Papers

On-water catalysis accelerates reactions between poorly soluble organic substrates in aqueous suspensions, but its molecular origin remains debated. This Account argues that hydrogen bonding and proton transfer can both enhance charge-transfer stabilization between the organic reactants. Hydrogen bonds from surface water polarize the reacting complex, whereas protonation can perturb the same donor-acceptor interaction more strongly without requiring identical reaction pathways. We connect simulations of the water/vapor interface, finite-temperature cycloaddition studies, and energy decomposition analysis based on absolutely localized molecular orbitals. Dangling OH groups establish reactive contacts, but an increased transition-state hydrogen-bond count alone does not explain activation. Electronic analysis instead identified hydration-enhanced charge-transfer stabilization between organic partners by approximately 30%. Comparisons of related dienophiles, a protonated limiting model, and oxygen/sulfur substitution link substrate-dependent electronic response to hydration and bond formation. A hydration-induced charge-transfer response metric provides a testable molecular hypothesis, not a standalone measure of catalytic acceleration. Activation depends on differential stabilization of reactant and transition-state ensembles. Increasing the accessible reactive interface can amplify this local mechanism without requiring exceptionally strong additional electric fields, connecting conventional on-water chemistry with selected microdroplet reactions while distinguishing adsorption, concentration, and intrinsic molecular activation.

Core-ionization induced charge migration in molecular systems has been widely studied, but the influence of relativistic effects on such dynamics remains relatively unexplored from a relativistic theory perspective. This issue is particularly important because core orbitals exhibit strong scalar-relativistic and spin-orbit imprints, and these effects are further enhanced in heavy-element systems, where relativistic signatures also alter the valence orbitals. To address this, we formulate four-component Dirac-Coulomb and two-component atomic mean-field exact two-component (and non-relativistic) Hamiltonian approaches for studying charge migration within real-time time-dependent density functional theory. The time-dependent induced electric dipole moment is used as a marker of the characteristic charge-migration time scales. We apply the method to nitrosobenzene (N $1s$ electron removal), iodoacetylene (I $2p$ electron removal) and interhalogen series (I $2p$, Cl $2p$ electron removal). For iodoacetylene, the charge migration timescale is relatively insensitive to the choice of Hamiltonian but they show weak relativistic phase and amplitude modifications. However, it would be misleading to conclude that relativistic effects are generally unimportant. In interhalogen systems, we observe significant deviations between relativistic and non-relativistic dynamics. These results demonstrate that relativistic signatures in core-ionization-induced charge migration are not determined by heavy-atom core ionization alone, but by the relativistic sensitivity of the valence orbital manifold that drives core-hole screening and charge redistribution. Moreover, consistently across all systems, we observe a delay in the emergence of the relativistic imprints.

Random-batch methods accelerate molecular dynamics by replacing full interaction sums with stochastic force estimators. In Langevin simulations, however, random-batch force errors can cause artificial heating and distort equilibrium and dynamical observables, especially for small batch sizes or under weak thermostat coupling. Moreover, the magnitude of these errors can vary across particles. We introduce moment-informed force rescaling for random-batch list (Mi-RBL) and random-batch Ewald (Mi-RBE) methods. A lagged average of each particle's sampled-force intensity sets an isotropic gain before the current batch is sampled, changing the random-batch force magnitude without changing its direction. The formal finite-time analysis quantifies the bias–variance tradeoff, and an estimate of the steady-state kinetic-temperature error predicts the scaling of the kinetic error with time step and thermostat friction. In the coexistence, binary-mixture, and electrolyte tests, Mi-RBL and Mi-RBE reduce kinetic errors and more closely reproduce the mean-square displacement, radial distribution function, and charge density profiles of the reference solutions. Mi-RBE also reduces the growth rate of the kinetic error with the time step by more than half and retains the predicted dependence on thermostat friction. The tested rescaling strength decreases with increasing batch size, and the particlewise update retains $O(N)$ complexity for fixed batch size.

