hydrogen-bonded OH bands unresolved by harmonic methods. Applying this integrated approach, we confirmed the distinct H-bonding preferences in the XN-M heterodimers: acetonitrile (ACN) and unsaturated aliphatic nitriles (e.g., 3-butene nitrile, 3BN; 4-pentene nitrile, 4PN) predominantly adopt linear N⋯H–O configurations, whereas acrylonitrile and benzonitrile favor cyclic double H-bonded structures. This study establishes a powerful framework that integrates advanced computational methodologies to decipher hydrogen-bonding motifs from complex spectral data, enabling the direct interpretation of both laboratory and astronomical infrared observations.
Adsorption of benzene on graphene studied by ML-accelerated <i>ab initio</i> molecular dynamics simulations
Periodic ab initio molecular dynamics simulations accelerated by machine learning were conducted to investigate the adsorption of benzene on graphene at a coverage of 0.11 ML and a temperature of 150 K. For this purpose, seven density functional approximations (DFAs) were used, differing in the exchange–correlation functional (PBE, SCAN, HSE06, and vdW-optB86b) and the long-range dispersion correction (D2, D3, D4, and MBD). Thermal effects on the structure, enthalpy (ΔadsH), and Gibbs free energy (ΔadsG) of adsorption were analyzed. The relatively fast surface diffusion of the adsorbate was identified as the dominant anharmonic effect causing an increase in ΔadsG of up to 50% compared to static harmonic calculations. In contrast, the anharmonic effect on ΔadsH was shown to be nearly negligible. The computed thermodynamic data were used to predict the desorption kinetics parameters, i.e., activation energy (Ea), Arrhenius pre-factor (ν), and position of desorption maximum (Tmax). Among the DFAs tested, the PBE + D4 method was found to provide the best overall agreement with the experimental data. The SCAN + MBD, PBE + D3, and PBE + D2 methods predict Ea values that agree with the experimentally determined value within the reported uncertainty but significantly underestimate Tmax.
High kinetic stability and high density of Ir(ppy)3 doped organic semiconductor glasses
Glasses prepared by physical vapor deposition (PVD) can have advantageous material properties, such as highly enhanced thermal stability and denser molecular packing, and thin glassy films prepared by PVD are utilized as active layers in organic light emitting diodes (OLEDs). However, the stability and density of PVD glasses with compositions typical of OLED devices are not well studied. Here, we prepared Ir(ppy)3 doped vapor-deposited glasses in three different organic semiconductor hosts; Ir(ppy)3 in a dilute concentration is often used as a light emitter in phosphorescent OLEDs. We studied these glasses during temperature ramping using spectroscopic ellipsometry and found that the Ir(ppy)3 doped PVD glasses have high kinetic stability and high density. Surprisingly, the observed kinetic stability exceeds that of single-component PVD glasses. This work allows further understanding of the material properties influencing OLED performance, thus facilitating the design of durable and stable devices.
Matching correlations matters: Modeling friction in a hydrophobic folding transition
The generalized Langevin equation provides a powerful framework for modeling and interpreting the conformational dynamics of (macro)molecules in solution. However, recent studies have shown that the standard fluctuation–dissipation relation—linking the memory kernel to the statistics of the random force—can include a non-zero cross correlation term between conservative and random forces. This raises questions about how to correctly extract memory kernels from simulation data when this correlation is neglected and whether inverting the Volterra equation to obtain a memory kernel yields a physically meaningful result. In a recent work [Wolf et al. J. Chem. Phys. 162, 054113 (2025)], we proposed an approximation to account for the cross correlation term. We demonstrate in this work that cross correlations play a significant role in the collapse transition of a hydrophobic polymer under various solvent conditions. In addition, we demonstrate that our proposed approximation yields an improved description of barrier crossing times. Notably, we find that this improvement has the same magnitude as the improvement gained by accounting for memory effects.
