Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we develop a neural network EDA model capable of predicting the electron delocalization energy component of water molecules, which captures the stabilization arising from charge transfer between occupied absolutely localized molecular orbitals of one molecule and the virtual orbitals of another. Exploiting the locality assumption of the electronic structure, our model enables accurate prediction of electron delocalization energies for molecular systems far beyond the size accessible to conventional density functional theory calculations, while maintaining its accuracy. We demonstrate the applicability of our approach by modeling hydration effects in large molecular complexes, specifically in metal-organic frameworks.

In theoretical vibrational spectroscopy, methods based on classical dynamics are widespread. In this paper, I examine the controversial presence of overtones and combination bands-features typically associated with quantum mechanics-in classical spectra. Using a perturbative approach both in time- and frequency-space, the presence of these features can be ascribed to the potential anharmonicity, rather than nuclear quantum effects. The analytical results are then confirmed by the autocorrelation spectra of some one- and two-dimensional potentials and the comparison with quantum calculations. The differences between classical combination frequencies and quantum combination bands are derived from both theory and numerical simulations, providing a sound method to discriminate between the two.

Phosphorus-bearing molecules are of growing interest in astrochemistry because of the essential role of phosphorus in biochemistry. Understanding their interstellar chemistry requires both accurate spectroscopic data and insights into their formation mechanisms. In this work, for a new possible phosphorus-bearing species, protonated phosphorus mononitride, PNH+, we present a high-level theoretical study, which combines an accurate spectroscopic characterization with a thermochemical and kinetic investigation of its formation pathways. The key spectroscopic parameters, computed by exploiting composite schemes rooted in coupled-cluster theory, refer to both rotational and vibrational spectroscopy, thus including rotational and centrifugal distortion constants, hyperfine parameters, as well as vibrational frequencies and infrared intensities. To assess their accuracy for PNH+, a comparison with those of the isovalent N2H+, HCO+, and HCS+ ions, whose experimental characterization is available, has been made. In parallel, we investigated three gas-phase formation reactions relevant to interstellar conditions: the protonation of PN by H3+ and the ion-neutral PH + NH+ and PH+ + NH reactions. For each process, the reactive potential energy surface is sampled using density functional theory, and reaction rate coefficients are derived from long-range capture theory and master equation analysis. These rates were then incorporated into a dedicated astrochemical model to discuss the expected abundance of PNH+ in the interstellar medium. The results show that, under interstellar conditions, multiple exothermic pathways can lead to PNH+, thus reinforcing its potential role in interstellar phosphorus chemistry.

We propose Ehrenfest Dynamics with Spontaneous Localization (SLED), a decoherence-corrected extension of Ehrenfest dynamics based on the Gisin-Percival quantum-state diffusion equation. In SLED, the electronic wave function evolves stochastically in the adiabatic energy basis, producing trajectory-level localization. The trajectory ensemble reproduces a Lindblad-type propagation of the reduced electronic density matrix. This approach ensures linearity, trace preservation, and complete positivity, providing a physically consistent alternative to ad hoc decoherence corrections commonly adopted in mixed quantum-classical methods. Benchmark simulations on one-dimensional Tully models and multidimensional spin-boson Hamiltonians demonstrate that SLED reproduces electronic populations and captures the essential features of coherence decay. The tests, however, also reveal that accurate treatment will require generalizing the localization kernel controlling the electron-nucleus coupling strength from a constant into a function of time and phase space coordinates. SLED is implemented in the newly developed Skitten program and will be integrated into Newton-X. While the present study serves as a proof of concept, SLED establishes a rigorous and extensible framework that bridges mixed quantum-classical dynamics with open quantum system theory.