Wide-field mid-infrared photothermal (MIP) imaging enables high-speed, chemically specific imaging at sub-micrometer spatial resolution through several possible contrast mechanisms, including scattering change, fluorescence modulation, and phase change. Each mechanism offers distinct advantages, yet existing wide-field MIP instruments have typically been limited to a single contrast mechanism so far, requiring a dedicated instrument for each. Here, we introduce a switchable multi-contrast wide-field MIP microscope that allows the photothermal readout mechanism (based on scattering-, fluorescence-, or quantitative phase imaging (QPI)) to be matched to the sample and imaging conditions. We characterize the spectral fidelity and signal-to-noise (SNR) ratio of each mechanism using 5 \mu m polystyrene (PS) beads, and the effective spatial resolution using 300 nm PS beads. Scattering- and fluorescence-based detection achieved a sub-500 nm measured point spread function full width at half maximum, while QPI-based detection reached approximately 900 nm but provided up to 26-fold higher SNR than scattering. We further demonstrate imaging across a range of samples, including A549 cells in buffer, mouse lung tissue section, bacteria, and SU8 polymer at 1.4 and 5 \mu m thickness, with a field of view exceeding 200 \mu m. Comparison across these samples shows that each contrast mechanism offers distinct trade-offs in resolution, sensitivity, and applicability, guiding readout-strategy selection by sample type. These results establish a flexible, switchable multi-contrast platform for wide-field MIP imaging, with broad applicability across polymer and biological samples.

A framework for quantum electrodynamics of equilibrium systems is proposed that takes the Maxwell equations as foundational and links them to the structure of density-functional theory for the quantum system. By switching from purely internal observables like the one-particle density to combined internal and external quantities, the external sources and their energy are taken into account. A fully regularized functional theory emerges that avoids the usual representability problems. With the Kohn-Sham construction that links to an auxiliary uncoupled system, the ultra-violet cutoff can be removed and one arrives at a fully renormalized and non-perturbative light-matter description. Regularized forms of density-functional theory with and without magnetic fields appear as boundary cases, while the emergent physical picture reproduces the macroscopic Maxwell equations.

We present a general method for estimating entropy differences between arbitrary probability distributions using stochastic interpolants. The marginal distributions which bridge the base and target obey a continuity equation, which allows a straightforward calculation of the entropy difference in terms of an inner product of the probability flow velocity and score fields. This formulation has several advantages: (i) no computationally expensive divergence calculations of either field are required, (ii) the score field need not even be learned directly if model transferability is not required, and (iii) on-the-fly estimates are produced nearly for free during training. When tractable base distributions are chosen (e.g. Gaussian chain, ideal gas, etc.), statistical thermodynamic entropies are then immediately recovered. Notably, the analysis relies only on the definition of the Gibbs-Shannon entropy, rather than any particular statistical mechanical ensemble, so that generalized non-equilibrium entropies may be computed. The method is demonstrated on several systems of increasing complexity: (i) a 40-dimensional Gaussian mixture model, (ii) the classical XY model of N spins arranged in one dimension, (iii) the 13-atom Lennard-Jones cluster, and (iv) an active Brownian polymer.

Many proteins function through transitions between conformational states, yet rare states are rarely sampled by diffusion models trained on an equilibrium ensemble, demanding better sampling methods. We introduce METALICA, which implements Metadynamics on a pretrained diffusion model via Replica Exchange. It accumulates a bias potential along a Collective Variable, repels new samples from previous ones through biased sampling, and reweights samples onto the unbiased distribution. METALICA holds one replica per diffusion level, forming a Markov Chain that evolves through inter-replica communication and is refined in place as the bias grows. METALICA is the dual of sequential control, in which Sequential Monte Carlo parallelizes the sampler over a batch of particles. Parallelism over the levels of the diffusion-time schedule instead allows METALICA to generate samples from long chains, essential for the discovery of rare events, with accuracy set by run length rather than by the memory available. We validate on a bimodal target with known free energies, then apply METALICA to the unfolding of a protein. At a budget for which sequential control yields no unfolded structure, METALICA populates the basin and resolves a second free energy minimum.

Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before a reactant state, is a central kinetic statistic, providing a mechanism-independent reaction coordinate and a foundation for transition path theory and the calculation of rates. This review surveys modern approaches for estimating the committor and related kinetic statistics from molecular simulations, with an emphasis on self-supervised methods that learn solutions of their defining dynamical equations rather than relying on labeled shooting data. We develop a common operator viewpoint connecting generator-based partial differential equations, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks. Empirical and theoretical evidence points to the efficiency of these methods. We provide theoretical and practical guidance for realizing their full potential in applications, including strategies for treating non-Markovian effects and for sampling. We conclude by identifying opportunities for further research, including connections to reinforcement learning and generative modeling.

We present psi4-cbo, an open-source extension of the Psi4 program for simulations within the cavity Born-Oppenheimer Approximation (CBOA). By treating the cavity photon mode on the same footing as the nuclear coordinates, psi4-cbo incorporates light-matter coupling into standard electronic structure workflows. These workflows include self-consistent field calculations, response theory, analytic gradients, joint Hessians, and geometry optimization. The package is designed for reproducibility and is documented with working examples for its main workflows. To evaluate the approximations inherent to the CBOA framework, we use an exact \gls{qedfci} reference. Resolving the correlation energy into electron-electron and electron-photon channels enables us to precisely determine the point at which the CBOA becomes quantitatively sufficient. Additionally, we highlight that, since self-consistent treatments dress the cavity modes, satisfying experimental resonance conditions is method-dependent. As a practical application, we investigate the chromophore acrolein under vibrational strong coupling and detail how to locate resonances on the dressed photon modes rather than the bare molecular bands.

Micron-sized particles moving through a fluid are subject to viscous resistance and thermal fluctuations. Beyond steady Stokes friction, colloids also exhibit hydrodynamic memory effects arising from conservation laws of the surrounding fluid. Although fluid-flow problems in complex geometries and confinements are often highly involved, numerical or approximate solutions can be obtained; translating these solutions into closed-form equations of motion for individual colloids, however, is rarely possible. Here, we develop a theoretical framework that provides both underdamped and overdamped colloidal dynamics from the solution of an arbitrary flow problem: (i) Using the Lorentz reciprocal theorem, we express the colloidal equation of motion in terms of the geometry's Green's function and the fluid-flow profile. This formulation yields both the corresponding Stokes drag as well as a Basset-like hydrodynamic memory contribution. (ii) As colloid inertia is often negligible, we furthermore derive the corresponding overdamped (colloid-inertia-less) limit, which inherits the Stokes and Basset-like resistances and additionally gives rise to a spurious drift. We propose applications of this framework to passive and active particles subject to involved confinements and problem geometries.

Micron-sized colloid particles diffusing through a fluid experience hydrodynamic inertial and memory effects, the latter causing velocity autocorrelation to decay as a power law. At the same time, many theoretical descriptions treat colloidal diffusion in a fluid using overdamped Langevin dynamics for its convenience, eliminating velocity, omitting inertia, but also ignoring the power-law memory. In this Letter, we show that hydrodynamic memory survives in the overdamped (colloid-inertialess) limit. By identifying the dimensionless parameter controlling the crossover between early- and late-time dynamics, we derive in closed form the overdamped Langevin equation in the presence of hydrodynamic memory. This provides a theoretical framework for realistically describing colloidal dynamics in a fluid, and establishes a rigorous basis for the positional memory observed in high-resolution experiments. Our theory predicts that hydrodynamic memory becomes increasingly pronounced for smaller particles and under stronger external forcing, and offers experimental probes for the crossover from conventional exponential relaxation to memory-dominated power-law dynamics.

