Papers

A workflow is introduced for constructing accurate, wavefunction-derived Kohn-Sham (KS) exchange-correlation (XC) energies across atomic and molecular species. Correlated total energies and densities are obtained from configuration interaction wave functions in a polarized triple-zeta basis, cc-pVTZ, and each wavefunction density is then mapped onto a KS determinant. A composite correction adds valence and core basis functions (cc-pVQZ and cc-pCVTZ), plus a two-point Riemann extrapolation to approach the complete-basis-set limit. The workflow is applied to 100 closed-shell atomic and molecular species composed of main group elements to form the XC100 data set. The resulting KS densities closely reproduce the correlated densities, with a median L2/Ne difference of 1.42 X 10^{-5}, while the composite corrections recover substantial correlation energy beyond the cc-pVTZ reference. Comparison with conventional and machine-learned density functional approximations gives insight into the quality of Exc across different types of models for XC. In all, this paper's workflow demonstrates a practical route for generating XC reference data for the assessment and development of density functionals.

Under vibrational strong coupling (VSC), molecular vibrations hybridize with an optical cavity mode to form polaritons, offering a route to modify chemical and material properties without external driving. In this work, we develop a machine-learning interatomic potential (MLIP) based framework to study VSC inside optical cavities. By using the cavity Born-Oppenheimer approximation and treating the photonic degrees of freedom as an effective electric field, we provide a framework that can describe VSC solely based on the electronic ground-state potential energy surfaces (PES), electronic dipole moment, and polarizability, all quantities obtained outside the cavity. We train PES, polarization, and polarizability models to drive the molecular dynamics (MD) simulations of systems under VSC. We demonstrate the approach for both a single CO$_2$ molecule and liquid CO$_2$, showing that the polarizability renormalizes the effective cavity resonance: at fixed cavity frequency this renormalization renders the Rabi splitting strongly asymmetric, while with renormalized cavity frequency the symmetric splitting is capped by polarizability screening. The collective Rabi splitting of the liquid is connected quantitatively to the single-molecule splitting involving the $\sqrt{N/3}$ orientational enhancement and the local-field enhanced effective charges of the coupled vibration, while the equilibrium pair structure of the CO$_2$ liquid remains unchanged. These simulations are the first MLIP-driven simulations of collective VSC in the condensed phase and open further pathways to the exploration of chemical effects under VSC.

Empirical reactive force field (RFF) molecular dynamics enables atomistic simulation of bond breaking, bond formation, charge redistribution, and structural evolution in chemically complex systems. The ReaxFF method is arguably the most popular and transferable of the currently available RFF methods. However, routine use of ReaxFF often requires substantial manual effort to prepare inputs, interpret engine-specific outputs, organize simulation artifacts, and develop custom analysis scripts, limiting reproducibility and scalability. Here, we present ReaxKit, a modular Python toolkit for preparing, parsing, analyzing, and managing ReaxFF molecular dynamics simulations. ReaxKit uses a separation-of-concerns architecture that distinguishes engine-specific input/output handling, canonical domain data models, scientific analysis, workflow orchestration, presentation, storage, and graphical interaction. Engine adapters convert outputs from supported simulation environments into typed, engine-independent data structures, allowing analysis modules to operate independently of native file formats. User requests are executed through consistent command-line, functional Python, and browser-based graphical interfaces, while a dedicated workspace preserves raw data, normalized datasets, analysis settings, results, logs, caches, and provenance information. Representative applications demonstrate the breadth of the toolkit, including automated generation of elastic and equation-of-state training data from Materials Project structures and mechanical properties, characterization of active sites and local structural environments, and execution of simulation campaigns through YAML-defined study workflows. These capabilities show that ReaxKit supports all essential stages of the ReaxFF workflow.