Natural gas hydrates are a promising energy resource for achieving sustainable development goals; however, their effective and economical storage and transportation remain challenging. While the effects of thermodynamic conditions on gas hydrate storage efficiency have been extensively studied, the impact of 1,3-dioxolane (DIOX) concentration has rarely been explored. In this study, molecular dynamics simulation was employed to investigate the regulatory mechanism of DIOX concentration on the gas storage efficiency of DIOX-CH4 hydrate. As the DIOX concentration increased, the growth rate of DIOX-CH4 hydrate initially increased, peaked at 5.56 mol. %, and then declined. The growth rate of DIOX-CH4 hydrate was influenced by the DIOX concentration through the synergistic effects of DIOX and CH4 on the formation of different hydrate cages. The gas storage capacity (GSC) markedly decreased as the DIOX concentration increased and was predominantly controlled by the occupancy of 512 cages. This impact was further amplified by similar effects on the occupancy of 51264 cages. In addition, the influence of temperature and pressure on hydrate growth and GSC was evaluated at a DIOX concentration of 5.56 mol. %. The growth patterns, consistent with those reported in prior studies, indicated similar underlying growth mechanisms. The optimal storage efficiency was achieved at 270 K and 1 MPa. These findings suggest that DIOX-CH4 hydrate is a promising solution for the storage and transportation of natural gas at a DIOX concentration of 5.56 mol. % under mild conditions, which highlights its potential for practical application.

Gold nanoclusters supported on oxide surfaces exhibit enhanced catalytic activity due to charge redistribution at defect sites and strong metal-support interactions. In this study, we employ machine-learned interatomic potential-based simulations to investigate the dynamics of Au8 nanoclusters adsorbed on an oxygen-vacancy (F-center) defected MgO (100) surface. On-the-fly probability-enhanced sampling (OPES) simulations driven by a graph neural network-based collective variable reveal the low-energy conformational landscape of Au8 and the preferred binding site of CO, while machine-learned Bader charges uncover an inverse correlation between Au-Au coordination number and localized negative charge on undercoordinated Au atoms. The most stable Au8 conformer was then used to probe CO adsorption, which shows preferential binding of CO to the most negatively charged undercoordinated Au sites. Subsequent O2 adsorption resulted in significant charge transfer that enhances CO oxidation reactivity on the FC-defected MgO (100) surface. These findings highlight how defect-mediated charge transfer and cluster morphology together dictate adsorption behavior and catalytic functionality on oxide supports.

Zwitterions (ZIs) are unique molecules that carry both positive and negative charges, resulting in overall charge neutrality and high dielectric constants. These distinctive properties have enabled broad applications of zwitterionic functionality, including the emerging use of ZIs in lithium-ion battery electrolytes. As a contribution to this developing field, we use all-atom molecular dynamics simulations to investigate the ion transport mechanisms in amorphous mixtures of a zwitterionic liquid containing a range of LiTFSI salt concentrations. The local coordination environment around the Li+ ions plays a strong role in governing ionic conductivity, as well as the enhancement of Li+ transport numbers with increasing salt concentration. Addition of small amounts of water leads to increased conductivity and ion mobilities due to the water coordinating with the Li+ ions, which reduces direct interactions with larger charged species.

The adsorption energies of perfluorobutanesulfonic acid, perfluorobutanoic acid, and trifluoroacetic acid on functionalized activated carbon are calculated from first principles. We introduce a novel approach based on a thermochemical cycle and a continuum solvation model to address neutral and charged adsorption complexes and account for concentration, pH, and pore-size effects. The results highlight the benefit of N-based functional groups for enhancing the removal of challenging, short-chain perfluoroalkyl substances from water by activated carbon.