Investigation of ionic activity behavior in aqueous solutions containing heavy metal salts at 298.15 K
Modeling ionic activity is essential for understanding ion-specific effects in environmental and industrial systems. The microscopic interactions of heavy metal salts in aqueous solutions become increasingly complex with rising salt molality, posing significant challenges for accurately modeling ionic activity. This study applies, for the first time, an electrolyte version of the cubic-plus-association equation of state to calculate the mean ionic activity coefficients of several heavy metal salts in water at 298.15 K. Experimental data for four types of salts—nickel, cadmium, cobalt, and zinc salts—were compiled, and the ion–water binary interaction parameters are regressed from these measurements. The model successfully predicts water activity, osmotic coefficients, and mean ionic activity coefficients across most systems. Without explicit incorporation of ionic association, the average calculation relative average deviation of the mean ionic activity coefficient for nickel and cobalt salts is 5.9% and 6.3%, respectively, extending the applicable molality range up to 5.0 mol/kg water and 5.5 mol/kg water. For solutions of cadmium salts and zinc salts that have been confirmed to contain ion pairs, ion association was introduced into the model for calculation. The calculation results indicate that the consideration of ion association has significantly reduced the calculation deviations. Within the calculated salt molality ranges, the average relative deviation of the mean ionic activity coefficient for cadmium salts is all below 2.0%, while that for zinc salts is all less than 5.5%, respectively. This study provides valuable insights into the micro-level factors influencing activity coefficients and offers recommendations for improving model accuracy under complex conditions.
Recovering hidden degrees of freedom using Gaussian processes
Dimensionality reduction represents a crucial step in extracting meaningful insights from Molecular Dynamics (MD) simulations. Conventional approaches, including linear methods such as principal component analysis as well as various autoencoder architectures, typically operate under the assumption of independent and identically distributed data, disregarding the sequential nature of MD simulations. Here, we introduce a physics-informed representation learning framework that leverages Gaussian processes combined with variational autoencoders to exploit the temporal dependencies inherent in MD data. Time-dependent kernel functions—such as the Matérn kernel—directly impose the temporal correlation structure of the input coordinates onto a low-dimensional space, preserving Markovianity in the reduced representation while faithfully capturing the essential dynamics. Using a three-dimensional toy model, we demonstrate that this approach can successfully identify and separate dynamically distinct states that are geometrically indistinguishable due to hidden degrees of freedom. Applying the framework to a 50 μs-long MD trajectory of T4 lysozyme, we uncover dynamically distinct conformational substates that previous analyses failed to resolve, revealing functional relationships that become apparent only when temporal correlations are taken into account. This time-aware perspective provides a promising framework for understanding complex biomolecular systems, in which conventional collective variables fail to capture the full dynamical picture.
The solubilities of water in liquid CO2 coexisting with water or hydrate
We investigate the solubilities of water in liquid CO2 in the presence and absence of coexisting clathrate hydrate from theoretical calculations of the chemical potentials of water and CO2 in the aqueous, hydrate, and CO2 fluid phases across a wide range of temperatures and pressures. One of the advantages of the present method is that it is applicable to deeply cooled and heavily compressed states where a CO2 hydrate is formed. The experimental solubility curve against temperature is successfully recovered with an appropriate correction for the self-polarization energy implicitly embedded in the pairwise additive interaction model for water. The solubility of water in liquid CO2 coexisting with an aqueous solution decreases upon cooling. It is found that the intervening hydrate steadily reduces the solubility of water compared to that assumed to be in direct contact with the aqueous phase. The significant decrease results from the decrease in the chemical potential of water in the hydrate, relocating the boundary from the water/hydrate to the hydrate/fluid. Liquid CO2 loses its capacity to retain water upon cooling more seriously than that anticipated from the water/fluid boundary, and an excessive amount of water is precipitated into hydrate at low temperatures. The thermodynamic properties thus calculated provide valuable information on problems in the massive transport of CO2, specifically the blockage of pipelines and corrosion of vessels.
