Papers

Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental measurements vary significantly across laboratories due to differences in protocols and analysis methods, making it difficult to train reliable predictive models. We address this challenge through differential learning. Rather than predicting absolute decomposition temperatures, we instead train message passing neural networks to predict relative differences between pairs of molecules. This approach reduces sensitivity to systematic experimental errors and achieves >85% accuracy in ranking compounds by thermal stability, outperforming conventional regression methods on the same heterogeneous dataset. To understand what drives these predictions, we compare neural network models with interpretable alternatives built from descriptors derived from ab initio calculations and cheminformatics software. This analysis identifies bond dissociation enthalpy as a key determinant of thermal stability rankings, providing further insight into the complex chemistry of thermal decomposition. The differential learning framework generalizes across model architectures, from graph neural networks to classical descriptor-based approaches. Our results demonstrate that learning relative properties rather than absolute values offers a practical solution for modeling noisy experimental data, with direct applications in materials design where thermal stability predictions inform safety protocols.

Spatial variations in electrical connectivity and interfacial ion accumulation can strongly influence electrochemical performance, yet these properties are difficult to visualize directly with conventional ensemble measurements. Here, we introduce potential-modulated opto-iontronic microscopy, an interferometric scattering microscopy (iSCAT) approach for wide-field imaging of electric-double-layer (EDL) dynamics at nanostructured electrodes. Sinusoidal potentials were applied to focused-ion-beam-fabricated indium tin oxide (ITO) nanoholes, and the optical response was extracted at the modulation frequency by Fourier demodulation. The optical modulation amplitude increased approximately linearly with modulation voltage above a low-voltage roll-off and decreased with increasing frequency, consistent with kinetically limited interfacial charging. We then mapped the potential-synchronized optical amplitude across patterned ITO electrodes. Electrically isolated blocks exhibited strongly suppressed modulation signals, whereas electrically connected and partially milled nanogrid structures showed pronounced responses. These results demonstrate label-free optical mapping of local charging dynamics and electrical connectivity at heterogeneous electrochemical interfaces using a commercially available iSCAT platform.

Lithium chalcohalide antiperovskites are a promising, non-toxic alternative to lead halide perovskites, with potential as solid electrolytes for Li-ion batteries. we computationally investigate a relatively unexplored anion-interchange mechanism by which cubic Li$_{3}$$AB$ derivatives are obtained from the parent cubic antiperovskite Li$_{3}$$BA$ ($A$ = O, S, Se, Te, Po; $B$ = F, Cl, Br, I). The calculated relative energy landscape provides a useful guide for anion-site selectivity and its role in structural stability. The energetic stability results reveal that the smaller anion inside the octahedron stabilizes the structures. The lattice-dynamic calculations confirm that Li$_{3}$F$A$ ($A$ = Te, Po) and Li$_{3}$O$B$ ($B$ = Cl, Br, I), which are the most energetically stable compounds, are dynamically stable cubic phases without imaginary phonon modes. However, Li$_{3}$FS and Li$_{3}$FSe, while energetically stable, are dynamically unstable at equilibrium and become dynamically stable under triaxial compressive strain. In addition, we report the electronic structure and density of states (DOS) of all compounds, which show a substantial change in band gap upon anion interchange. The strain engineering of the lithium chalcohalide family illustrates how a few percent of the strain can tune the electronic band gap within the electrochemical stability window for solid battery applications. This study unveils essential characteristics of the anion site-interchange mechanism and provides a foundation for the understanding and design of lithium chalcohalide antiperovskites.

We report multi-hour stable trapping and steady state of non-laser-coolable Cs$^{+}$ ions in a linear Paul trap, achieved via resonant charge-exchange (RCE) cooling with a precisely centered ultracold Cs cloud. Without cold atoms, all ions are lost within 3 minutes. With centered cold atoms, RCE cooling establishes a stable population of ions for 6 hours with no measurable decay – more than two orders of magnitude longer hold times compared to prior hybrid atom-ion systems. We observe a threshold behavior in the steady-state number of ions ($N_{s}$): initial ion loadings above $N_{s}$ ions converge downward to $N_{s}$ ions and remain constant thereafter; initial loadings below $N_{s}$ ions show no measurable decay. The dynamics is governed by a competition between ion-ion rf heating and ion-atom collisional cooling. The prolonged, simultaneous ion-atom trapping suggests an upper bound on the three-body recombination-induced Cs$_{2}^{+}$ formation rate constant: $k_{3} << 1.4 \times 10^{-25} cm^{6} s^{-1}$. This remarkable realization of long-lived steady state overcomes a critical limitation of prior hybrid atom-ion systems and enables extended studies of ultracold ion-neutral chemistry, rare inelastic collisions, and sympathetic cooling routes for complex molecular ions.