Quantifying the magnetic properties of open-shell molecules is a common task in chemistry and is increasingly performed in silico using Kohn-Sham density functional theory (KS-DFT). Previous work demonstrates that the predictive accuracy of a few functionals for one such property - hyperfine coupling constants (HFCCs) - is possible, implying that such approximations must yield accurate spin-densities at the nucleus. However, the ability of such functionals to globally predict accurate spin-densities of comparable quality to rigorous ab initio coupled cluster theory, for example, is dubious, despite this being a fundamental quantity for KS-DFT. This work intends to explore the matter by evaluating moments of the spin-density, $\langle r^n \rangle = \int \rho(r)r^n d\tau$, $n=-2,\cdots,2,3$, for second-, third-, and fourth-row atoms to compare various KS-DFT functionals against CCSD. Our results broadly indicate that the tested functionals experience significant deviations with respect to CCSD spin-densities in regions up to 1 Bohr away from the nuclei, with most errors occurring in the immediate vicinity of the nucleus. We find evidence of extreme errors by some functionals for individual $\alpha$/$\beta$ spin-densities, although several ultimately end up benefiting from significant error cancellation once the corresponding global spin-density is formed. Nevertheless, a comparison between CAM-B3LYP and the Quantum Theory Project (QTP)-family of DFT functionals based on Correlated Orbital Theory conditions across all error metrics demonstrates that QTP00 more accurately reproduces the global spin-density as well as HFCCs, generally offering results in better agreement with CCSD. In line with previous work, we also corroborate the success of PBE0 and the TPSS-family of functionals for HFCCs, further finding that both approximations generally yield spin-densities that are amongst the best.

This work presents the spatially resolved mapping of methane decomposition chemistry in a short direct-current glow discharge, revealing segregated zones for dissociation and polymerization in an Ar-CH4 mixture at 400 mTorr. Laser-induced fluorescence (LIF), two-photon absorption LIF (TALIF), and optical emission spectroscopy (OES), are used to map the absolute number densities of atomic hydrogen (H), methylidyne (CH), and dicarbon (C2) radicals. The results reveal a segregated chemical environment driven by the non-local electron kinetics. The primary dissociation of methane is confined to the cathode sheath (< 4 mm), where the H atom density peaks at a value (). Analysis of the Halpha} line profile identifies two distinct suprathermal H atom populations with peak kinetic energies of ~110 eV and ~17.5 eV, attributed to ion-surface reflection and electron-impact dissociation, respectively. The densities of secondary radicals CH and C2 peak further from the cathode at the sheath-negative glow boundary (y=4-6 mm), identifying this region as the primary zone for polymerization, with the peak C2 density higher than that of CH by a factor of ~370. The discharge operates under strong non-equilibrium conditions, confirmed by the disparity between the low bulk gas temperature (~570 K) and the high vibrational temperature (3300-4700 K) of the emitting CH(A) radicals, the latter serving as a signature of a specific high-energy dissociative excitation pathway. This work demonstrates how the structured energy landscape of a short glow discharge spatially separates methane dissociation from subsequent polymerization. Kinetic estimates suggest that suprathermal H atoms may play a role in bulk plasma chemistry via abstraction reactions, with predicted CH4 activation rates exceeding those of the thermal H population.

Living systems compute with chemistry by mapping environmental signals onto specific internal chemical states. Despite recent advances in molecular programming, it remains unclear which physicochemical features control the computational expressivity of chemical systems. Here we inverse-design thermodynamically consistent chemical reaction networks whose steady-state response to an environmental input computes a target nonlinear function. Using implicit differentiation we train the free-energy landscape directly: standard chemical potentials, transition-state energies and thermodynamic drives. Increasingly large networks generated by elementary ligation and cleavage steps fit increasingly complex nonmonotonic polynomial functions, with expressivity scaling logarithmically with network size, predicted primarily by the number of reactions. Training individual energetic parameter classes reveals that internal thermodynamic drives, capable of breaking detailed balance, dominate trainability, with comparable performances achieved only by pairs of parameter classes. These results identify nonequilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.

