Papers

Nonadiabatic Matsubara dynamics has been proposed in both the Meyer-Miller-Stock-Thoss (MMST) and spin-mapping representations, whereby the dynamics are truncated in the higher path-integral normal modes of the nuclear degrees of freedom, but no truncation is performed in the electronic variables. In contrast to single-surface Matsubara dynamics, these methods do not appear to conserve the Quantum Boltzmann Distribution (QBD) for general systems. Recently, it was shown that truncating in the electronic path-integral normal modes of the MMST representation does not lead to conservation of the QBD for a single trajectory or accurate computation of a correlation function. Here, the electronic path-integral normal modes in the spin-mapping representation are investigated. We focus on a two-level system although we expect our findings to be applicable to any number of electronic states. We find, perhaps surprisingly, that the higher normal modes of the spin-vector $z$-component, the difference in state populations, are constrained by the QBD, that the time-evolved state population observable is a function of only the spin-mapping centroid, and that the full $N$-bead computational results are replicated using only the spin-mapping centroid, leading to accurate, QBD conserving dynamics. While at present these results are not a generally-applicable method, we believe this may explain the recent success of spin-mapping approaches compared to MMST approaches, %To our knowledge, this is the first method obtaining conservation of the QBD when truncating in electronic normal modes and should aid the future derivation of highly accurate and Quantum Boltzmann conserving nonadiabatic dynamics methods.

Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid interface dataset, containing 15.4 million first-principles atomic structures with up to 576 atoms and 15 chemical elements for training and evaluating MLIPs. The structures were compiled from density functional theory (DFT) calculations performed in studies of solid-liquid interfaces, with most configurations originating from ab initio molecular dynamics (AIMD) simulations. Each record contains atomic species, positions, simulation cell, periodic boundary conditions, potential energy and atomic forces. SoLiD26 includes aqueous coinage metal interfaces, electrode-electrolyte systems, and selected bulk reference structures, calculated with VASP using the PBE functional and D3 dispersion corrections. We describe the data ingestion and preparation pipeline used to construct the dataset. The application of SoLiD26 for training and evaluating MLIPs is demonstrated with a suite of MACE models on a simple training, validation and test split. The dataset enables development and benchmarking of MLIPs for structurally and chemically heterogeneous solid-liquid interfaces.

In this work, we consider the problem of uncertainty estimation of the results obtained through theoretical calculations using a composite quantum chemistry scheme. Each component of the composite scheme carries its own individual uncertainty, originating principally from a finite size of the basis set used in the calculations. Of practical interest is the total uncertainty, i.e. the uncertainty of the sum of all components. We first show that the conventional error propagation rules that treat each component as a statistically uncorrelated variable are not well-justified in practice. To remedy this, we propose a method of estimating the total uncertainty of the composite scheme which does not rely on the assumption that the components are independent. It is formulated as a series of random walks with different starting values that represent the uncertainty of each component of the composite scheme. This method is first applied to two example composite schemes for the water dimer and the nitrogen molecule that illustrate its most salient features and allow for a deeper analysis. Next, the method is tested for a larger set of interaction energies from the S66 dataset for which trustworthy reference data are available and the error can be assessed unambiguously. It is shown that the method provides reliable and reasonably tight uncertainty estimates at an arbitrary predefined confidence level required in a given application. While the focus of the paper is on composite schemes, we believe that the general idea can be useful more broadly in computational chemistry and physics.

