Papers

Use of the Kohn-Sham eigenvalue gap (highest occupied molecular orbital to lowest unoccupied, HOMO-LUMO) as a fundamental gap (I-A, I= ionization potential, A= electron affinity) estimator is a long-standing challenge for relatively simple exchange-correlation functionals, e.g. meta-GGAs. The LAK meta-GGA [T. Lebeda et al. Phys. Rev. Lett. 133, 136402 (2024)] was developed to emphasize certain constraints in ways intended to make the LAK KS gaps close to the HSE06 hybrid functional [A.V. Krudau et al. J.Chem. Phys. 125, 224106 (2006)] gaps. The LAK functional was tested against a small atomization data set and a very small bond length set, with no molecular HOMO-LUMO gap testing. Here we test LAK against widely used, large reference data sets for molecular heats of formation, bond lengths, harmonic frequencies, I, and A and compare with r2SCAN [J.W. Furness et al., J. Phys. Chem. Lett. 11, 8208 (2020)] as well as HSE06 and PBE0, a quite different hybrid. On these tests LAK does not give meaningfully better geometries, heats of formation, HOMO-LUMO gaps, or DFT IP theorem satisfaction than r2SCAN. Compared against experimental I and A values the same holds; LAK results resemble r2SCAN results, not HSE06. Moreover, the HOMO and LUMO shifts generated by the NCAPR GGA [J. Carmona-Espindola et al., J. Chem. Phys. 157, 114109 (2022)] as derivative discontinuity estimates, correct the bare NCAPR HOMO and LUMO eigenvalues to substantially better agreement with experiment for both fundamental gap and IP theorem than do the meta-GGAs (LAK, r2SCAN) or hybrids (HSE, PBE0). We discuss possible reasons why LAK behaves differently for molecules and solids.

Density functional theory (DFT) strikes a practical balance between accuracy and computational cost in many problems of computational chemistry and materials science. However, many DFT calculations are limited by fixed atom-centered basis sets, which dictate how accuracy and cost scale with system size. We propose Gaussian Splatting for Density Functional Theory (GS-DFT), which represents molecular orbitals as a cloud of Gaussians whose positions, shapes, and mixing coefficients are optimized jointly by gradient descent to minimize the energy without training data. Conceptually, GS-DFT is 3D Gaussian splatting with the renderer replaced by quantum mechanics. We introduce two key solver components: adaptive density fitting with screening for efficient evaluation of two-electron integrals, and a regularized differentiable orthogonalization of the molecular orbitals. Empirically, the optimized basis reaches the accuracy of the largest conventional basis sets with a fraction of the parameters, converging systematically in energy, density, and nuclear forces. At equal parameter count, it captures the stretched-bond and anion physics that fixed bases only recover with specialized basis augmentation. The resulting solver exhibits quadratic peak memory scaling in the cloud size, allowing us to simulate systems of up to 2,742 atoms (10,406 electrons) without any modifications at triple-zeta scale using a single four-GPU node.

Ferroelectric switching in heterostructures couples composition, layer topology, temperature, and interfacial boundary conditions. ReaxFF molecular dynamics isolates these variables in ZnO/ Zn1- xMgxO/ZnO and Zn1-xMgxO/ZnO/ Zn1-xMgxO stacks. Within pristine, initially single-domain models, coupling to switchable Zn1-xMgxO reduces the applied field required to reverse ZnO by up to fivefold. Temperature generally lowers the coercive field, whereas Mg concentration produces a nonmonotonic response. At equal ZnO and Zn1-xMgxO (ZMO) proportions, structures with ZnO at the center switch at lower fields than those with ZMO at the center at all four temperatures examined, demonstrating a topology-dependent response. Layer-resolved trajectories reveal topology-dependent switching sequences with direction-dependent redistribution of normal stress near the heterointerfaces, consistent with a stress-assisted cooperative pathway. Limiting MgO-containing structures exhibit sequential multilevel switching or low-polarity trapping, depending on thickness and temperature. Fixed-charge atomistic simulations complement previous continuum descriptions by resolving structural, energetic, and local stress evolution under a common applied field. TEM and STEM-EDS observations provide experimental structural context for the modeled architectures. Together, the results establish layer topology and interfacial mechanical confinement as design variables for wurtzite ferroelectric heterostructures.

We implement the Becke weight for atom A ($w_A$) as the partitioning scheme needed for phase space electronic structure theory. We show that this ansatz can lead to very efficient electronic structure calculations at nonzero nuclear momentum ($P \ne 0$). We further show that using Becke weights leads to efficient VCD calculations that are very stable with respect to basis set size. We hypothesize that, as far as quantum chemistry simulations in an atomic orbitals basis, the choice of Becke weights as a partitioning scheme will be adopted for all future phase space electronic structure calculations in an atomic orbital basis, as theorists learn to efficiently simulate coupled nuclear electronic dynamics through a new framework.

Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem. Multiscale simulations introduce an additional challenge: robust uncertainty quantification across scales. Even within one scale, computations are often multistage, producing a sequence of target quantities, each dependent on the previous, and each with some uncertainty. Conformal methods have emerged as a model agnostic framework for recalibrating surrogate predictions to produce sets which contain the truth at a user-specified rate. For multistage workflows, we require uncertainty calibration for multiple chemical properties and atomistic configurations, and we want to propagate uncertainty sets to downstream quantities of interest. Such propagation should capture the error cancellations which occur in many downstream targets in materials science; for instance, an approximate energy difference is often more accurate than individual energy predictions. We present the first exploration of multivariate conformal methods for chemical properties, including Bonferroni-corrected hyperrectangles, hyperellipsoidal sets based on the Mahalanobis distance, and custom loss functions within conformal risk control. Calibration is applied directly to predicted energies, atomic forces, and virial stresses, then propagated to elastic constants and vacancy formation energies employing a variety of commonly considered approximate protocols in materials modeling. We highlight the benefits of building correlation predictions into the conformal procedure, making it possible to build sets which capture near symmetries and error cancellation. We conclude with a discussion of the interplay of the employed approximate computational protocol and conformal guarantees.