The inclusion of dispersion effects is important in density-functional theory (DFT) to model non-covalent interactions correctly, a task that is essential in many applications of the theory. Many dispersion functionals have been proposed in the past. The exchange-hole dipole moment (XDM) model combines the simplicity of a damped pairwise asymptotic expression for the dispersion energy with a theory-grounded approach to calculate the dispersion coefficients. XDM is, arguably, the most accurate dispersion correction for the description of molecular crystals and it has been thoroughly tested for other applications across a wide range of chemistries. Here, we address the two main shortcomings of XDM. First, XDM relies on the use of experimentally determined free-atom polarizabilities. Second, because the XDM atom-in-molecule properties (volumes, polarizabilities, exchange-hole dipole moments) use the Hirshfeld partition method, XDM describes systems with large atomic partial charges, like alkali cations or halide anions, poorly. We propose neXDM, a non-empirical variant of XDM that removes the experimental parameters by using the Kirkwood polarizability formula, thereby making neXDM a pure meta-GGA dispersion functional. In addition, following previous work by Bučko et al. on the similar Tkatchenko–Scheffler (TS) method, we replace the Hirshfeld partitioning with its iterative counterpart. The performance of neXDM is shown to be on par with XDM in standard molecular and crystal benchmark sets, and greatly improves the modeling of ionic systems. The new neXDM method sets a new record for the best dispersion-corrected generalized-gradient approximation (GGA) functional for molecular crystal lattice energies in the X23 set (0.700~kcal/mol).

Accurate simulations of electrochemical interfaces require the simultaneous treatment of constant-potential conditions, nuclear quantum effects, and sufficient configurational sampling. Integrating these capabilities within a general and efficient molecular dynamics (MD) framework remains challenging. In this work, we implement fix uvt and fix pimd/uvt in LAMMPS for constant-potential classical MD and path integral molecular dynamics (PIMD), respectively. We organize the class hierarchy to reuse LAMMPS's existing Nosé–Hoover chain thermostat routines and share nuclear propagation routines across PIMD integrators. A common interface connects these integrators to models that provide the electron-number derivative of the potential energy. We present three examples with accompanying input commands to guide users through constant-potential classical MD, thermostatted PIMD, and constant-potential PIMD, covering analytical models, liquid water, and electrochemical interfaces described by machine learning potentials. This work provides practical tools and guidance for large-scale constant-potential simulations incorporating nuclear quantum effects.

Orbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offering a route to scalable electronic structure calculations. Machine-learned orbital-free density functionals have recently achieved promising results on small organic molecules, predicting energies with sub-millihartree accuracy. However, their convergence in density optimization remains sensitive to hyperparameter tuning and architectural choices. Here, we extend the recently introduced (weak) surrogate functional framework - designed to predict ground-state electron densities only - to also yield their energy, resulting in "strong" surrogate functionals. We find that these learned functionals enable stable convergence across all tested neural network backbones, reducing electron density errors relative to the Kohn-Sham reference by an order of magnitude compared to previous OF-DFT methods. More importantly, the predicted energies are competitive with state-of-the-art machine-learned interatomic potentials (MLIPs) trained only on energies, while exhibiting superior generalization to larger, unseen systems.

This study examines phosgene (COCl2) adsorption on pristine and noble metal-doped (Ag, Au, Pd, Pt) B12N12 nanocages using dispersion-corrected density functional theory [B3LYP-D3(BJ)]. Boron-site substitution narrows the HOMO-LUMO gap far more than nitrogen-site substitution (69-82% vs 41-67%) and was adopted throughout. All systems were fully optimized; multiple starting geometries converged to two stable minima per dopant, X-O and X-Cl. Pristine B12N12 binds phosgene weakly (Eads = -12.8 to -24.9 kJ mol^-1). Doping strengthens binding, spanning -14.0 to -60.7 kJ mol^-1 after counterpoise correction, with platinum exhibiting the highest affinity and structural stability. Natural population analysis indicates phosgene donates at most 0.33 e, and QTAIM classifies all cage-adsorbate bond critical points as closed-shell or intermediate, confirming physisorption. Vibrational frequency analysis confirms all structures as true minima and reveals that the adsorption entropy penalty (59-161 J mol^-1 K^-1) is decisive. Under Grimme's quasi-harmonic approximation, pristine cages fail to bind phosgene (Delta G = +23.8 to +27.9 kJ mol^-1), whereas only Pt-O (Delta G = -8.8 kJ mol^-1) and Pd-O (-7.2 kJ mol^-1) adsorb phosgene spontaneously at 298 K. Transition-state theory indicates rapid room-temperature recovery (tau = 43 ms for Pt-O), while thermodynamic desorption occurs at 69.6 deg C, ensuring a practical regeneration window. Silver-doped cages provide narrow post-adsorption gaps and high electrophilicity but cannot retain phosgene at ambient conditions. Platinum doping is therefore the most effective strategy for reversible phosgene detection using B12N12 nanocages.