PyExoCross is a Python-based spectroscopic post-processing framework for converting large atomic and molecular line lists into scientifically useful quantities, including partition function, specific heats, cooling functions, lifetimes, oscillator strengths, line intensities, stick spectra, and absorption and emission cross sections. It is intended for applications in astrophysics, planetary atmospheres, laboratory spectroscopy, and other high-temperature environments where large modern databases require efficient and reproducible computational treatment. The new release provides both a configuration-file-driven command-line interface (CLI) and a Python application programming interface (API) within a unified computational framework. The original input file workflow is retained, while the Python API enables direct use in scripts, notebooks, and automated pipelines. The version PyExoCross 2.0 also adds explicit support for non-local thermodynamic equilibrium (non-LTE) calculations, including two-temperature models and user-defined density and population treatments for both absorption and emission spectra. In addition, database compatibility has been expanded beyond ExoMol, HITRAN, and HITEMP line lists to include high-resolution molecular database ExoMolHR and atomic database ExoAtom. GPU acceleration is also introduced for computationally intensive intensity and cross-section calculations and is available for all currently supported database formats. These developments improve workflow flexibility, reproducibility, and integration with modern data-analysis environments, while extending the physical modelling capabilities of the code. PyExoCross therefore provides a more general and extensible platform for large-scale spectroscopic simulations based on modern atomic and molecular databases.

Whereas the effect of monotonically decaying memory kernels on reaction dynamics has been studied extensively, the effect of oscillatory memory, observed for widely different systems, is much less explored. We analyze non-Markovian barrier-crossing dynamics in the presence of a single exponentially decaying oscillatory friction memory kernel by simulations and analytical theory. We identify three scaling regimes: two in which the mean first-passage time $\tau_{\mathrm{MFP}}$ scales quartically and linearly with the memory kernel decay time $\tau_{\mathrm{o}}$, respectively, and a third in which $\tau_{\mathrm{MFP}}$ scales quartically with the oscillation period $\tau_\varphi$ of the memory kernel. Our results are relevant to a broad class of many-body dynamical systems such as dihedral isomerization processes and chemical reactions in solvents.

An accurate theoretical treatment of periodically driven quantum systems is crucial for various fields in the exact sciences, for instance Nuclear Magnetic Resonance (NMR) spectroscopy. Conventionally, either average Hamiltonian theory or Floquet theory is used to predict or describe experimental outcomes, such as the time evolution or the spectra yielding the information of the sample under study. A detailed analysis of the equivalence of these two approaches with an emphasis on applications in NMR will help to improve the theoretical understanding of NMR experiments. In this work, we identify the Floquet-Magnus expansion as essential to prove the mathematical equivalence of Floquet theory and average Hamiltonian theory. We advocate a calculation scheme which is less prone to algebraic mistakes because explicit integration is avoided. On this basis, we provide the first four orders of both theories. We further examine their applicability to some experiments in NMR. As examples, we investigate the Bloch-Siegert shift and dipolar coupled spin systems under magic-angle spinning. Based on our analysis, we recommend the use of the Floquet-Van Vleck approach including both the effective Hamiltonian and the kick operator. The consistent separation of secular and non-secular contributions appears to be especially advantageous for numerical robustness. Its accuracy is about three times better than the one provided by average Hamiltonian theory despite their formal equivalence. Our findings provide important insights into the theoretical background of Floquet theory and average Hamiltonian theory. This includes the extent of their algebraic and perturbative equivalence, with an emphasis on how these findings are of relevance in the analysis of solid-state NMR experiments.

Simulating nuclear magnetic resonance (NMR) spectra is a promising application of quantum simulation. Using Quantinuum's System Model H2, a trapped-ion quantum computer, we demonstrate an end-to-end, large-scale digital NMR simulation of a classically challenging benchmark molecule, 1,2-di-tert-butyl-diphosphane. We implement a hardware-efficient reduction of the nuclear-spin Hamiltonian, enabling Trotterized real-time evolution of an effective 21-spin model with tailored error suppression to reduce the effects of device noise. The reconstructed liquid-state proton NMR spectrum agrees with classical reference calculations and reproduces key spectroscopic features that previous quantum hardware demonstrations did not capture. Given the widespread use of NMR in chemical analysis and industrial research, these results advance digital quantum simulation of NMR spectra towards practical quantum utility.