Biological and artificial networks compute by transforming time-dependent inputs into functional outputs. Because the internal state of a stochastic network relaxes on finite timescales, its output generally lags behind a changing environment, producing computational errors. We show that for reversible continuous-time Markov networks the error admits a universal thermodynamic bound. Decomposing the total error into representation and lag contributions, we derive an inequality relating the lag error to the entropy production rate and a memory time equal to the integrated equilibrium autocorrelation of the output observable. The bound implies that accurate dynamical computation requires either substantial dissipation or long-lived memory encoded in slowly relaxing modes. We demonstrate these principles in artificial Markov networks and in models of biochemical information processing. Our results establish a thermodynamic limit on information processing in stochastic networks and provide a quantitative framework for understanding the energetic costs of biological computation.

Excitation-energy transfer underpins the conversion of light into usable energy in photosynthetic organisms and serves as a paradigm for evolutionary optimized transport in open quantum systems. Although this process is often described as incoherent thermally assisted hopping, such descriptions become inadequate when electronic coupling, vibronic interactions and environmental fluctuations occur on comparable energy scales. Determining how the environment controls transport therefore remains a fundamental challenge. Here, we use temperature-dependent 2DES to investigate energy transfer in the photosynthetic antenna protein allophycocyanin over the range 10 - 296 K. The dominant $\beta \rightarrow \alpha$ transfer step exhibits a pronounced non-monotonic temperature dependence: the transfer time decreases from 400 fs at 10 K to 200 fs near 30- 40 K before increasing again to 400 fs at 296 K. In contrast, the homogeneous optical dephasing time decreases monotonically across the same temperature range. To interpret these observations, we model APC as a vibronically coupled excitonic dimer interacting with a structured environment and solve the dynamics using hierarchical equations of motion. Conventional fixed-bath models, including Drude-Lorentz and explicit intermolecular-mode spectral densities, fail to reproduce the observed turnover. Quantitative agreement is obtained only when the low-frequency sector of the environmental spectral density is allowed to anharmonically evolve strongly with temperature, while the high-frequency bath remains essentially unchanged. More broadly, these findings demonstrate that transport efficiency is controlled not simply by the magnitude of environmental fluctuations, but by the distribution of environmental spectral weight across frequency space, providing new experimental constraints on theories of molecular transport in complex quantum environments.

Removing an electron from a water molecule can drive its two hydrogen atoms to pair up and depart as molecular hydrogen. However, even for this elementary reaction, the route from start to finish has remained hidden because measurements have yet to follow the electronic and nuclear motion simultaneously. Combining correlated photoelectron and ion imaging, few-femtosecond pump–probe measurements, and nonadiabatic simulations, we track the complete pathway in isolated heavy water (D$_2$O) molecules. The reaction takes an indirect route and dissociates along three distinct pathways (direct, roaming, and delayed) with formation times of about 34 and 72 femtoseconds for the direct and delayed branches. Yet bond formation requires the molecule to first break its own symmetry. Only random asymmetric motion enables the electronic-state switch at a conical intersection, joining the two hydrogen atoms before the oxygen–hydrogen bond breaks. These results establish a time-resolved picture of molecular hydrogen formation from water and provide a general strategy for linking electronic excitation to chemical outcomes in settings from radiation damage to hydrogen production.

Ion transport in crystalline hosts is controlled by an applied potential that simultaneously sets ionic distribution and transport rate, restricting operation to a one-dimensional control space. Here we show that the transport rate of Li+ ions in double-gated van der Waals devices can be modulated while the system occupies fixed ionic-density states. We measure the ionic current along the van der Waals interfaces between hexagonal boron nitride and graphene or MoS2 while simultaneously monitoring the in-plane electronic response. The ionic current exhibits pronounced hysteresis, with plateaus marking discrete ionic-density states balanced by electronic charge, while an independently tuneable electrochemical-potential drop controls the ionic transport rate. The devices sustain over 1,000 switching cycles and function as hybrid ionic-electronic transistors capable of logic operations and memory retention, with ON/OFF ratios exceeding two orders of magnitude. This work demonstrates a two-dimensional control space for ions intercalated in layered materials.