A central challenge in molecular modeling is reconciling simulations with experimental observables, as force-field inaccuracies can distort equilibrium populations and long-timescale kinetics. While time-dependent restraints can improve simulated kinetics, time-dependent structural observables remain limited. Thus, most experimental data are time-averaged, informing equilibrium ensembles but not directly dynamics. Although ensemble refinement can improve thermodynamic accuracy, incorporating time-averaged observables into kinetic models remains difficult. Here, we introduce BICePs-reweighted Reversible DeepMSMs, combining Bayesian Inference of Conformational Populations (BICePs), variational learning of Markov processes, and maximum entropy (MaxEnt)/maximum caliber (MaxCal) principles to infer consistent thermodynamics and minimally perturbed kinetics. At its core is a reversible DeepMSM prior, obtained by applying an orthogonal transformation post-hoc to a pre-trained dynamics model (e.g., VAMPnet). This preserves the eigenspectrum exactly while yielding a valid transition matrix that is nonnegative, row-stochastic, and reversible. The reweighted model refines stationary populations, transition dynamics, and state-conditioned configurational landing densities. We validate the approach on a quadruple-well toy system and alanine dipeptide using backbone dihedral angles and J-coupling constants. In both systems, perturbing the observables induces predictable changes in the equilibrium ensemble and corresponding inferred kinetics. The resulting relaxation timescales and dynamical modes closely agree with analytical and MaxCal reference models. Furthermore, a diffusion-based generative model trained on MaxEnt landing densities produces physically realistic alanine dipeptide trajectories that reproduce thermodynamics and kinetics while satisfying the imposed experimental restraints.

Several researchers have reported an atypical inversion of the lowest singlet ($S_{1}$) and triplet ($T_{1}$) excited states in nitrogen-substituted planar hydrocarbons. Contrary to Hund's rule of maximum multiplicity, poly-aza derivatives of phenalenyl, such as cyclazine and heptazine, have long been known to possess a negative singlet-triplet gap, $\Delta E_{ST}=E(S_{1})-E(T_{1})$. Using the well-known Pariser-Parr-Pople (PPP) model, a semi-empirical, wave-function-based Hamiltonian for correlated $\pi$-electrons, we investigate the singlet-triplet gap in ten N-substituted phenalenyl motifs. Two schemes of long-range Coulomb interactions are considered, and key parameters, including the on-site orbital energy, Hubbard U, and hopping integrals, are systematically tuned to benchmark the model for reliably estimating negative singlet-triplet gaps. Full configuration-interaction (FCI) method is employed to incorporate electron-correlation effects. Ground-state and $T_{1}$ absorption spectra are subsequently calculated using the optimized PPP parameters that best reproduce the available experimental data. For validation, first-principles TDDFT calculations using various exchange–correlation functionals are also performed. Conventional adiabatic TDDFT fails to reproduce the inverted ($S_{1}-T_{1}$) ordering, indicating that the inclusion of the correlation driven multiconfigurational effects are essential for describing it. We find that the singlet TDDFT ground-state absorption spectra reproduce several qualitative features of the PPP-FCI spectra, although the TDDFT excitation energies are systematically higher. Detailed analysis of the ground-state and $T_{1}$ absorption spectra reveals significant configuration mixing, providing a comprehensive characterization of the optical response of the N-substituted phenalenyl derivatives studied.

Weak, non-covalent interactions such as electrostatics, induction, and dispersion are commonly treated as distinct physical mechanisms, often using separate models and approximations. Here, we present a unified quantum-electrodynamical framework in which all weak interactions between neutral or charged subsystems emerge from a single interaction Hamiltonian and are expressed entirely in terms of electromagnetic Green functions. Starting from macroscopic QED in arbitrary linear environments, we derive compact master formulas that generate permanent electrostatic, mixed induction, and fully induced (dispersion) interactions on equal footing. Monopoles, dipoles, and quadrupoles are treated consistently within the same multipole expansion, leading to a common structural form in which all interaction channels are obtained from contractions of multipole moments and response functions with Green tensors. Environmental effects enter universally through dressed Green functions. The standard non-retarded free-space limits, including Keesom, Debye, and London/van der Waals interactions, are recovered as a consistency check of the formalism. Beyond this validation, the formulation shows that once the general expressions are established, different physical scenarios, such as interfaces, confinement, or polarisable continua, can be treated by direct substitution of the corresponding Green function, without reformulating the interaction for each case. This elevates Green functions to a universal organising principle for weak interactions in complex environments.