Raman spectroscopy is widely used for molecular identification in biological samples, but spectral congestion often obscures key molecular fingerprints. Strategies to enrich vibrational spectra with additional controllable features are therefore desirable. We theoretically show that vibrational strong coupling (VSC) can reshape Raman spectra by reorganizing vibrational energies and intensities. Near-resonant coupling enables the resolution of quasi-degenerate vibrational modes and redistributes Raman activity among molecular vibrations, brightening otherwise Raman-inactive modes. Off-resonant coupling mediates effective interactions between vibrations, leading to tunable intensity reorganizations and spectral shifts via cavity-controlled vibrational mixing. Using a microscopic theory of Raman scattering, we show that observing Raman signals from collective VSC polaritons requires appropriate geometries for the scattering setup, the sample, and the cavity environment, helping to explain why polaritonic Raman signatures have remained challenging to observe experimentally.

We propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than constructing an interatomic potential from scratch, we refine a pretrained DFT-based uMLIP using a small set of accurate reference energies obtained from quantum computing. We demonstrate the approach for three chemically distinct applications: the Menshutkin reaction, water adsorption in the metal-organic framework HKUST-1, and CO hopping on a high-entropy-alloy nanoparticle. For the Menshutkin reaction, fine-tuning on gas-phase configurations improves the transition-state energy inside a carbon nanotube but not the product energy. For water adsorption in HKUST-1, fine-tuning with only 14 reference configurations brings adsorption thermodynamics obtained from millions of configurations sampled by Widom insertion into closer agreement with reference values. For CO hopping on an IrPdPtRhRu nanoparticle, the preference for on-top over bridge adsorption is recovered in the finite-temperature free-energy profile obtained from enhanced-sampling molecular dynamics, even though the reference data contain only energies. These results demonstrate that the proposed framework provides a practical route for incorporating quantum-computing calculations into realistic atomistic simulations and that quantum-computing reference data can improve pretrained uMLIPs.

We extend a recent physics-based matrix model for calculating PEM fuel cell impedance (doi:10.1149/2754-2734/ad6ce8) to cases of low air flow stoichiometry and functionally graded cathode catalyst layers (CCLs). We demonstrate that the matrix model produces accurate spectra and is almost three orders of magnitude faster than a model based on the standard boundary-value problem solver. The physics-based matrix model can compete with equivalent circuit models for fitting experimental EIS spectra, particularly those measured from cells with functionally graded CCL.

Chirality-induced spin selectivity (CISS) is usually associated with molecular handedness. The possible contribution of chiral phonons is less established. We study a helical tight-binding model in which local phonon angular momentum modulates spin-dependent nearest-neighbor hopping. Fewest-switches surface hopping calculations give the transmitted spin polarization $\mathrm{SP}=aC+b\mathrm{PH}$. Here $C$ is the molecular chirality and $\mathrm{PH}$ is the phonon chirality. A mirror symmetry reverses $C$, $\mathrm{PH}$, and $\mathrm{SP}$ simultaneously. This symmetry excludes both a chirality-independent offset and a $C\cdot\mathrm{PH}$ term. The phonon contribution can therefore enhance, cancel, or reverse the molecular CISS signal.

Estimating a ground-state energy and preparing a ground state both split into a front-end that supplies an approximate state $|\chi\rangle$ and a back-end that reads or refines it. We argue, and measure at every step, that the only thing crossing that split is the front-end's spectral weight distribution $p_n=|\langle E_n|\chi\rangle|^2$ over the eigenstates of the problem Hamiltonian $H_P$. Everything downstream is priced in it, so the front-end is a swappable module, and we swap it seven times without altering anything to its right. We call the arrangement WARP. Four back-ends spend that distribution. A readout (A) samples $g(t)=\langle\chi|e^{-iH_Pt}|\chi\rangle$ on the device and extracts true eigenvalues by classical signal processing, returning an energy below the variational bound of the state it was handed, from a signal $50\times$ shorter than a Fourier transform needs. Three routes spend the same account on a state instead. An adiabatic schedule (B) has a minimum gap set by the overlap; a discrete amplification (C) is cheaper in depth but must halt at an index the overlap fixes, so it needs a measured value and not a bound; a recursion (D) of depth $\mathcal{O}(3^m)$ raises the question of whether it can be run at all. The interactions are the substance. On stretched N$_2$ a sample-based diagonalisation reports a variational bound 79 mHa high, fifty times outside chemical accuracy; reading the state it has already prepared returns $E_0$ to $1.4\times10^{-6}$ Ha, three orders of magnitude inside it, and needs no new front-end and no new circuit. And because the weights price everything, they must be measurable when scarce, so we beat a weak reference against a strong one and recover overlaps four orders of magnitude below the shot-noise floor. We map the operating envelope throughout, and state the limitations.