Learning collective variables that respect permutational symmetry
In addition to translational and rotational symmetries, clusters of identical interacting particles possess permutational symmetry. Coarse-grained models for such systems are instrumental in identifying metastable states, providing an effective description of their dynamics, and estimating transition rates. We propose a numerical framework for learning collective variables that respect translational, rotational, and permutational symmetries and for estimating transition rates and residence times. It combines a sort-based featurization, residence manifold learning in the feature space, and learning of collective variables with autoencoders whose loss function utilizes the orthogonality relationship [F. Legoll and T. Lelievre, Nonlinearity 23, 2131–2163 (2010)]. The committor of the resulting reduced model is used as the reaction coordinate in the forward flux sampling and to design a control for sampling the transition path process. We offer two case studies, the Lennard-Jones-7 in 2D and the Lennard-Jones-8 in 3D. The transition rates and residence times computed with the aid of the reduced models agree with those obtained via brute-force methods.
Origin of 4′,6-diamidino-2-phenylindole (DAPI) fluorescence dynamics in solution and DNA minor groove binding: Unveiled by femtosecond broadband fluorescence, transient absorption, and theoretical calculations
4′,6-Diamidino-2-phenylindole (DAPI) is a widely utilized DNA sensor, renowned for its strong affinity for the AT minor groove. Despite its essential role as a fluorescent probe in molecular biology and biomedical research, the origins of DAPI’s intrinsic fluorescence and the mechanisms behind its fluorescence enhancement upon DNA sensing remain unresolved. This study provides a comprehensive investigation into the fluorescence dynamics of DAPI, both in solution and when bound to detect the AT minor groove of a self-complementary dodecamer duplex DNA. We employed an integrated approach combining femtosecond broadband time-resolved fluorescence, transient absorption, and supported by theoretical calculations. The results reveal an unprecedented ultrafast inter-solute–solvent three-state excited-state proton transfer pathway, which clarifies the root cause of low fluorescence yield and fluorescence dynamics of free form DAPI in aqueous solution. Irrespective of the structural heterogeneity and rotamer conformation of the ground state of DAPI, this involves deprotonation of the excited state at a rate of ∼2.4 ps, followed by proton reuptake by the resulting weakly emissive deprotonated state, leading to direct reformation of DAPI’s ground state at 139 ps. Binding to the DNA minor groove completely inhibits this proton transfer, accounting for the significant fluorescence enhancement observed in the DAPI-DNA complex. Furthermore, the broadband capacity of our time-resolved fluorescence approach enables, for the first time, direct tracking of the fluorescence dynamic Stokes shift of minor groove-bound DAPI, revealing significant dispersive collective solvation dynamics specific to the sensing site. These findings provide valuable insights into how microenvironments dictate DAPI fluorescence dynamics and may assist in the strategic design of light-up sensors for recognizing interior hydration dynamics and local residue motions of DNA.
Computing dielectric spectra in molecular dynamics simulations: Using a cavity to disentangle self and cross correlations
Dielectric spectra are typically obtained in molecular dynamics (MD) simulations by analyzing the fluctuations, in the absence of an applied electric field, of the total dipole moment of the simulation box. We compare this standard method to a protocol that focuses on a virtual cavity whose size is chosen to include short-range dipolar cross correlations, while excluding long-range correlations that are affected by the choice of electrostatic boundary conditions. We tested this protocol on three non-polarizable systems with different dielectric permittivities. We showed that it produces the same dielectric spectra as the standard method while being less sensitive to noise. The question of the decomposition of a dielectric spectrum into self and cross contributions is discussed in the context of both methods. We propose that, for a liquid with a sufficiently high dielectric permittivity, the cavity protocol yields a self-spectrum consistent with the electrostatic boundary conditions applicable to the experimental situation.