Coherent multidimensional spectroscopy will benefit greatly from near single-cycle pulses through the spectral breadth of states that can be accessed and the high frequency oscillations that can be resolved. However, the necessary cost of working with such pulses is the spectral structure arising from efficient self-phase modulation, which imparts into the signal and complicates interpretation of the molecular response. In this paper, we develop an in-operando approach to characterize and remove pump spectral features from two-dimensional spectroscopic signals. The pump spectrum is retrieved via an interferometric integrated autocorrelation measurement that runs concurrently with the experiment and thereby inherits the same time and frequency domain properties. A deconvolution procedure then suppresses noise and extracts the third-order response from the total signal. The analysis enables correction of spectral intensities and unambiguous assignment of spectral features to the molecular response. This work pushes multidimensional spectroscopy toward the impulsive limit and enables integration with other nonlinear spectroscopies such as attosecond spectroscopy - all without compromising few-cycle pulse properties.

We develop a first-principles theory for nonadiabatic surface dynamics, providing a fit-free connection between density functional theory (DFT) calculations and the effective multi-orbital Anderson-Newns (AN) theory. Our theory contains two main advances. First, we outline the multi-orbital AN theory with orbital overlap and derive closed-form expressions for the hybridization energy, electronic dynamics, and electronic friction. Second, we describe a procedure to map the DFT Hamiltonian into the effective AN Hamiltonian with nuclear-position dependence. We obtain the electronic part of the AN Hamiltonian solely from the adsorbate-projected density-of-states matrix. We then define the bare nuclear potential as the difference between the total energy and the hybridization energy. The theory is applied to H and CO on the Cu surface, where widely used assumptions about the hybridization function, including the wide-band limit, semi-elliptical forms, and separability in energy and nuclear coordinate, are found to fail, and the single-orbital description breaks down qualitatively for CO. We expect this work to be broadly useful for first-principles modeling of coupled nuclear-electronic dynamics and chemical reactions at metallic surfaces.

We present an extension of the Frenkel exciton model to incorporate exchange interactions between monomers in molecular aggregates in conjunction with {a multi-reference electronic structure approach}. Our derivation, {which combines the Frenkel exciton Hamiltonian and the single-electron pair exchange approximation}, yields a non-perturbative, variational expression for the exchange coupling that naturally excludes any basis set superposition error. The method has been implemented in OpenMolcas and enables combination with multi-reference electronic structure techniques. The main objective of the present study is to assess the role of exchange in systems with strong multi-configurational character. Illustrative examples demonstrate how the inclusion of exchange at different levels of approximation can substantially alter the magnitude and sign of intermonomer couplings and thus, for instance, potentially converting the predicted classification of the aggregate from H-type to J-type. Comparison with TDDFT-based couplings highlights significant discrepancies arising from multi-reference effects, double excitations, and Rydberg transitions. Overall, this approach advances the predictive modeling of photophysical and photochemical processes in aggregates of polyacenes, carotenoids, and other systems where multi-configurational and Rydberg states are essential.

We present a geometry-refinement workflow that adapts to the cost of an energy–gradient evaluation and the reliability of molecular topology. An initial Cartesian calculation and continuous bonding diagnostics determine whether to begin in projected Cartesian coordinates or in chemically adapted internal coordinates. Chemical perception generates local redundant measurements; sparse rank and conditioning tests combine them into nonredundant, symmetry-adapted coordinates; and a common optimizer uses the selected representation with resident or external calculators. For inexpensive gradients, the projected Cartesian route limits coordinate overhead; a single monitored internal-coordinate step can then provide chemical interpretability. When gradients are costly and topology is certified, symmetry-adapted internal coordinates reduce expensive energy–gradient calls. A quadraticized map improves localized anharmonic cases, while mixed Cartesian/internal charts cover uncertain or complex topologies. Local sparse operators and resident force-field and tight-binding calculators extend both routes to large systems. The same Cartesian calculator contract supports multilevel energy and gradient composition. The measured coordinate pipeline and recurrent resident tight-binding update show near-linear scaling over the tested size ranges; this statement applies to those qualified components, not to arbitrary electronic-structure calculations or cold starts. For certified, directly separable internal charts, the optimizer can use chemically designed Morse or Gaussian maps to quadraticize the local step problem. The quadraticized branch uses a short geometry-local history, while delocalized charts remain unchanged. This reduces iterations in difficult minima without changing the electronic potential or coordinate chart.