Electrospray ionic liquid ion sources operating in the pure ion regime emit metastable, low solvation number ion clusters whose post-emission dissociation modifies plume composition, energy distributions, and diagnostic observables. While field-free measurements support first-order, thermally activated decay on microsecond timescales, the acceleration region is characterized by rapidly varying electric fields and nanosecond residence times such that dissociation becomes strongly field-enhanced. In this work, microcanonical ensemble molecular dynamics (MD) trajectories are used to infer dissociation kinetics of EMI-BF4 ion clusters under controlled internal energy and applied electric field. Positive and negative dimers, trimers, and tetramers are simulated at temperatures between 600 to 1000 K and uniform fields of 10^6 to 10^9 V/m. Dissociation lifetimes are extracted using a connectivity-based fragmentation criterion, and product channels are classified to obtain pathway-resolved branching probabilities. Across all solvation numbers, lifetimes are field independent in the weak field limit, but collapse by orders of magnitude above 10^8 V/m. Cluster polarity dependence is most prominent in the temperature-controlled regime and diminish as electrostatic work dominates. The MD simulations further reveal that field-driven changes in dissociation topology, including a transition in trimer breakup from neutral pair evaporation to charged core ion ejection, and three competing tetramer pathways involving single neutral loss, double neutral emission, and core ion ejection. The MD dataset is reduced to compact parameterizations of {\tau_n}(E,T), yielding transferable kinetic inputs for multiscale plume transport models.

Optimization methods for experimental science are often evaluated on synthetic functions that are reproducible but omit important characteristics of real experiments. We introduce RECOB (REliable Chem Optimization Benchmark, Github repository: \hyperlink{https://github.com/XieZikai/RECOB}{https://github.com/XieZikai/RECOB}), a black-box optimization benchmark constructed exclusively from data generated through physical experiments in chemistry and materials science. The suite contains 14 single-objective and two multi-objective tasks spanning chemical reactions, material formulations, electrochemical systems, continuous-flow processes, and automated laboratories. Each task provides a machine-readable specification of its decision variables, feasible domain, physical constraints, objective direction, and experimental provenance. Continuously queryable learned oracles are screened using repeated holdout validation and prespecified admission criteria, while measured-table replay enables evaluation using the original experimental responses. Under a common paired evaluation protocol, we compare ten single-objective and eight multi-objective optimization methods. HEBO achieves the best aggregate single-objective rank, while qNEHVI leads the multi-objective comparison. Model-based methods generally outperform non-adaptive baselines, although their computational overhead varies substantially. We further assess benchmark reliability using independent oracle retraining and measured-table replay. Aggregate optimizer rankings remain highly consistent across retrained oracles, while replay preserves the broad performance hierarchy using only physically measured responses. Together, these results show that RECOB can reproducibly distinguish optimizer performance as an experimentally grounded and reliability-tested benchmark for black-box optimization in chemistry and materials science.

Large language models (LLMs) have already shown strong capabilities in solving complex chemical problems expressed in natural language. Yet, many tasks performed by chemists require understanding of 3D structures of chemical compounds and reasoning about them – abilities, which, to the best of our knowledge, were neither intended by LLM developers nor tested in contemporary models. Such understanding is essential for autonomous molecular discovery and drug development, which is one of the Holy Grails of AI application to chemistry. We tested this capability in modern LLMs on 810 geometries and DFT energies of 27 small organic molecules. Strikingly, while models released before July 2025 struggled to rank conformers by stability, many recent frontier models, including GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash achieved competitive accuracy, outperforming the Universal Force Field, and GPT-6 Astra and Claude Opus 5 came very close to a modern GFN-FF force field. Analysis of models' explanations for their rankings suggests that they perform best for molecules with well-defined intramolecular interactions – hydrogen bonds – which are well quantified in scientific language; at the same time all models except the two newest – GPT-6 Astra and Claude Opus 5 – struggle with molecules governed by loosely defined concepts (e.g., ring strain), where force fields excel, suggesting that the scientific language itself might impose constraints on LLMs. Notably, a model's ability to rank conformers is strongly associated with its performance on scientific, coding, and abstract-reasoning benchmarks, suggesting that it emerged unintendedly from models training on linked but conceptually different tasks. Thanks to this generalization of mere scientific knowledge into understanding molecular structures, current frontier models offer a realistic starting point for AI-driven molecular design.

Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily obtained using automatic differentiation. Higher-order derivatives, most notably the Hessian, describe collective motion and allow the direct prediction of experimental observables, but are considered computationally inaccessible for large systems. We suggest a solution: in physical systems, interactions decay with distance, and most MLIPs build on this locality through message passing up to a finite receptive field. This implies both sparsity of higher-order derivatives and their decay with distance. This structure can be exploited using automatic sparse differentiation (ASD). We explain how to compute the sparsity pattern for MLIP derivatives and demonstrate that, for multiple foundation MLIPs, ASD computes full Hessians of large porous materials exactly, but with modest speedups at best. The larger gains come from truncated ASD: discarding small, but nonzero, Hessian entries between distant atoms yields order-of-magnitude speedups with negligible impact on predicted observables.

Understanding how collective ion transport emerges from equilibrium fluctuations is central to electrolyte statistical mechanics. Finite-volume fluctuations provide an accessible route to this information, but their interpretation is complicated because they mix wave numbers and collective fields. Here, we extend the finite-volume counting framework for inferring collective ion diffusion by combining the exact window projection with an inertial independent-particle reference, and apply it to a symmetric 1:1 solvent primitive model. The growth of ion-number fluctuations between the ballistic and plateau regimes appears nearly ideal, but this apparent ideality results from compensation between negative structural and positive dynamical excess contributions. The coupled ion-number–solvent relaxation further reveals a signed redistribution between solvent-associated and solvent-orthogonal projections that is largely hidden in the total response. The inferred collective diffusion depends on observation length and dynamical closure: measured structure alone does not systematically improve the estimate, whereas the coupled-field description reduces the high-concentration discrepancy. These results provide a basis for analyzing more realistic electrolytes with mutually coupled ion-number, charge, and solvent fluctuations.

Backbone resonance assignment is a prerequisite for most biomolecular NMR applications, yet conventional multidimensional strategies frequently fail for intrinsically disordered proteins (IDPs) and regions (IDRs) because of severe spectral overlap, rapid amide proton exchange with water, and missing sequential correlations. In many biological applications, however, complete protein assignment is unnecessary, as only a limited sequence segment surrounding a functional site is required. Here we introduce segmental backbone assignment, an assignment strategy implemented by FOcused SpectroscopY (FOSY), which concentrates experimental effort on relatively short regions while retaining the high-dimensional sequential connectivity needed for unambiguous assignments. We present a self-consistent suite of selective two-dimensional FOSY experiments that enables bidirectional assignment walks along the protein sequence through complementary forward and backward transfer schemes. The methodology employs frequency-selective polarisation transfer to replace high-dimensional experiments with sensitive and readily interpretable 2D spectra while preserving the information content of multidimensional correlation experiments. The approach is demonstrated by completing the assignment of G302-K311 segment, which is missing in the published assignment of the 441-residue human Tau protein. The approach complements conventional multidimensional or residue type-selective assignment strategies by providing an efficient means of traversing assignment interruptions and rapidly characterising functionally important segments in intrinsically disordered proteins.

Hydrogen is known to form stable compounds with a variety of simple molecular solids under high pressure. However, ammonia hydride has not been experimentally observed. Here, through a series of laser-heating diamond anvil cell (LHDAC) experiments, we observed the transparent ionic ammonia transformed into an opaque phase above 150 GPa and 1000 K. Raman spectroscopy reveals a pronounced structural change, accompanied by the emergence of two distinct asymmetric H$_2$ vibrons ($\nu_1$ and $\nu_2$). A new ionic phase produced following thermally induced dissociation and hydrogen reorganization in hot dense ammonia. The combination of experimental results and first-principles calculations identify this phase as the theoretically predicted ionic $P4_12_12$ phase of NH$_7$ , which consists of NH$_4^+$, H$^-$and H$_2$ units. Our results suggest that the formation of ammonia hydrides follows a thermodynamic pathway distinct from those of other H$_2$-containing mixtures, revealing a new ionic transformation pathway driven by hydrogen reorganization in hot dense ammonia.