The nonequilibrium relaxation of a series of, in part, very different photoactive proteins is compared, ranging over up to eleven decades in time. The series comprises various PDZ domains and MCL 1/peptide complexes with artificial azobenzene photoswitches, as well as two different cyanobacteriochromes (Slr-g3 and TePixJ). In either case, an embedded chromophore photoisomerizes after electronic excitation on an ultrafast femtosecond to picosecond timescale, initially perturbing the structure of the protein directly around the chromophore. This local perturbation propagates over the protein in a cascade of events, which spread over a wide range of timescales from picoseconds to seconds. In a very universal manner for all protein systems, a series of kinetic steps can be identified using lifetime analysis with a roughly equidistant spacing of about one per decade on a logarithmic scale. First, the inherent resolution to disentangle exponential relaxation processes is carefully evaluated. Concluding that this is not limiting, various models are discussed that may cause such a universal relaxation response. Diffusion on a rugged free energy landscape along a one- or low-dimensional progress variable may explain that behavior, where the quasi-randomness of the kinetic matrix thins out eigenstates that contribute to transport. The separation of kinetic steps is a measure of the typical barrier heights, which, by comparison to the universal patterns observed experimentally, is found to be in the range of kBT. Such barrier heights give a protein the flexibility to quickly structurally rearrange, yet provide some level of stability, which is relevant, for example, in the context of allosteric communication.

We have developed a multi-step strategy for training stable and precise machine learning potentials (MLPs) for activated processes that are accurate for both in-domain interpolation and out-of-domain (OOD) extrapolation regimes and applied it in the realm of vacancies in 2D materials. An essential part of obtaining well-performing MLPs is balanced and properly sampled datasets. To achieve this, we have designed a sampling technique based on the nudged elastic band and constrained molecular dynamics. Our analysis goes well beyond the calculation of conventional metrics, such as the root mean square error on the validation dataset. We use tailor-made metrics that focus on the atoms that critically determine the defect migration process. In the context of chalcogen vacancy dynamics in monolayer MoS2, we extensively benchmarked the MACE MLP model and checked its behavior for atoms close to the vacancy or in near-barrier configurations, which are the most difficult to describe since they require very robust OOD generalization performance. Generally, we found that a properly trained MACE model is able to reliably reproduce energies and forces even in these extreme cases. To demonstrate the utility of our approach, we calculated relaxations and minimum energy paths for single- and multi-vacancy transitions in monolayer MoS2, as well as free energy barriers utilizing thermodynamic integration. We believe our conclusions are also valid for other equivariant message passing neural network potentials due to their general similarity. Finally, we discuss the possibility of tuning the density functional theory-based MLP toward quantum Monte Carlo accuracy.

We introduce a one-parameter generalization of Nosé's thermostat that rescales coordinates and momenta in the virtual system by a tuning parameter a. The resulting real-time equations of motion preserve a stationary extended density whose marginal over the physical coordinate and momentum variables (q, p) is canonical and independent of a. Thus, a tunes the dynamics without altering the target ensemble. For a harmonic oscillator with angular frequency ω, the symmetric case (a=12) is Liouville-integrable (a second invariant confines trajectories) and, therefore, non-ergodic. A local linear analysis of the (q, p) block shows that 0 ≤ a ≤ 1 yields only node/spiral types and, thus, precludes chaos. By contrast, a < 0 or a > 1 creates genuine saddle sectors whenever the thermostat variable satisfies |ζ|>ζc=ω/-a(1-a), furnishing a minimal stretch-squeeze mechanism for robust chaotic mixing. Numerical tests on harmonic and double-well models corroborate these predictions. Under fixed-volume periodic boundary conditions, inserting the virial identity with laboratory velocities imposes a hidden constraint. We remove it either by adopting a state-dependent gauge parameter or by centering the virial-work term, thereby eliminating the hidden "pressure lock." We also outline compatibility with Nosé-Hoover chains and a continuous, Hamiltonian-like construction for the grand canonical (μVT) ensemble. Overall, this framework provides a minimal, deterministic thermostat whose single parameter controls chaotic mixing while preserving the desired ensemble.

In this study, we generated and characterized two C3-benzene compounds through the reaction of a C3 carbon molecule with benzene at 4 K. These compounds were identified using infrared spectroscopy with D and 13C isotopic substitution of benzene reagents, as well as quantum chemical calculations. Upon sample deposition and annealing, a weakly interacting C3-benzene complex (labeled A) was observed. The predicted bond distance between the C3 unit and benzene is 3.260 Å, indicating a relatively weak interaction due to this extended separation. Under UV-light irradiation, a previously unconsidered singlet heptafulvene carbene (labeled B) was generated at the expense of species A. The overall C3-mediated C-C bond insertion reaction of benzene, leading to the formation of heptafulvene carbene (B), is predicted to be exothermic by 200 kJ mol-1. This novel singlet heptafulvene carbene compound has been produced and characterized experimentally for the first time in solid neon. In addition, the reaction pathway for fulvenallene formation in the reaction between a carbon atom and benzene is discussed in detail. The results presented herein provide new insights into understanding the reactivity of carbon molecules and the synthesis of novel carbene compounds.