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

The application of machine learning to molecular property prediction has become increasingly prevalent in drug discovery, yet most models operate as black boxes, returning a prediction without revealing which structural features drive it. MolExplain addresses this gap by combining property prediction with sub-structure level visual explainability in an interactive web interface. The system featurizes molecules as Morgan fingerprints, classifies them using a trained XGBoost model, and applies SHAP attribution to produce a smooth heatmap overlay indicating which regions of the molecule contribute for or against the predicted property. Applied to cyclic peptide membrane permeability, the tool's attribution independently recovers the known role of backbone N-methylation in improving passive membrane diffusion, consistent with established chemistry. While demonstrated on cyclic peptides, the framework is designed to generalize to other molecular properties, positioning MolExplain as a platform for interactive, explainability-driven molecular design.

Padé resummation of the correlation energy from Møller-Plesset (MP) perturbation theory is not size-consistent and is therefore seldom used. We present an orbital-based Padé resummation of the MP2 and MP3 self-energy operators, whose trace yields a size-consistent correlation energy. We find that damping the third-order same-spin contribution leads to substantially increased accuracy, with the resulting same-spin damped orbital Padé resummed (SSD-OPMP3) approach attaining near-chemical accuracy (mean absolute error $\sim$ 1 kcal/mol) for total atomization energies of non-multireference molecules in the W4-17 dataset against the gold standard CCSD(T) method. Excellent performance surpassing CCSD is also obtained for other molecular datasets such as those containing barrier heights, noncovalent interactions, dipole moments, static polarizabilities, and transition-metal chemistry. SSD-OPMP3 is thus a highly accurate electronic structure method with the same noniterative $O(N^6)$ cost as MP3, filling a gap in the cost/accuracy hierarchy of wavefunction methods.

We reply to the Comment by Surjuse, Deng, Asadchev, and Valeev [arXiv:2603.16989] on our article "Efficient implementation of the superposition of atomic potentials initial guess for electronic structure calculations in Gaussian basis sets" [J. Chem. Phys. 152, 144105 (2020)]. We do not disagree with their work, but point out that they appear to have misinterpreted a statement in our original abstract: our suggestion to evaluate the matrix elements of the superposition of atomic potentials (SAP) using two-electron integral code was made on grounds of portability rather than as a recommendation against a dedicated one-electron implementation. Such an implementation is in fact already available in existing codes - in Dirac, for example, where a Gaussian SAP potential is formally indistinguishable from a Gaussian finite-nucleus model at the level of the nuclear attraction integral routine. We further note that both routes reduce to the same three-Gaussian overlap kernel through the Obara-Saika Laplace transform, and that SAP itself enjoys a rigorous theoretical justification through Theophilou's theorem on spherical densities and its extensions by Nagy.

Molecular properties govern how molecules interact with one another or external fields. Accurate molecular properties are essential for constructing intermolecular interaction models, and to achieve high accuracy we need to go beyond leading-order (dipolar) terms. This work presents CCSD(T) references for 73 small non-spin-polarized molecules for molecular dipole and quadrupole moments, and dipole-dipole and quadrupole-quadrupole polarizabilities. Using these results, we provide a holistic performance analysis of molecular properties computed with HF, MP2, CCSD, and a wide range of density-functional methods. Additionally, we investigate in detail what levels of basis sets are required to simultaneously describe all properties in the data set. The best-performing density functionals for all four properties are the asymptotically cor- rected hybrid GGAs B97-3-AC, closely followed by PBE0-AC. Surprisingly, modern meta- GGAs and range-separated functionals perform inconsistently, with large errors in the quadrupo- lar properties. These results have direct implications for methods for intermolecular interac- tions, such as symmetry-adapted perturbation theory based on density functional theory, or for the quality of properties and interactions in machine-learning datasets. Finally, for DFT methods, the Jensen aug-pcseg-2 basis set is a computationally efficient alternative to more established basis sets, but for generating correlated references, third-row elements benefit from core polarization, and double augmentation is strictly necessary for quadrupole-quadrupole polarizabilities.