In search of mechanism of aggregation-induced emission in carbazole and triphenylamine substituted ethenes: An approach based on spin-flip time dependent density functional theory and optimally tuned range-separated hybrid functional
Aggregation-induced emission (AIE) has emerged as a groundbreaking advancement in the field of photoluminescence behavior. A thorough understanding of the AIE mechanism is essential for the rational design of innovative molecules exhibiting these exceptional properties. In this study, we report a quantum mechanical (QM) investigation through spin-flip time dependent density functional theory (SF-TDDFT) using optimally tuned range-separated hybrid (OT-RSH) functional to explore the cause of fluorescence quenching of the ethylene derivatives such as TPE-9CB, TPE-3CB, and tetraphenylethene-triphenylamine in tetrahydrofuran (THF) solution. The fluorescence enhancement in the crystalline state of an ethylene derivative TPE-9CB is studied by adopting a QM:MM based approach using the SF-TDDFT method and RSH functional. It is observed that the OT-RSH functional delivers impressive results regarding photophysical properties. The twisting of the central C=C bond has been identified as the phenomenon that quenches the photoexcited state of the ethylene derivatives in THF solution. Notably, the minimum-gap point (MGP) along the α-torsional coordinate of the S1 state in solution lies ∼0.1 eV below the Frank–Condon (FC) point for all the derivatives. Interestingly, all examined monomers exhibited nearly zero oscillator strength (f values) near the point of minimum-gap, indicating minimal radiative transitions in the solution state. Conversely, the excited state deactivation channels, which involve the twisting of the central C=C bond in the solution phase, are effectively restricted in the solid state by steric hindrance and electrostatic repulsion from the neighboring molecules. In the solid state, the MGP is 0.48 eV above the FC point, demanding high energy to prevent photoinduced rotary relaxation. This effectively blocks nonradiative deactivation pathways, resulting in a significant enhancement of the emission response. Our findings underscore the potential of AIE phenomena in advancing material design and applications.
Electronic conduction in copper–graphene composites with functional impurities
Coal-derived graphene-like material and its addition to FCC copper are investigated using ab initio plane wave density functional theory (DFT). We explore ring disorder in the sp2 carbon and functional impurities such as oxides (–O) and hydroxides (–OH) that are common in coal-derived graphene. The electronic density of states analysis revealed localized states near the Fermi level, with functional groups contributing predominantly to states below the Fermi level, while carbon atoms in non-hexagonal rings contributed mainly to states above it. The functionalization of graphene induces charge localization, while ring disorder disrupts the continuous flow of electrons. By projecting the electronic conductivity along specific spatial directions, we find that both the crystal orientation and the graphene purity significantly influence the anisotropy and magnitude of electronic transport in the composites. This study implicitly highlights the importance of structural stress to obtain improved electrical conductivity in such composites.
Molecular dynamics investigation of polymer-decorated nanoparticles with co-nonsolvent: Structural transitions from isotropic layers to heterogeneous patches
With the assistance of molecular dynamics simulations, we systematically investigate the conformations of polymer-decorated nanoparticles (PDNP), making use of the co-nonsolvency effect, which dramatically alters the effective solvent quality through minor variations of co-nonsolvent (CNS) concentrations. In response, surface-grafted polymers undergo a transition from an isotropic brush-layer in a good solvent to heterogeneous patches in CNS. To quantitatively describe this transition, we use the surface coverage θ as an additional order parameter and develop a theoretical model for its calculation, which is compared with the simulations. By further examining θ as a function of CNS concentration, we observe that PDNPs exhibit a collapse-reentry process similar to planar brushes, but with richer details of the two-step reentry behavior: angular isotropy recovery followed by radial expansion. We investigate how this CNS-induced collapse transition influences the reversible adsorption/exclusion of cargo nanoparticles on PDNPs, depending on the size of the cargo and the concentration of the cosolvent.
Blockwise optimization for projective variational quantum dynamics (BLOP-VQD): Algorithm and implementation for lattice systems
We present an efficient approach to simulate real-time quantum dynamics using projected variational quantum dynamics, where the computational cost is reduced by strategically optimizing only a subset of the variational parameters at each time step. Typically, the variational Ansatz consists of repeated blocks of parameterized quantum circuits, where all parameters are updated in a standard optimization procedure. In contrast, our method selectively optimizes one block at a time while keeping the others fixed, allowing for significant reductions in computational overhead. This semi-global optimization strategy ensures that all qubits are still involved in the evolution, but the optimization is localized to specific blocks, thus avoiding the need to update all parameters simultaneously. We propose different approaches for choosing the next block for optimization, including sequential, random, and fidelity-based updation. We demonstrate the performance of the proposed methods in a series of spin-lattice models with varying sizes and complexity. Our method preserves the accuracy of the time evolution with a much lower computational cost. This new optimization strategy provides a promising path toward high-fidelity simulation of the time evolution of complex quantum systems with reduced computational resources.