Real-time quantum-electrodynamical density-functional theory (QEDFT) for an open device requires local electron and field histories to determine their sources. For occupation-dependent interactions and a gapped finite-band photon continuum, we prove a causal one-to-one map on the forward image over a nonzero time interval. Three source-reconstruction benchmarks test the electronic limit, the photon-continuum limit, and a coupled joint inverse against a closed-form continuum endpoint. This provides the density–source foundation for real-time open-system QEDFT.

Machine-learning interatomic potentials enable accurate and efficient atomistic simulations, yet their reliability for out-of-distribution configurations far beyond the training domain remains a significant challenge. Here, we introduce a semiparametric interatomic potential based on a generalization of the Abell–Tersoff bond-order potential, incorporating a chemically informed functional form and explicit high-order many-body correlations to address this challenge. The model is trained and evaluated on various datasets of silicon, carbon, water, and small molecules, achieving interpolation accuracy comparable to existing MLIP models while exhibiting improved extrapolation to unseen configurations, including those at high pressures and temperatures. These results provide insights into the design of specific inductive biases for reliable extrapolation in interatomic potentials. inductive biases that can be used to control extrapolation in machine-learning interatomic potentials.

Fock-space variational Monte Carlo (FS-VMC) evaluates variational quantities by sampling discrete many-body configurations. In molecular applications, concentrated Born distributions can hinder Markov-chain mixing, while Monte Carlo estimators can also exhibit large variance. We introduce importance-reweighted FS-VMC (IR-FS-VMC), which combines Metropolis–Hastings sampling, an evaluation kernel, and self-normalized importance sampling to estimate the Born-distribution energy, gradient, and stochastic reconfiguration (SR) matrix. Controlled comparisons on equilibrium H2O and Fe2S2 show that the acceptance rate and local-energy variance can respond differently to the sampling and estimation procedure. A single production protocol yields accurate variational energies for H2O dissociation, 36-site hydrogen lattices, and Fe2S2 and Fe4S4 active spaces without system-specific sampling parameters.

Quantum states with conserved total spins, or spin eigenfunctions, are important for studying quantum chemistry and quantum manybody physics problems. A typical class of spin eigenfunctions are Dicke states, which attain maximal spins. While we already have many efficient quantum algorithms to prepare Dicke states, it is not yet clear if we could do so for arbitrary spin eigenfunctions deterministically. Generalizing Bärtschi and Eidenbenz's elegant algorithms for Dicke state preparation, we successfully prepare arbitrary spin eigenfunctions characterized by branching paths and binary spin trees. As a byproduct, we also develop the corresponding classical algorithms to reconstruct all these spin states.

Determining the binding mechanisms between polymer dots and proteins is important for developing novel nanotechnologies for biomedicine and bioimaging. In this work, we use all-atom molecular dynamics simulations to determine the binding affinity of Pdots with ovalbumin protein at pH = 7 and 1. The selected Pdots are mixtures of Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-(2,1',3)-thiadiazole)] (PFBT) and poly(styrene/maleic anhydride) (PSMA) with varying composition. At pH = 7, the Pdots have a net negative charge due to the COO- functional groups on the PFBT, and the protein also has a net negative charge. At pH = 1, the Pdots are charge neutral with PFBT containing only COOH functional groups, and the protein also has a net positive charge. We sample the initial position of the protein by varying its initial position through all 6 orientations of a cube. For each orientation, we pull the protein towards the PFBT region of the Pdot. We compare the Coulombic and Lennard-Jones interaction energies for the 6 different interacting faces and two selected pH values. We find that the LJ interaction energies are similar among all 12 of these cases. The measured Coulombic interaction energies suggest that pH = 1 has better binding affinity than pH = 7. The potentials of mean force (PMF) along the pulling coordinates differ with pH. The PMFs from 2 of the 6 initial configurations at pH = 1 are negative whereas none of them are negative at pH = 7, confirming the preferred binding affinity when pH = 1. One of the faces at pH = 1 has the lowest PMF of about -30 kcal/mol, which is much lower than about 6 kcal/mol seen for the lowest case at pH = 7. Comparison of protein residue charge distributions at pH = 7 and 1 further shows that the electrostatic interaction is critical to the binding affinity, and negatively charged residues reduce at pH = 7 does not bind to negatively charged Pdot.

Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.

The construction of a grid-based potential energy surface (PES) can be prohibitively expensive as the number of grid points grows exponentially with molecular size. Molecular symmetry can reduce this cost by eliminating symmetry-equivalent points. We present an algebraic symmetry-based grid reduction method, ASyBGR, that is capable of handling non-Abelian and higher-order cyclic symmetry groups. Non-coordinate-mixing operations are encoded into a vector space bit-string, where Gaussian elimination and closure can identify all symmetry-valid sign change patterns and the coordinate axes whose grids can be halved about the origin. In the presence of degenerate subspaces, we optimize the coordinate basis to maximize reduction. The method was validated by comparing vibrational configuration interaction (VCI) energies calculated using the full and symmetry-reduced fourth-order HDMR PESs for molecules spanning a broad range of symmetries. The number of grid points required to construct the PES was reduced by as much as 81%, while VCI energies deviated below 1 1/cm for all test cases on average. The relative root-mean-square deviation (RRMSD) between the full and symmetry-reduced potential energy and dipole moment surfaces were at most on the order of 10$^{-3}$ and 10$^{-2}$, respectively.

Laser-induced plasmas are primarily characterized by their temporal density decay. Using time-resolved transverse optical diffractometry, we reveal their concurrent spatial evolution, demonstrating pronounced broadening and flattening during the first 200 ps following ionization. This non-self-similar evolution arises from the local density dependence of electron-ion recombination, without particle transport. Exploiting the continuum of initial densities within a single plasma disc, we experimentally reconstruct an effective, time-dependent local recombination law over a broad density range. Applied locally to independently measured initial plasma profiles, this kinetic map quantitatively predicts their subsequent evolution. These results are relevant to transient diffractive optics, plasma-based optical elements and waveguides, and the development of laser-induced plasmas as platforms for gas-phase THz plasmonics.

Vibrational spectroscopy reports on molecular structure with chemical-bond specificity, but in biological systems the vibrational signals of a target chromophore are typically buried under contributions from the surrounding matrix. Resonance Raman (RR) spectroscopy addresses this problem by matching the excitation wavelength to an electronic transition of the chromophore of interest, which increases Raman cross sections by up to six orders of magnitude and restricts the enhanced modes to those coupled to the resonant electronic state. This chapter introduces the physical basis of resonance enhancement and shows how RR isolates chromophore-specific vibrational markers in complex biological systems. We then extend the same principle into the time domain with femtosecond stimulated resonance Raman spectroscopy (FSRRS). In FSRRS, the Raman pump is tuned across the visible range and can be placed in resonance with the transient absorption of a chosen excited-state species, which makes the Raman pump wavelength an additional experimental variable. Sampling this variable across the excited-state absorption manifold provides a selection criterion that separates coexisting transient species sharing a common vibrational window.

Core ionization is spatially localized, but the correlated valence response that screens the resulting hole need not be. Using the water dimer as a minimal hydrogen-bonded system, we ask whether acceptor-site O~1s ionization recruits valence channels on the neighboring donor molecule and how proton displacement redistributes that response. We introduce correlated shifted-start real-time $\Lambda$-coupled-cluster theory to connect the core-hole spectrum with orbital- and fragment-resolved screening pathways. At equilibrium, the satellite response contains donor-local and intermolecular charge-transfer-like contributions. Their intermolecular origin is supported by the strong suppression of both contributions when the monomers are separated. In a fixed-nuclei geometry scan, elongating the hydrogen-bonded donor O–H bond nearly doubles their combined share of the satellite response, while analysis of the first approximately 10~fs of the fixed-nuclei electronic response reveals stronger modulation involving a donor-localized valence orbital. More broadly, this work shows how localized core ionization can serve as a site-selective probe of electronic communication, with satellite structure revealing how nuclear geometry redirects many-electron screening through molecular environments.