We develop an operator formulation of statistical quasiparticle theory for open quantum systems with nonlinear system-bath coupling, extending beyond the linear coupling setting of the standard Gaussian influence functional formulation. For Gaussian baths, the statistical quasiparticles, termed dissipatons, are explicitly constructed as collective operators in the thermofield bath space. Our construction provides a microscopic operator basis for the dissipaton algebra and generates hierarchical equations of motion directly from the Liouville equation. Nonlinear interactions are incorporated through a recursive generalized Wick's theorem, enabling automatic generation of hierarchical equations for polynomial couplings. The resulting framework connects explicit bath operator representations to nonperturbative, non-Markovian dynamics and unifies the treatment of linear and nonlinear system-bath interactions.

Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work, we introduced the hybrid Enhanced Stokes-Einstein (ESE) model, which advanced the state of the art in the physically consistent prediction of self-diffusion coefficients of solutes at infinite dilution in pure solvents by integrating the Stokes-Einstein equation with machine learning (ML). Here, we extend this approach to concentration-dependent self-diffusion coefficients and multicomponent solvents with HADES. This hybrid architecture leverages a deep-set neural network to connect pure-component and mixture prediction within a single framework. HADES predicts self-diffusion coefficients in liquid mixtures with any number of components at any composition and temperature. The only required inputs are SMILES-encoded molecular structures of the components and the pure-component viscosities, making the method broadly applicable. Trained and evaluated on a comprehensive dataset of 2526 data points for 600 systems, HADES significantly outperforms benchmark prediction methods. The trained model and its source code are fully disclosed, and the application is available via an interactive website https://ml-prop.mv.rptu.de/.

We determine the maximum stable volume of bubbles attached to flat surfaces as a function of contact radius, contact angle, and capillary length. By solving the Young-Laplace equation using a shooting method, we calculate equilibrium bubble shapes and determine when bubbles detach through three distinct modes: necking at the cavity, sideways instability, and spreading followed by necking on the surrounding surface. Our predicted detachment volumes agree well with experimental measurements of electrolytic, boiling, and carbonated bubbles, as well as pendant drops. These results bring together and extend previous analytical estimates and, by symmetry, also apply directly to pendant drops.

Relaxor ferroelectrics retain crystalline order while exhibiting glass-like dielectric and thermal responses. Using atomistic models trained on first-principles data, we investigate these responses in the prototypical relaxor Pb(Mg_{1/3}Nb_{2/3})O_3 (PMN). Compositional disorder couples elastic motion to electric polarization, producing a continuum of polar vibrational excitations, unlike the discrete polar optical modes of conventional ferroelectric PbTiO_3. The simulations qualitatively reproduce the temperature dependence of the low-frequency susceptibility, while the calculated far-infrared spectra agree closely with experiments. At low frequencies, disorder in PMN generates a boson peak comprising excess phonon-like modes and quasi-localized excitations. The latter exhibit a quartic frequency dependence of their density of states and pronounced anharmonicity, as in structural glasses. These excess modes account for PMN's anomalous behavior in the specific heat for temperature higher than 5 K. These results connect the dielectric, optical, and thermal anomalies of a crystalline relaxor through its disorder-induced polar excitation spectrum.