The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that microstructure evolves along an arbitrary thermal trajectory, however, remains a challenging problem. Conventional continuous-cooling-transformation diagrams, for instance, provide only a static description, and they do not exist for all materials, especially emerging and novel ones. Deep-learning approaches address this issue, but they treat image-based characterization, temporal prediction, and microstructure forecasting as separate tasks. Here, we introduce a probabilistic deep-learning framework that unifies these tasks for in situ high-temperature confocal laser scanning microscopy data. The initial surface image is compressed with a beta-variational autoencoder into a low-dimensional latent representation, which, together with the cooling rate and the full temperature history, is passed to a temporal fusion transformer that produces a quantile forecast of the Widmanstatten ferrite ratio. In parallel, a Gaussian process predicts the end-state Widmanstatten ferrite ratio and corrects the forecast equilibrium level, yielding probabilistic prediction intervals. We demonstrate the efficacy of our method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes and showcase significant accuracy. This verifies the relevance of our framework and paves the way for a streamlined and highly accelerated investigation of material evolution under thermal treatment.

Optically readable molecular spins are attractive as quantum sensors due to their nanoscale modularity, synthetic tunability, and scope for sensitive readout. In particular, photoexcited spin-triplet states in organic molecules are appealing in supporting high optical-spin contrast at room temperature. Their utility is underpinned by their coherence, which warrants a detailed understanding of it. Here, from first principles, we systematically explore the Hahn-echo decoherence of the benchmark molecular system for room-temperature optically detected spin coherence—pentacene guest molecules coupled to a para-terphenyl host. Using generalized cluster-correlation expansion methods, we investigate the mechanisms of nuclear-spin-induced decoherence from zero to high magnetic field, exploring the role of guest vs host molecules, specific nuclei, zero-field splitting interactions, and hyperfine parameters. We describe how zero-field decoherence is driven by ~6 nuclei on the guest, while high-field decoherence is driven by ~600 nuclei in the host; how the longitudinal zero-field splitting parameter, $D$, can prolong $T_2$; and the magnetic-field-dependent processes which drive decoherence. These results advance our understanding of decoherence in optically readable molecular spins, providing insight for their synthetic enhancement and deployment as quantum probes.

CeO serves as an important model system for understanding the interplay of electron correlation, spin-orbit coupling, and 4f/5d configurational mixing in lanthanide oxides. Its anion photoelectron spectrum exhibits dense electronic manifolds and multielectron shake-up processes that complicate spectral assignments and photodetachment dynamics. Here, we combine high-resolution anion photoelectron spectroscopy with spin-free and spin-orbit-coupled CASSCF+NEVPT2 calculations to characterize the electronic structure of CeO- and CeO. A binning-based data analysis approach enables observation of weak transitions hidden by spectral congestion, while multireference calculations reproduce the low-lying electronic structure and detachment energetics. On this basis, tentative assignments are proposed for the observed photoelectron features. Analysis of photodetachment selection rules, photoelectron angular distributions, and multielectron detachment channels provides additional insight into the mechanisms underlying the spectrum. These results establish a comprehensive interpretation of the CeO- photoelectron spectrum and underscore the importance of combining high-resolution spectroscopy with multireference relativistic theory for electronically complex lanthanide systems.

We formulate Hartree–Fock as a density-matrix renormalization group (DMRG) sweep of a single Slater determinant, assuming a density–density electron–electron interaction. We compress the occupied state and long-range interaction separately, reducing each sweep step to a small local Fock problem. Exchange is retained throughout. Uniform antiferromagnetic hydrogen chains provide an almost ideal setting for this approach, while retaining the Coulomb interaction and its $1/r$ tail. We initialize and converge a chain of 100,000 electrons in 1.8 million spatial basis functions in about two hours on a 64-GB Mac mini, using less than a third of its memory. The work per sweep is nearly proportional to chain length, and the number of sweeps needed for convergence changes little from 1000 to 100,000 atoms. DMRG's compression of states and environments thus makes large HF calculations with long-range Hartree and exchange practical on an ordinary desktop computer.