Deciphering the molecular mechanisms of startle disease: The role of the Asn46Lys mutation in the glycine receptor
The glycine receptor (GlyR) is a pentameric ligand-gated ion channel that plays important physiological roles in the nervous system. Its malfunction is related to neurological disorders, including startle disease (hyperekplexia), which is characterized by exaggerated startle responses to sudden unexpected stimuli, such as noise or touch. Here, we unravel the molecular mechanisms of action of a specific mutation that is linked to startle disease: Asn46Lys (N46K). This residue, in the GlyR extracellular domain, is not directly involved in neurotransmitter binding but can disrupt the neurotransmitter pathway. To understand how, we performed molecular dynamics simulations of the wild-type and mutated extracellular domain of a homomeric α1 GlyR, assessing the effects of the Asn46Lys mutation. We then sampled multiple unbinding and binding events by means of metadynamics simulations, with a funnel restraint to limit the exploration in the solvent. By evaluating the binding free energy landscape, we observed a substantial reduction in neurotransmitter binding affinity, which is consistent with experiments. We suggest that such a reduction is linked to the interaction of its crucial glutamic acid residue with the lysine mutation competing with the glycine ligand.
Mechanical characterization of intermediates of Ag+–DNA complex: An atomic force study
Silver ions (Ag+) are known to interact with DNA through nonspecific and specific mechanisms, inducing conformational and mechanical changes in the double helix. In this study, we investigate how varying Ag+ concentrations affect the mechanical properties of DNA, in particular, contour length, persistence length, end-to-end distance, and area of occupation, using atomic force microscopy. At low Ag+ concentrations, we observe localized stiffening and shortening of DNA molecules, which we attribute to the formation of monoadducts and biadducts with specific DNA bases. These interactions promote the emergence of secondary structures that contribute to DNA compaction and a reduction in contour length. At intermediate concentrations, accumulation of torsional stress and the formation of multiple metal-mediated contacts lead to a global conformational collapse, as evidenced by a significant decrease in the effective persistence length. At the highest Ag+ concentration tested (1.5 mM), the uniform distribution of molecular area suggests a widespread structural collapse of DNA. These findings support previous reports of Ag+-induced DNA condensation and reveal a progressive structural transition from extended to globular conformations. The observed behavior provides insights into the biophysical consequences of metal–DNA interactions and may have implications for understanding the molecular basis of the antimicrobial activity of silver ions.
Kinetic energy release distributions of multiply charged NOq+ (q = 2–7) molecular ions produced in collisions with 1.6 MeV Ar8+ ion beam
We report the fragmentation dynamics of multiply charged molecular ions of NO under the impact of highly charged Ar8+ ions in the intermediate velocity (vp ≈ 1 a.u.) range. Coincidence measurements of ionic fragments from NOq+ (2 ≤q≤ 7) were performed utilizing the three-dimensional momentum imaging technique of a recoil-ion momentum spectrometer equipped with a time- and position-sensitive detector and a multi-hit data acquisition system. With a 1.6 MeV Ar8+ beam, we observed a total of 13 fragmentation channels. Coulomb breakup of the unstable parent molecular ion shows a clear preference for the symmetric charge breakup over the asymmetric one. We have measured the Kinetic Energy Release Distributions (KERDs) for various fragmentation channels. We have also generated the potential energy curves for NOq+ molecular ions using the internally contracted multi-reference configuration interaction approach with complete active space self-consistent field wave functions. The obtained KERDs are compared and discussed in the background of the ab initio potential energy curves as well as the simple Coulomb explosion model.