Permutation invariant polynomials (PIPs), together with related polynomial-based invariant descriptors such as fundamental invariants (FIs), are widely used in constructing high-fidelity global potential energy surfaces (PESs) of molecules that contain identical atoms. Although the monomial symmetrization approach (MSA) enables fast evaluation of PIPs through recursive factorization, it leads to deeply nested computational graphs that may require large memory and are inefficient in modern automatic differentiation frameworks. In this work, we introduce JaxPIP, a JAX-based implementation that reformulates PIP/FI evaluation into tensorized linear algebra operations. By replacing recursive factorization with dense matrix operations combined with log-exp transformation and segmented summation, the evaluation becomes regular and GPU-friendly. This allows efficient execution with just-in-time compilation and enables large-scale batch evaluation of energies and forces (as well as higher-order derivatives). The resulting architecture supports ensemble simulations such as quasi-classical trajectory (QCT), path-integral molecular dynamics (PIMD), and diffusion Monte Carlo (DMC) in a fully vectorized manner. As demonstrated in examples, JaxPIP provides a practical route for efficient simulations of molecular systems with scalable GPU execution.

Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failure in weak-gradient regimes, limiting their practical use in quantum many-body physics and ab initio quantum chemistry. Here we derive an unbiased direct gradient estimator and introduce the adaptive minimum-variance phase (AMVP) estimator for neural-network variational optimization. By improving the signal-to-noise ratio of weak gradients, these methods enable reliable scientific calculations where training previously failed, while substantially reducing computational cost. The framework enables compact networks to outperform larger and fine-tuned default standard-estimator models with over an order of magnitude less GPU time on correlated flux models, and ultimately exceed the density matrix renormalization group (DMRG) accuracy. It further achieves chemical accuracy in N$_2$ bond breaking and, for the first time, in heavy-element I$_2$ with explicit spin-orbit coupling. These results demonstrate that gradient-estimator design expands the capabilities of neural-network variational methods for accurate scientific computing.

We present in this article data on the computed potential energy surface, permanent moment surface (from dipole to hexadecapole) and polarizability surface (from dipole-dipole to quadrupole-quadrupole) for the H$_2$O molecule, calculated at the level of the generalized gradient approximation (GGA) in Kohn-Sham density functional theory (KS-DFT) in the grid-based projector augmented wave code GPAW, using the charge perturbation code C-pol. The GGA-based functionals are PBE, RPBE and BLYP. The monomer surfaces are composed of systematic, symmetry-reduced deformations of the H$_2$O monomer and comprise 10,000 configurations per functional, with traceless Cartesian tensor moment values reported as data strings in the xyz file format. In addition, we provide cluster datasets for n-H$_2$O aggregates (n = 2-6), sampled from liquid-like configurations and computed with the same KS-DFT settings, where each extended xyz file encodes the total interaction energy of the cluster in the comment line. Finally, ab initio molecular dynamics (AIMD) trajectories of periodic liquid water boxes, containing between 25 to 150 H$_2$O molecules at ambient conditions, are included, furnishing time series of atomic configurations and energies that are consistent with the monomer and cluster data and suitable for validating classical and machine-learned water models.

We demonstrate physics-constrained agentic development of a meta-generalized gradient approximation (meta-GGA) functional using a large language model (LLM) to assist the search and optimization of a band-gap-oriented revision of r2SCAN. No real bonded systems were fitted, preserving r2SCAN's nonempirical philosophy. Across the finalist set, band gaps and several molecular subsets improve relative to r2SCAN; the top finalist, r2SCAN+, reduces the band-gap MAE on a benchmark comprising 24 solids from 1.26 to 0.96 eV and the aggregate MAE on 329 molecular properties from 4.79 to 4.42 kcal/mol. We first curated 78 exchange and 90 correlation candidate correction terms from r2SCAN's dimensionless ingredients, spanning polynomial terms through third degree, exponentials, exponentially damped products, and ratios. Allowing each candidate to combine one to three correction terms from the exchange catalog, the correlation catalog, or both yields about 8 x 10^5 distinct forms, making exhaustive high-throughput screening impractical. We defined the search criteria for the LLM agent using r2SCAN's exact constraints, physical norms, and the targeted iso-orbital derivative response. The agent then combined these criteria with its pretrained knowledge and accumulated search feedback to propose and refine sparse forms, prioritizing terms tied to the iso-orbital response; a second LLM critic screened proposals before deterministic verification. Compared with uniform random search, the workflow learned from prior evaluations, incurred far fewer downstream rejections (0.6% versus 24.6%), and located stronger high-response candidates: 51 agentic candidates exceeded the best random-search response of 1.263, with the overall best reaching 1.331. These results show that agentic search can support density-functional development when flexible hypothesis generation is coupled to automated physical verification.