Sample-based quantum diagonalization (SQD) is a hybrid quantum-classical method for electronic-structure calculations. We applied SQD to a titanium-based metallocene catalyst system for 1-hexene production, where free-energy differences must be predicted within 1 kcal/mol to accurately determine product selectivity. However, currently accessible active spaces on quantum computers are limited in size, leaving significant dynamical correlation effects outside the active space. To address this challenge, we combined SQD with Tailored Coupled Cluster (TCC) theory. In this SQD-TCC framework, static correlation is treated by SQD, while dynamical correlation is incorporated through CCSD and its perturbative triples extension, TCC(T). We calculated the relative energies of two transition states governing 1-hexene selectivity. While SQD alone did not yield converged relative energies within practically accessible active spaces, SQD-TCC and SQD-TCC(T) provided reasonably converged results. Importantly, the relative energy obtained from SQD-TCC(T) differed by more than 1 kcal/mol from the corresponding CCSD(T) result, demonstrating the significance of static correlation in this system and its impact on predicted selectivity. Although TCC(T) calculations based on CASCI are feasible for small active spaces, the present system requires substantially larger active spaces that are beyond the reach of CASCI. By using SQD, we were able to access active spaces impractical for classical CASCI calculations. These results demonstrate that both static and dynamical electron correlation are essential for a reliable description of this chemistry and highlight the importance of incorporating dynamical correlation into quantum-computing approaches targeting chemically accurate simulations.

Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of 3D generative models for larger and more complex molecules, restrict access to unexplored chemistry. Here, we introduce QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical (QM) methods to iteratively explore targeted QM property manifolds. By coupling generation with physics-based evaluation, QALPA improves molecular sampling and model reliability in sparsely populated regions of chemical space. Our results show that training on complementary QM datasets spanning both small (QM7-X) and large (Aquamarine) drug-like compounds enables accurate molecular generation across a broad size range, improving transferability beyond the training distribution for complex property manifolds involving both extensive and intensive properties. As a proof of concept, QALPA coupled with the machine learning-augmented tight-binding method EquiDTB efficiently augments alloQM, a QM dataset introduced in this work, comprising 6,253 conformers of allosteric drug molecules, by populating sparse regions of the property landscape defined by the many-body dispersion energy and HOMO-LUMO energy gap. These results demonstrate that the integration of generative AI with efficient ML/QM methods offers a practical pathway toward augmenting sparse QM datasets and sustainably expanding the exploration of chemical space for molecular discovery.

Many important chemical systems, from radiation-driven processes to condensed-phase photochemistry, involve reaction mechanisms that are so complex it is a challenge to characterize them experimentally or predict them from chemical intuition. Existing computational approaches to mechanism discovery typically assess pathway importance through thermodynamic favorability alone, which does not provide time-dependent kinetics or inform how to model spatial inhomogeneities. Here we describe an integrated workflow that discovers complex reaction mechanisms without prescribing them and connects molecular-scale reactivity to spatiotemporal observables. The workflow combines high-throughput DFT, automated reaction network construction with chemical plausibility filtering, stochastic pathway sampling to identify reactions which are likely to occur, and spatially resolved reaction-diffusion kinetics simulations with explicit tracking of species in space and time. To demonstrate the workflow on a system of high complexity, we apply it to radiolytic chemistry in an extreme ultraviolet (EUV) organic polymer thin film photoresist, where a single 92 eV photon initiates cascades of radical ions, fragments, and low-energy electrons across a nanoscale radiolytic spur. Starting from over 3,300 species and millions of candidate reactions, the workflow identifies the most likely reaction pathways and produces spatiotemporal maps that resolve product formation on femtosecond-to-nanosecond timescales across a 15.5-nm domain. The simulations predict products detected experimentally and reveal that the identity of the initially photoionized species profoundly shapes the downstream product distribution through multi-step pathways governing the balance between deprotection and crosslinking reactions. The methodology is broadly applicable to complex condensed-phase reactive systems.