We demonstrate that hyperuniformity, the suppression of density fluctuations at large length scales, emerges generically from the interplay between conservation laws and non-equilibrium driving. The underlying mechanism for this emergence is analogous to self-organized criticality. Based on this understanding, we introduce four non-equilibrium models that consistently demonstrate hyperuniformity. Furthermore, we show that systems with an arbitrary number of conserved mass multipole moments exhibit an arbitrary strong tunable hyperuniform scaling, with the structure factor following S(k) ∼ km, where m is set by the number of conserved multipoles. Finally, we find that hyperuniformity arising from a combination of conserved noise and partially conserved average motion is not robust against non-linear perturbations. These results highlight the central role of conservation laws in stabilizing hyperuniformity and reveal a unifying mechanism for its emergence in non-equilibrium systems.

Phase diagrams of diblock copolymers as functions of pressure and composition, essential for the molecular design of energy-efficient polymeric materials with pressure processability, are presented. While pressure-dependent modifications of temperature-composition phase diagrams have been investigated in relation to temperature-induced transitions, diagrams explicitly displayed on the pressure-composition plane remain largely underexplored. To establish a fundamental understanding of pressure-induced phase transitions, we construct pressure-composition phase diagrams using compressible random phase approximation theory and self-consistent field theory. The contrast in block compressibility is shown to govern pressure-responsive phase behavior: a large contrast enhances miscibility under pressure, whereas a small contrast reduces it. In highly asymmetric systems with more compressible soft segments, voids preferentially accumulate in soft domains, stabilizing ordered phases and skewing phase boundaries. These results provide a theoretical basis for the rational design of pressure-responsive polymeric systems.

We developed an alternative approach to access the full configuration interaction (FCI) energy based on perturbation expansions using the seniority-number basis. In this study, we started from the ground state of the seniority-zero Hamiltonian, also known as the doubly occupied configuration interaction (DOCI) wavefunction, to derive the Rayleigh-Schrödinger perturbation theories up to the fourth-order. We also derived the second-order perturbation theory by taking the DOCI-Fock operator as the zeroth-order Hamiltonian. From demonstrative calculations for small molecules, the DOCI-SCF-RSPT4 and the DOCI-SCF-MP2 quantitatively reproduced the FCI energy. Although solving the DOCI wavefunction is an NP-hard problem in classical computing, connections to the density matrix renormalization group or quantum computing will provide a promising way to accurately solve many-electron problems.

Adsorption processes play a fundamental role in molecular transport through nanofluidic systems, but their signatures in measured signals are often hard to distinguish from other processes, such as diffusion. In this paper, we derive an expression for the power spectral density (PSD) of particle number fluctuations in a channel, accounting for diffusion and adsorption/desorption to a wall. Our model, validated by Brownian dynamics simulations, is set in a minimal but adaptable geometry, allowing us to eliminate the effects of specific geometries. We identify distinct signatures in the PSD as a function of frequency f, including a 1/f3/2 scaling related to diffusive entrance/exit effects and a 1/f2 scaling associated with adsorption. These scalings appear in key predicted quantities-the total number of particles in the channel and the number of adsorbed or unadsorbed particles-and can dominate or combine in non-trivial ways depending on parameter values. Notably, when there is a separation of timescales between diffusion inside the channel and adsorption/desorption times, the PSD can exhibit two distinct corners with well-separated slopes in some of the predicted quantities. We provide a strategy to identify adsorption and diffusion mechanisms in the shape of the PSD of experimental systems on the nano- and micro-scale, such as ion channels, nanopores, and electrochemical sensors, potentially offering insights into noisy experimental data.