The increasing use of generative models has made ensembles of short molecular dynamics trajectories increasingly common, creating a growing need for methods that can recover physically meaningful steady-state populations and kinetics from improperly weighted conformational ensembles. Randomized Iterative Trajectory Reweighting (RiteWeight) addresses this problem through repeated random clustering and iterative reweighting, without requiring the fixed Markovian discretization used in conventional Markov state models (MSM). However, the choice of reduced feature space in which RiteWeight performs random clustering has not been systematically investigated. Here, we compare two machine-learned representations, DeepTICA and SPIB-VAE, with linear TICA for recovering steady-state observables from flawed distributions. DeepTICA learns nonlinear coordinates by targeting slow transfer-operator eigenmodes, whereas SPIB-VAE compresses configurations into a low-dimensional latent space while retaining information predictive of future metastable states. DeepTICA provided a comparatively robust RiteWeight representation under limited hyperparameter exploration, whereas SPIB-VAE benefited more strongly from broader optimization. Moreover, a kinetic score computed from a coarse MSM at a resolution comparable to that used for RiteWeight random clustering provided a useful criterion for efficiently selecting reduced representations and their hyperparameters for RiteWeight.

Plasmonic excitation of metallic nanostructures can generate energetic carriers capable of transferring charge into nearby adsorbed molecules, providing a possible pathway for driving chemical transformations. In many theoretical descriptions of this process, charge transfer is determined by the equilibrium electronic structure of the molecule and substrate together with the time-dependent carrier distribution produced during plasmon decay. However, the intense transient electric fields associated with localized surface plasmons can also dynamically perturb the molecular electronic structure itself on ultrafast timescales. In this work, we investigate how these time-dependent fields modify the molecular spectral function and influence charge injection at catalytic interfaces. Using a model of CO2 adsorbed on Au(111), we compute the real-time molecular Green's function within a correlated frontier-orbital active space under plasmon-like driving. We find that the driving field rapidly produces transient Floquet-type replica bands in the molecular density of states, opening additional resonant pathways for hot-carrier injection that are absent without external driving or in time-local descriptions. Coupling the evolving molecular spectrum to a time-dependent hot-electron distribution described within a two-temperature Sommerfeld framework, we predict large enhancements in quasiparticle spectral overlap that governs injection during plasmon dephasing. These results suggest that dynamically generated non-equilibrium spectral structure may play an important role in plasmon-assisted catalysis and provide a framework for studying driven catalytic interfaces beyond static electronic structure descriptions.

We introduce here a new approach to the study of isolated chlorophyll pigments with the electronic spectroscopy of their deprotonated anions in a cold environment at 9K. These anions are isoelectronic to the corresponding neutral molecules, therefore their electronic properties are comparable. This comparison is pursued through this article. The electronic spectrum of the Q bands of deprotonated pheophorbide and methyl pheophorbide are recorded by resonant two photon electron detachment with vibrational resolution and show well-resolved bands extending through the visible spectrum in a very similar fashion to their neutral counterparts in solution. The results are compared with data of neutral pheophytin obtained by fluorescence line narrowing spectroscopy at 4K and resonance Raman spectroscopy and found very similar. The theoretical analysis is achieved by density functional (ground state DFT WB97XD, hybrid exchange-correlation functionals and 6-311++G(d,p) basis sets) and time dependent density functional (TDDFT) calculations. The comparison of the active orbitals in the Q transitions shows a great similarity between neutral and anionic forms together with some orbital and related level inversions. The vibronic spectra are simulated by Franck-Condon calculations to assign the dominant transitions of the electronic spectrum. Globally, this is a new perspective in the electronic spectroscopy of chlorophyll pigments that has been opened.

The static screening response of a metallic nanoparticle is encoded in its induced charge density. Integrated observables such as the dipole moment or the polarizability do not determine this density, because many different screening profiles yield the same integrals. We show that in noble-metal nanoparticles the screening charge is atomically structured, with facet, edge, and vertex sites responding differently from a smooth classical conductor. This structure survives in a surface layer of about one atomic width even when the integrated response has reached the conductor limit, and it is absent from continuum descriptions. To resolve it across size, morphology, and composition, we calibrate an atomistic charge-dipole model for Ag and Au nanoparticles directly to first-principles induced-density profiles. The calibration includes the short-range kinetic and exchange-correlation contribution to the hardness kernel that a purely electrostatic model omits, reducing the induced-density-profile error by about a factor of 2.5. A single averaged parameter set per element predicts clusters withheld from the fit and transfers without adjustment to Ag/Au core-shell, alloyed, and elongated particles. A continuous fast multipole implementation of the model scales almost linearly with the number of atoms and reaches several-million-atom particles, far beyond the reach of first-principles methods.