The electronic circular dichroism (ECD) spectrum is a primary experimental probe for assigning the absolute configuration of chiral molecules, yet interpreting a measured spectrum requires time-dependent density functional theory (TDDFT) calculations that can cost hours per molecule and must be repeated for every candidate stereoisomer and conformation. We present PhysECD, a physics-constrained, parity-aware E(3)-equivariant framework that bypasses computationally expensive TDDFT and predicts ECD spectra directly from the 3D structure of an individual conformer. Instead of regressing the spectrum as an opaque sequence, PhysECD predicts the physical quantities that generate it: per-state excitation energies and electric and magnetic transition dipoles. These quantities determine the rotatory strength R – the dot product of the two dipoles, a pseudoscalar that reverses sign under mirror reflection – and yield the final spectrum through a differentiable Gaussian-broadening formula derived from the underlying physics. The parity structure of the equivariant features guarantees the correct chiroptical symmetry: reflecting a molecule exactly negates the predicted spectrum. On the CMCDS dataset, PhysECD attains a per-molecule spectral Pearson correlation of 0.642 (mean) / 0.822 (median), substantially exceeding prior learned predictors while remaining physically interpretable. Experiments across multiple backbones further show that the framework is backbone-agnostic, paving the way for real-time assignment of absolute configuration.

Diamond was grown from a liquid Ga-Fe-Ni-Si alloy near 1 atm, although bulk graphite is thermodynamically more stable under those conditions. This Perspective asks what must be true for a diamond growth front to continue advancing in such a metastable regime. I define a finite-difference transfer free energy for a specified interfacial event and identify the completed attachment of one carbon atom at a kink as the repeatable event that governs sustained growth. In the macroscopic limit, surface terms cancel, and the required carbon activity is fixed by the bulk free energy of diamond. On a graphite-normalized scale, the diamond equilibrium threshold is about 2.0 at 1300 K, whereas graphite saturation occurs at unity. Diamond growth therefore requires kinetic selection above the graphite threshold, not a reversal of bulk phase stability. The analysis distinguishes thermodynamic driving force, kinetic accessibility, experimental detection limits, and the carbon retained after an experiment. It gives isotope-resolved tests for distinguishing sustained diamond growth from seed survival, transient carbon attachment, or failure to detect graphite. A $^{13}\mathrm{C}$-enriched diamond seed provides a test of natural-abundance gas or condensed carbon sources: a registered, crystallographically continuous $^{12}\mathrm{C}$-rich layer outside the original seed can establish source-attributed overgrowth without isotopically labeling each feed. The same accounting is extended to cubic boron nitride. Separate boron and nitrogen chemical potentials are required, and $^{10}\mathrm{B}/^{11}\mathrm{B}$ and $^{15}\mathrm{N}$ contrasts could distinguish reservoir supply from seed loss for each element. The resulting criteria make low-pressure metal-flux growth claims quantitatively testable and guide experiments, thermodynamic modeling, and atomistic simulation.

Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use approximate but efficient descriptions of interatomic interactions to reach the length and time scales needed to describe macromolecules. These empirical potentials typically perform well for bulk and/or collective properties but face challenges with chemical realism for complex systems, e.g., during reactive processes. In this work, we address this challenge by combining the computational efficiency of a deep potential (DP) machine-learning force field and the chemical realism of first-principles van der Waals (vdW) corrected hybrid density functional theory (DFT) enabled by a SeA high-throughput framework. Using this approach, we study the structure and dynamics of PE oligomers and polymers in an ethylene solvent under common high-pressure (supercritical) radical polymerization conditions. We found that the local solvation environment of radical-containing PE oligomers converges for chain lengths greater than (n~6), suggesting extensibility of our oligomer-trained MLFF to significantly longer polymers. We then confirmed the extensibility of these models to long PE chains by characterizing the molecular weight scaling of single-chain structure and dynamics, which showed classic good solvent behavior. Our PE MLFF retained a consistent level of fidelity and stability across a wide range of thermodynamic state points and chain lengths, at full atomistic resolution, therefore paving the way towards first-principles-based polymer structure and property prediction.

The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learning with explicitly non-local physically informed descriptors and introduce an exchange-correlation functional (CIDER26SS) framework regularized for wide transferability. CIDER26SS is size-extensive, highly efficient, provides a balanced and accurate description of both molecular and solid-state systems, and is specifically well-optimized for transition metal surface chemistry. Surpassing existing conventional functionals, CIDER26SS resolves the CO/Pt puzzle, identifying the correct binding site for CO adsorption on the Pt(111) surface, along with an accurate adsorption energy, Pt lattice constant, and surface energy. Predictions agree well with the experimental values, even when all bulk and surface data for Pt are excluded from the training set. Remarkably, CIDER26SS exceeds the accuracy of semilocal approximations even for systems far outside the training domain.

Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to tackle efficiently the computational difficulties. This paper will present a ML model able to predict the Hessian matrix of biological system made of thousands of atoms. The method, based on learning the Hessian in internal coordinates is intrinsically invariant to molecular rotations and translations, and has a very good scaling with the systems' size. The training was performed on a dataset of simple aminoacids, as they constitute the building blocks of larger proteins. From the predicted Hessian matrix it is possible to calculate thermochemical properties within the harmonic approximations, among them enthalpies, entropies, Gibbs' free energies, and zero point vibrational energies.

Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a shielding-tensor model trained directly on monomer-corrected calculations that approximate PBE0, rather than the PBE reference targeted by earlier ShiftML models. ShiftML4 is trained on a diverse set of structures containing 12 of the most common NMR nuclei in molecular organic solids. On experimental benchmark sets, the 13C isotropic RMSE against experiment is 1.67 ppm, compared with 2.34 ppm for GIPAW-PBE on the same geometries. ShiftML4 gives a similar 1H prediction RMSE to ShiftML3 (0.5 ppm) and improves the 15N RMSE from 7.24 to 6.08 ppm. The model also reduces errors in the shielding-tensor anisotropy, with an RMSE of 4.63 ppm on 13C CSA principal components against 5.85 ppm for GIPAW. The improvements in prediction accuracy are retained on better geometries. Basing shift predictions on structures relaxed with PET-MOLS, a recent machine-learned interatomic potential that reaches approximate hybrid-DFT geometries in seconds, lowers the ShiftML4 errors further to 0.48 ppm (1H), 1.49 ppm (13C) and 3.66 ppm (15N).

Quantum optics characterizes light through photon correlations, but ab initio cavity quantum electrodynamics (QED) has only calculated raw field moments at present. In this study, intracavity photon statistics of cavity-coupled molecules are computed from QED coupled-cluster (QED-CCSD-22) densities and validated against exact diagonalization of the Pauli-Fierz Hamiltonian. A change of coordinate origin or dipole convention displaces the ground state coherently, so second-order and higher cumulants of the cavity field are well defined even for molecular ions. The photon subsystem of QED Hartree-Fock is exactly coherent, so the invariant statistics measure electron-photon correlation. The field fluctuations are super-Poissonian, and their degree of second-order coherence is set by the cavity frequency rather than by the coupling strength, falling from 18.5 to 4.9 for H2 as the cavity is detuned from 5 to 20 eV. A parity selection rule makes the sign of the third cumulant a probe of the orientation of the molecular charge asymmetry along the cavity polarization.

Single-molecule FRET probes the conformational dynamics of biomolecules by measuring the distance between two dyes. The experiment produces a stream of coloured photons, which is only an indirect readout of these dynamics. Recovering the free-energy landscape and diffusion coefficient from such a photon stream is a difficult inverse problem. Existing approaches assume a small number of discrete states, bin the photons, or are computationally expensive. Here we derive an exact likelihood for the recorded photon stream under a model in which the dye distance diffuses on a continuous free-energy landscape. It uses the raw inter-photon times and colours at full time resolution, and analytically integrates out all hidden trajectories. The likelihood is differentiable, so automatic differentiation returns exact gradients with respect to all model parameters. This lets us jointly infer the free-energy landscape, the diffusion coefficient, and the photophysical parameters by gradient-based optimization. On simulated data, we recover free-energy landscapes, including ones with a short-lived intermediate, together with the diffusion coefficients. Uncertainties follow from the curvature of the likelihood, computed directly from the same gradients. The framework also guides experimental design. Before any data are recorded, we can evaluate how much a given acquisition setting reduces the resulting uncertainty. Evaluation on the GPU is fast, and independent traces are processed in parallel, so a single fit converges in minutes and scales to large datasets. The likelihood extends photon-by-photon analysis to continuous free-energy landscapes and diffusion coefficients, putting fast quantitative inference with uncertainties within reach of the smFRET community.