Machine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide complexes from the SIMDAVIS 1.2.1 database we compared three increasing levels of structural description: tabular features of the coordination site, continuous symmetry measures of the coordination polyhedron, and the complete 3D arrangement of atoms. All three converge to an accuracy near 76%, only slightly above the 71% of the single rule "predict SIM for Dy3+". To explain the failures, we combined multireference ab initio calculations with an inspection of the structures behind the high-confidence errors. The SIMs missed by the geometric models are field-induced relaxers whose ground Kramers doublets are prone to tunnelling, a property invisible to geometric descriptors. Many false positives contain several lanthanide centers or radicals, so their relaxation is collective and outside the single-ion picture. The electronic-structure and connectivity information needed to identify SIMs is therefore not accessible to geometric methods alone. Geometric models remain useful: restricting the screening to compounds with high prediction confidence raises the accuracy to 88% while retaining 48% of the dataset. Building on the analysis of the failures, we propose a strategy that combines simple filters for nuclearity and for radicals with ligand-field descriptors from ab initio calculations.

Retrosynthetic planning advances through decisions that reshape the remaining chemical problem: a locally plausible disconnection can leave precursors whose chemoselectivity constraints complicate the rest of the route. Existing planners often channel model proposals through search or template procedures, leaving open whether a general-purpose large language model (LLM) can itself sustain and revise route strategy. We developed Rachel, a stateful environment that executes and checks LLM-directed chemistry but prescribes neither a search policy nor a stopping rule. Without supplied reference routes or route-level solutions, GPT-5.5 achieved strict closure for 111 of 120 PaRoutes120 targets and 24 of 25 targets in the separate RF25 difficult-target cohort. RF25 was drawn largely from studies published after GPT-5.5's reported knowledge cutoff. Closure required complete routes and independent source resolution of every terminal precursor after planning. On a shared PaRoutes subset, forward-model support exceeded that of most comparator methods, and Rachel received the highest mean overall route score from both method-blinded LLM evaluators. Recorded trajectories showed continued model-proposed chemistry, with revised strategies carried into subsequent steps. Replacing LLM route decisions with fixed policies reduced strict closure to 6-15/120 despite continued local chemical execution; restricting planning support also reduced closure in RF25. Within Rachel, a general-purpose LLM coordinated successive chemical choices and revised its strategy as earlier decisions reshaped the remaining problems.

We present a framework to advance analytical theory in the area of soft matter and multiphysics using assistance from AI. The framework emphasizes (i) setting "honesty rules", (ii) focusing on a few key papers that are used to formulate a problem of interest, (iii) specifying checks a priori, (iv) using only one task per session and keeping a running log of tasks, checks, corrections, and sessions so that a mistake is not propagated through a project, and (v) ensuring all decisions are approved by humans while AI focuses primarily on execution. The framework is applied to two distinct problems to create new knowledge, including (i) diffusiophoresis of a spherical particle in the presence of an arbitrary number of electrolytes at arbitrary Debye lengths, and (ii) analytical description of I-V curves, including overlimiting current, in bipolar membranes without including the water dissociation kinetics. Both of these problems are advanced analyses and would each typically require several months to a year's worth of effort, but were solved in a matter of a few days. However, the increased efficiency should be cautiously navigated to avoid cognitive offloading and ensure that AI-assisted results are used to extract human-readable insights instead of just reporting a new mathematical formula. The usage of AI is transparently presented. Finally, we also share brief thoughts on how to integrate AI tools in theoretical research during graduate education.

Attosecond charge migration (CM) is an ultrafast process occurring when a molecule is irradiated by ultrashort laser pulses, creating a localized hole. The hole propagates rapidly through the molecule, generating electric currents and transferring charge across the molecular backbone. CM is a key ingredient in solar energy conversion, photosynthesis, and radiation damage. Despite its importance and intensive research, the fundamental physical and chemical mechanisms of CM remain not fully understood. Especially, a deeper insight into the role correlations play in the dynamics, and which chemical attributes determine CM timescales, is needed. Here we study with ab-initio time-dependent density functional theory CM in the benchmark molecule, BrC$_4$H. We thoroughly explore CM under different initial conditions at the electronic and structural levels, including with theories of varying degrees of electronic correlations. We uncover a universal behavior where the hole moment (connecting to experimental observables) dominant frequency is roughly independent of all of these characteristics. In contrast, the timescales of the hole density evolution do vary with the chemical conditions and level of correlations. Employing a semi-analytical theory that reconstructs the hole moments in the cationic reference frame, we show that the attosecond timescale of CM is determined by molecular dipoles that filter out specific frequency responses with an analogy to optical selection rules. Our results provide essential insight into CM physics, which should be useful for interpreting attosecond experiments and engineering CM timescales by tailoring transition dipoles.