Active matter systems can generate mechanical stresses that deform their surroundings, providing a route to transport and shape dynamics far from equilibrium. Deformable vesicles containing active particles offer a minimal setting in which such active stresses are directly coupled to boundary mechanics. Here, we use numerical simulations to investigate a two-dimensional, fixed-area vesicle filled with chiral active particles interacting through excluded volume and polar alignment. We find that the coupling between particle chirality and vesicle deformation produces distinct modes of collective motion, including run-and-tumble-like migration, rotor-like dynamics, and persistent spinning. Most notably, we show that vesicle rotation is non-monotonic in chirality: at fixed activity, an optimal chirality maximizes the rotational velocity. We develop an analytical theory that relates the vesicle rotation to the effective torque generated by the chiral active particles. The theory identifies the competition underlying the optimal chirality and predicts a scale-invariant dependence of the rotational velocity on activity and chirality. This scaling collapses simulation results obtained over different activity strengths onto a universal curve. Our results establish a general mechanism by which chirality and collective alignment regulate the transmission of active stresses to deformable boundaries, providing a framework for understanding transport and rotational dynamics in confined active systems.

In this work we introduce the use of Quartic Force fields (QFF) potential expansions in the context of variational calculations. Such potentials are commonly employed in molecular Vibrational Second-Order Perturbation Theory (VPT2) studies, for which equations explicitly dependent on the QFF parameters exist. However, QFF are unbound potentials for more or less large displacements from the reference point and, most of the time, this prevents their use in conjunction with variational wavepacket-based calculations. In this work, we propose a general correction to QFFs and introduce a fully automated numerical approach to avoid their unbound character. Our corrected potentials, bound QFF (bQFF), do not exhibit appreciable modification of the local topography around the region of interest for infrared spectroscopy. As a consequence of this, we can affirm that the vibrational eigenstructure (eigenvalues, eigenstates) remains essentially unaltered by our correction. To illustrate their numerical stability, we have interfaced our bQFF routines in combination with to two well-established quantum simulation software packages MCTDH and ElVibRot which feature variational approaches. More specifically, our bQFFs are separable and hence directly expressible as MCTDH operators. Furthermore, concerning the size of our bQFF expansion, we show that it is possible to tensor-decompose our bQFF in Canonical Polyadic form (CP-bQFF). We use the Monte Carlo Canonical Polyadic decomposition algorithm for this. CP-bQFF results are virtually identical to uncompressed bQFF, but the computational efficiency is largely improved. Our approach paves the way for the automated variational study of anharmonic eigenstates in molecular systems within the reach of QFF-based potentials, using either time-dependent or time-independent schemes.

Advanced, orbital-dependent exchange–correlation (XC) functionals can significantly improve the description of electronic structural properties, but they substantially worsen spectral properties that are computed within standard linear-response time-dependent density functional theory frameworks. This is not a failure of the underlying Kohn-Sham states, but due to a formal inconsistency in the treatment of the dynamic density response when the non-locality of the XC potential is not taken into account consistently on the level of the full off-diagonal density matrix. To avoid these complexities, we present a non-empirical additive correction $\Delta f_\textnormal{xc}(\mathbf{q},\omega)$ to the dynamic XC kernel that re-enforces the exact f-sum rule of the non-local KS Hamiltonian within TDDFT. Comparing our new results against a representative set of accurate experimental measurements (ambient aluminum, silicon and carbon, as well as heated and compressed aluminum) reveals a dramatic improvement in all cases without any additional computational cost. The corresponding extension to the open-source GPAW code is made freely available online. We further investigate the implications of non-local pseudopotentials and show that the non-locality has an important, physically motivated effect that is indispensable to capture the correct plasmon dispersion in lieu of full all-electron simulations. In addition to being important for the estimation of a plethora of dynamic and spectral properties, our work constitutes an important step towards a universal XC functional that can be used to estimate all kinds of observables with high accuracy. Finally, we outline the potential utility of our framework for the development of advanced non-local XC functionals, and suggest a new way to rigorously verify non-local pseudopotentials against experimental measurements of collective excitations.