We present the orbital-optimized quasi-variational distinguishable cluster doubles (OQVDCD) method, obtained by applying the distinguishable cluster (DC) approximation to orbital-optimized quasi-variational coupled cluster doubles (OQVCCD). The resulting method retains a Hermitian-like energy functional, is size-extensive, obeys the generalized Hellmann-Feynman theorem at a stationary point, is exact in the isolated two-electron and two-hole limits, and scales as O(o2v4), like coupled cluster singles and doubles (CCSD). For the tested closed-shell reaction energies, it is shown that OQVDCD reduces the mean absolute deviation relative to OQVCCD from 1.20 to 0.81 kcal/mol, bringing the errors close to distinguishable cluster singles and doubles (DCSD). In the strongly correlated examples considered here, OQVDCD shifts the OQVCCD energies toward the benchmark, although the convergence remains somewhat less robust than for DCSD.

Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general-purpose MLFFs because generating high-quality ab initio reference data is computationally expensive. Classical empirical potentials offer a potentially inexpensive source of synthetic training data, but existing models often lack the accuracy needed to provide useful reference energies. Here, we introduce the density-based intermolecular potential (DensIP), a physics-based model of intermolecular interactions that uses machine-learned electron densities and only four universal parameters. We train and test DensIP on CCSD(T)/CBS interaction energies from DES15K, a dataset of dimers of small organic molecules. DensIP achieves sub-kcal/mol errors for dimers containing molecules absent from the training set, including molecules in non-equilibrium conformations, demonstrating strong transferability. We further show that DensIP can be applied to molecules as large as drug ligands. Notably, DensIP outperforms state-of-the-art general-purpose MLFFs for long-range interactions, making it a promising approach for generating accurate synthetic training data at scale.

Hierarchical equations of motion(HEOM) provide exact dynamics of open quantum systems coupled to harmonic baths, but their computational cost becomes prohibitive for systems with many independent local environments. In this work, we develop a symmetry-adapted HEOM formalism to significantly reduce the computational cost for the permutationally invariant Holstein-Tavis-Cummings (HTC) model. The method removes redundant information in two stages. First, all auxiliary density operators (ADOs) related only by relabeling identical molecules and their bath channels are replaced by a single canonical occupation-pattern representative. Second, molecules with the same local hierarchy occupation produce repeated matrix elements within each representative, allowing only the distinct complex variables to be propagated instead of the full (N + 1) $\times$ (N + 1) ADO matrices. The resulting matrix-free equations are evaluated using precomputed connections and molecular multiplicities. At fixed hierarchy depth L and number of bath correlation exponentials m, the number of canonical representatives becomes independent of the ensemble size for N $\geq$ L and the number of unique variables saturates for N $\geq$ L + 2. The formulation easily extends to multiple-exponential bath decompositions, arbitrary initial density operators, static disorders, and cavity loss. Our benchmarks reproduce conventional HEOM dynamics while requiring far fewer propagated variables and substantially less memory.

The discovery of spin-dependent luminescent properties of negatively charged boron-vacancy centers ($V^-_B$) in hexagonal boron nitride (hBN) enabled a new platform for quantum sensing with van der Waals materials. Particularly, the possibility of performing optically detected magnetic resonance (ODMR) for determining the electron spin resonance (ESR) frequencies of hBN color centers became a strong tool for quantum sensing of magnetic fields with submicrometric resolution. However, due to low ODMR contrast, current techniques proposed for mapping DC magnetic fields require hours of integration to obtain a magnetic image of a micron-sized region. In this work, we report the implementation of a frequency-tracking approach for real-time monitoring of ESR frequencies of localized $V^-_B$ centers in hBN. With this technique, magnetic field monitoring was used to map the field pattern generated by a micron-sized conical magnetic tip in only a few minutes. By controlling the magnetic sample's position relative to the quantum sensor, three-dimensional magnetic mapping of the field was achieved with diffraction-limited resolution and shot-noise-limited sensitivity of 54 $\mu$T$/\sqrt{\text{Hz}}$. Magnetic field gradients of 3.6 $\pm$ 0.2 $\mu$T/nm were measured with our system, in which a maximum detected field rate of 6 mT/s was achieved. The results of this study establish spin resonance frequency-tracking as a viable technique and fast method for minute-scale magnetic imaging, reducing acquisition times by at least one order of magnitude if compared to conventional techniques.