We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory. In particular, we avoid the explicit construction and eigen-decomposition of the response function matrix and use Lanczos quadrature to evaluate the trace of a matrix function on a real-space grid. We also show it is possible to exploit the spatial smoothness of the RPA correlation energy density in real-space to reduce computational cost. Specifically, we compute the energy density on a coarse grid, then reconstruct an approximation to the full, fine grid via interpolation. We implement this formulation within the SPARC electronic structure package and demonstrate its convergence, accuracy, agreement with planewave results, and scaling. Interpolation can enable an $8\times$ reduction in the computational prefactor. Given the embarrassingly parallel nature of the proposed method, we achieve near-ideal speedups and near-cubic scaling, thus allowing us to compute the RPA correlation energy for a silicon system with 256 valence electrons at chemical accuracy in less than 30 minutes on 4,096 CPU cores.

X-ray spectroscopy techniques probe chemical states in systems with atom-site specificity; however, the site-selective characterization of local bond environments in novel research materials often relies on a combination of reference spectra, multiple complementary spectroscopic techniques and electronic structure calculations. Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. Here I show that the wealth of information encoded in the Auger spectrum offers the opportunity to train convolutional neural networks for site-selective functional group classification in organic molecules directly from the Auger spectrum lineshape. Furthermore, the inclusion of the core-electron binding energy as an additional modality improves the classification, either by augmenting the input fitted intensities with the binding energy or by conditioning the classification on the binding energy via feature-wise linear modulation layers. The latter approach was previously developed for visual reasoning in image classification, and the present results show that this is the more robust approach for including the binding energy. This work demonstrates the potential for new data-driven characterization capabilities in spectroscopic techniques that are information-rich but difficult to interpret.

Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.

Accurate prediction of transition-metal hydride (TM-H) bond dissociation energies (BDEs) remains challenging because of strong electron correlation, relativistic effects, and nuclear quantum contributions. Here, we assess the performance of neural-network variational Monte Carlo (NN-VMC) based on the Psiformer ansatz for systems with increasing complexity (LiH, OH, TiH and NiH), and compare it against CCSDT(Q)/CBS as well as available theoretical and experimental data. Throughout this study, both NN-VMC and ab initio calculations employ a common ccECP Hamiltonian to enable tractable and consistent comparisons. To reduce finite-training errors, we introduce complementary zero-variance and infinite-step extrapolation schemes. For LiH and OH systems, NN-VMC yields total energies that differ within sub-milli Hartree compared to CBS extrapolated ab initio results and the BDE differences remain under 2$\sigma$. In case of Ti and TiH, the variational NN-VMC energies at the end of training are already consistent with CCSD(T)/CBS, while post-training extrapolation systematically closes the gap towards the CCSDT(Q)/CBS results. In contrast, NiH provides a stringent test of wavefunction expressivity, where increasing the number of determinants in NN-wavefunction ansatz improves the recovered correlation energy. The comparison of NiH BDE reveals that, while the existing theoretical predictions cluster into distinct high and low BDE groups, the broad scatter in available experimental data prevents a definitive assessment of the most accurate theoretical approach. This work demonstrates that NN-VMC with ccECPs Hamiltonian provides a competitive framework for quantitative prediction of main-group and early TM-H energetics, while identifying late TM-H as an important benchmark for future developments in neural-network wavefunctions and electronic structure theory.

The Kohn-Sham (KS) formulation of density functional theory (DFT) is a map from the many-electron problem to an effective single-electron problem that is governed by a local multiplicative potential. The generalized-Kohn-Sham (GKS) formalism extends it to permit any single-electron operator—nonlocal, local non-multiplicative, local multiplicative, or any combination of them. Doing so expands the scope and ease of modeling the exchange-correlation (XC) functional in DFT, which encodes the complicated many-electron interactions into a mean-field of the electron density. However, unlike KS theory, development of XC functionals in GKS theory has been hindered by the absence of corresponding exact XC potentials and energies. We present the exact XC potentials and energies for atoms and molecules by solving the inverse GKS problem, using highly accurate correlated ab initio densities. Our approach is validated across weakly and strongly correlated systems. We further examine a common, yet untested, assumption that KS and GKS correlation potentials and energies are similar, finding instead that they differ substantially in strongly correlated systems. Overall, this work offers a powerful tool to model next-generation of XC functionals within the GKS formalism of DFT.