Understanding the microscopic structure of water at metal oxide interfaces is crucial for advancing electrocatalysis. IrO2, specifically, has shown exceptional activity for electrochemical water oxidation, but we currently lack a fundamental understanding of how the surface structure of IrO2 impacts water reactivity. In this study, we developed a machine learning potential trained to first-principles accuracy for modeling IrO2/water interfaces across different facets: (110), (100), (101), and (001). Using extensive machine learning molecular dynamics simulations, we investigated the spontaneous dissociation of water molecules at these interfaces. Our results reveal a distinct dissociation probability trend: (110) > (100) ≈ (101) > (001), which we attribute primarily to the reaction thermodynamics of surface water dissociation. A strong correlation is observed between the surface Ir-O bond distances and the dissociation probabilities, highlighting the role of surface geometry in modulating reactivity. As a consequence, the interfacial solvation structures and hydrogen bonding environments are dynamically tuned by the varying water dissociation capabilities across facets. This work elucidates how water dissociation energetics depend on surface orientation and interfacial structure, offering atomistic insights into manipulating reaction chemistry at electrocatalytic interfaces.

In nature, hollow precipitation tubes form around deep sea hydrothermal vents and generate an electric potential across the material membrane. These structures are of significant scientific interest due to their possible connection to the origins of life on Earth, and synthetic precipitation membrane structures have been created in the laboratory to study their growth. This paper reports on the formation of metal hydroxide precipitation membranes within a microfluidic device designed to allow for the measurement of the electric potential across the flow channel during material formation. Using this device, the electric potential and growth curves were measured for nickel hydroxide, iron hydroxide, and cobalt hydroxide precipitation membranes. Based on these experiments, it was hypothesized that the application of an electric potential in opposition to the generated potential would reduce the growth rate of the membranes, and this hypothesis was experimentally verified. The results of this work discuss that the membranes formed are likely selectively permeable to a diffusive positive ion, possibly H+, which is responsible for controlling the growth rates of the material. Additional experiments, including direct electrical measurements of the membrane itself during growth, measurement of the pH within the flow channel, and material characterization after removal from the device, are proposed to further explore the growth of precipitation membranes.

We present the investigation of sum-frequency vibrational spectroscopy (SFVS) for interfacial OH stretching modes, employing a mixed quantum/classical approach coupled with artificial neural network-based molecular dynamics simulations. This study provides an explicit account of electric dipole effects under Fermi resonance, which couples the bending overtone with the fundamental stretching mode. Our results reveal that Fermi resonance modulates the spectral features of stretching-mode SSP SFVS. The findings provide new molecular-level insights into the vibrational coupling mechanisms at water interfaces and represent an important advancement in interpreting surface-specific vibrational spectra.

Conventional density functional theory (DFT) is unable to predict inverted singlet-triplet gaps, largely because the underlying Kohn-Sham framework does not capture correlation effects arising from double excitations. This limitation has hindered the accurate description of systems where singlet-triplet inversion plays a key role, for example, in thermally activated delayed fluorescence emitters. To address this issue, we develop a modified long-range corrected functional, LC-BLYP(D), that augments the ground-state description with MP2 correlation and incorporates CIS(D) correlation for the excited state. By explicitly accounting for correlation effects that are absent in conventional functionals, LC-BLYP(D) provides a more balanced treatment of ground- and excited-state energetics. In addition, we introduce a single-step tuning protocol for the range-separation parameter, designed to optimize the performance of LC-BLYP(D) without the need for iterative procedures. When applied in combination, the tuned LC-BLYP(D) functional reproduces inverted singlet-triplet gaps with a mean absolute error of 0.022 eV, representing a substantial improvement over standard DFT approaches. These results demonstrate that the incorporation of correlated wavefunction-based corrections into a range-separated framework not only overcomes a fundamental shortcoming of DFT but also offers a practical and accurate tool for investigating excited-state properties in complex molecular systems.