Penning ionization allows in situ generation of ionic complexes to launch vibrational relaxation and dissociation dynamics for rare gas–H$_2^+$/HD$^+$ complexes. The ensuing translational kinetic energy release (TKER) spectra characterizing the diatomic cation are a particularly sensitive way to probe the long range part of the intermolecular interactions. TKER distributions from quasi-classical trajectory (QCT) simulations for the Ne–H$_2^+$ and Ne–HD$^+$ complexes quantitatively match measured positions and relative intensities of most experimentally observed features and are also on par with time-dependent quantum simulations. Specifically, the QCT-simulations allow to separate signatures arising from H$_2^+$/HD$^+$ relaxation into different final vibrational states, such as for $(v=2) \rightarrow (v' = 0/1 )$. Trajectory-resolved QCT analysis also provides mechanistic interpretations of the spatial and time-dependent dynamics underlying the TKER distributions. Rare gas–H$_2^+$/HD$^+$ complex lifetimes directly correlate with the final rotational quantum number $j'$ of the diatomic. Low-$j'$ products are associated primarily with an initial axial approach of the rare gas atom, whereas formation of higher-$j'$ states predominantly results from an initial T-shaped approach geometry.

Changing solvent inertia changes the kinetic part of a molecular Hamiltonian and hence its flow in phase space, without requiring a change in the potential energy surface (PES). We study how this affects product probability in a solute-solvent model with two degrees of freedom. Initial conditions are chosen at the same total energy for each solvent mass. For this family, the deterministic outcome changes from reaching products to returning to reactants at a threshold mass. The initial condition at that mass belongs to the stable manifold of the transition-state periodic orbit. Weak Gaussian forcing of the solvent momentum replaces the abrupt change in outcome by a continuous change in product probability over a mass interval proportional to the square root of the noise strength. The deterministic threshold and a linear response calculation predict the position and width of this probability change without fitting the stochastic simulations. Monte Carlo results at three noise strengths agree with the prediction within sampling uncertainty. The calculation connects solvent inertia to product probability through the geometry of the full Hamiltonian flow, rather than through a change in the PES.

Machine-learned interatomic potentials (MLIPs) have emerged as a promising alternative to classical force fields and first-principles theory for predicting the properties of chemical and material systems. Many MLIPs are graph neural networks with O(3)-equivariant features, whose accuracy comes at substantial computational cost. Here we introduce the edge cluster expansion (ECE), an analogue of the atomic cluster expansion in which the environment is expanded around edges between atom pairs rather than single atoms, and build upon it to develop the line-graph neural network ECENet. ECENet uses O(2)-equivariant features that persist on the edges between atoms, giving it natural access to O(2) operations that are cheaper and less restrictive than their O(3) counterparts. ECENet performs at the state of the art on the MD22 benchmark and lies on the accuracy-cost Pareto frontier when trained on the SPICE-MACE-OFF dataset. Furthermore, with long-range electrostatics implemented via latent Ewald summation, ECENet accurately predicts molecular dipole moments and the infrared spectrum of liquid water. The frontier ECENet architecture establishes equivariant edge-centered representations as an efficient and physically expressive foundation for equivariant MLIPs.

Recent advances in the variational quantum eigensolver (VQE) have strengthened its promise for molecular electronic-structure calculations on near-term noisy intermediate-scale quantum (NISQ) devices. However, barren plateaus (BPs) remain a major obstacle to scaling VQE, as vanishing gradients can severely obstruct optimization at large scale. While most BP studies focus on randomly initialized circuits and global gradient behavior, practical quantum chemistry VQE typically uses chemically motivated initializations and local optimization around HF- or MP2-derived states. Although chemically informed initializations are widely expected to improve VQE trainability, how they mitigate local barren plateaus remains unclear. We therefore study the occurrence of local barren plateaus in molecular UCCSD-VQE, considering both chemically motivated initialization points and iterates along the corresponding optimization trajectories. In the benchmarks considered here, chemically initialized starting points and variational trajectories show polynomial decay of local gradient variance with system size instead of the exponential BP-like decay observed for random controls, indicating that chemical initialization mitigates barren plateaus in the local optimization regions and highlights the trainability advantage of chemically motivated VQE strategies.

Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them from limited simulation data remains a challenging, important problem. Separate short trajectories may sample different metastable states without capturing transitions or establishing their relative equilibrium populations. For unbiased trajectories generated with the same Hamiltonian at a single temperature, alternate methods based on histogram reweighting cannot correct this imbalance. Here we present SPIBER, which combines the State Predictive Information Bottleneck (SPIB) with Generative Flow Networks (GFlowNets). SPIB uses deep learning to approximate slow degrees of freedom through a past-future information bottleneck, retaining information needed to predict future metastable states. We show that this compression limits conditional entropy variations in populated regions, allowing conditional mean potential energies, which are much easier to calculate, to be used to approximate free energy differences. Given sufficient local sampling to estimate these energies, they define the target distribution for GFlowNets, energy-based generative samplers that sample according to estimated thermodynamic stability rather than observed populations. For a particle in a radial double-well potential, for alanine dipeptide, and for the nine-residue peptide AIB9, SPIBER recovers free energy differences between sampled metastable states to within one thermal energy unit of reference values. The method combines collective-variable learning and free energy estimation in up to four latent dimensions, without requiring converged state populations or additional molecular dynamics simulations.

The persistence of beta-blockers like atenolol in aquatic environments necessitates efficient remediation strategies, such as enzymatic biodegradation. This study employs reactive molecular dynamics (rMD) simulations to investigate the initial degradation steps of atenolol by the oxidoreductase 9CL6 ammonia monooxygenase. Using the ReaxFF reactive forcefield within a simulated aqueous environment, the degradation processes were modeled at 300 K and 350 K over 6.5 nanoseconds. Results indicate initial degradation rates of 24% at 300 K and 18% at 350 K, with each enzyme molecule degrading approximately 20 atenolol molecules before reaching a saturation point. Mass spectrum analysis reveals the primary formation of C8 and C6 fragments at both temperatures. However, at 300 K, additional intermediate fragments (C1, C3, C11, C13) are observed, which are completely bypassed at the higher temperature of 350 K. Furthermore, increasing the temperature to 350 K accelerates the initial degradation phase from 2 ns to 0.5 ns, despite yielding a slightly lower overall degradation rate. Ultimately, these atomistic insights provide a foundational computational methodology for predicting biodegradation rates and pathways of pharmaceutical contaminants at the molecular scale.

Quantum tunnelling plays a central role in the structure and spectroscopy of hydrogen-bonded systems, and its sensitivity to isotopic substitution provides a stringent probe of the underlying potential-energy landscape. Despite extensive numerical studies, many high-level approaches tend to obscure the simple physical relationships linking effective mass, barrier geometry, and tunnelling amplitudes. Here, we develop a Cornell-type analytical–numerical framework to describe proton and deuteron tunnelling, combining a semi-analytical localized wavefunction ansatz with numerical solutions of the one-dimensional Schrödinger equation. The resulting tunnelling splittings exhibit an exponential dependence on the square root of the effective isotope mass,$\ln(\Delta E)\propto -\sqrt{\mu_{\mathrm{eff}}}$, in agreement with semiclassical Wentzel–Kramers–Brillouin (WKB) theory. Comparison with multidimensional reaction-space calculations for the formic acid dimer shows that this scaling persists in fully coupled 3D and 5D quantum models, yielding an empirical relation $\ln(\Delta E)= -1.75\sqrt{\mu_{\mathrm{eff}}}+2.60$. The present framework provides a transparent and computationally efficient approach for quantifying mass-scaling and tunnelling dynamics in hydrogen-bonded and other double-well systems.