Optimizing atmospheric pressure interfaces requires balancing efficient ion transport with unwanted collision activation. The complex environment at the inlet region, including rapid pressure drops, RF/DC electric fields, ion-gas collisions, and space charge effects, makes simulation of this system challenging. In this work, we introduce MuCITE (Multiphysics simulation for Continuous Ion Transport and Energetics), which combines continuous ion injection, precomputed electric and gas flow fields, and particle-in-cell space charge updates. Furthermore, by employing improved impulsive collision theory, it can not only predict transport but also analyze the loss position of ions and the evolution of their kinetic energy, internal energy, and dissociation probability during transport. Transport predictions with ion currents and activation trends of in-source fragmentation were compared with experimental measurements using a custom-built source and a Q-Exactive HF source. The results show that the simulation predictions agree well with the experimental results and can serve as a reference before experiments. MuCITE allows users to derive ion transport, output beam characteristics, and ion energy through the configurable interface geometry and operating conditions. It supports simulation-guided design and tunable parameters to achieve predicted efficient ion transport or in source activation.

We present a general framework for simulating the nonequilibrium vibronic dynamics of molecules interacting with metal surfaces, which utilizes non-Markovian electronic friction and the corresponding generalized Langevin equation. The method employs a Markovian embedding scheme to sample coordinate-dependent quantum colored noise, with the underlying memory kernels represented systematically by exponential decompositions obtained directly from the electronic forces. In doing so, the approach extends the widely used electronic friction and Langevin dynamics formalism beyond the conventional Markovian approximation while retaining the coordinate dependence of the electronic back-action. We demonstrate the method for nonequilibrium charge transport through a vibrationally coupled molecular junction, where comparisons with numerically exact hierarchical equations of motion simulations show that the non-Markovian formulation is both more accurate and more robust than its Markovian counterpart. In particular, we find that non-Markovian electronic forces play a decisive role in the nonequilibrium vibrational dynamics, stabilizing regimes in which the conventional Markovian approximation fails.

Growing biochemical systems are intrinsically noisy, with one important source of stochasticity arising from the discrete reaction events of the underlying biochemical network. Because growing systems do not generally admit stationary abundance distributions, it is unclear how to separate this intrinsic chemical noise from other sources of variability in population and single-cell data. Here we develop a large-deviation theory for exponentially growing stochastic chemical reaction networks by decomposing abundance into volume and composition. In these coordinates, balanced growth corresponds to a stable composition together with exponentially increasing volume. We show that composition fluctuations satisfy a large-deviation principle with speed equal to the volume of the growing system, and the corresponding quasipotential is selected by a solution of the contact Hamilton–Jacobi equation. We also derive a fluctuation theory for accumulated observables and show that their covariances decay with accumulated-volume, rather than with physical time. Applications to a minimal autocatalytic network and a coarse-grained cellular growth model demonstrate how the theory predicts composition quasipotentials, growth-rate fluctuations, and correlations between observables. The framework therefore provides a stochastic null model for single-cell heterogeneity generated by the intrinsic stochasticity of the underlying chemical reaction network.

Selective activation of dinitrogen (N$_2$) under mild conditions is difficult: N$\equiv$N is one of the strongest bonds in chemistry, and most heterogeneous catalysts capable of breaking it require high temperature and pressure. Atomically dispersed transition-metal sites offer a computationally tractable route to studying the strongly correlated intermediates involved. We connect periodic DFT calculations with correlated active-space calculations on a finite, non-periodic surface fragment, demonstrating the workflow for the hydrogenation step RuH$_2$(N$_2$)* $\rightarrow$ RuH(NNH)* at an isolated Ru$_1$ site on Ru(0001). A first-shell fragment is extracted from the periodically relaxed structure, an active space is selected with Active Atomic Valence Space (AVAS) around the Ru-N/N-H bond reorganization, and natural-orbital truncation reduces its cost while retaining the relevant correlated degrees of freedom. The reduced Hamiltonian is mapped to qubits with the Jordan-Wigner transformation and solved with the adaptive derivative-assembled pseudo-Trotter variational quantum eigensolver (ADAPT-VQE). Dynamic correlation beyond the active space is examined with strongly contracted NEVPT2 and DSRG-MRPT2. AVAS gives a 22-qubit active space, which natural-orbital truncation compresses to 16 qubits, reproducing the untruncated CASCI state energies to within 0.21 kcal/mol (reaction-energy error 0.2 kcal/mol). Statevector ADAPT-VQE in this reduced space converges for both reaction states under a fixed pool-gradient stopping criterion. NEVPT2 yields anomalously large, state-imbalanced corrections for the finite Ru fragment, while DSRG-MRPT2 retains an endothermic reaction energy at its default flow parameter, although its magnitude depends strongly on that parameter.

Direct time-resolved observation of large amplitude dynamics during ground state statistical unimolecular decay remains an experimental challenge. Here, an experimental Time-Resolved Coulomb Explosion Imaging (TR-CEI) study (J. Chem. Phys. 151, 174301, 2019) of the canonical unimolecular reaction NO$_2$ $\rightarrow$ NO($^2\Pi, v=0, J)$ + O($^3P_j$) at threshold dissociation energies is modeled and analyzed. Quasi-classical trajectory (QCT) simulations on an accurate and validated ground state potential energy surface (PES), with energy and angular momentum constraints from known product state distributions, are used to characterize the dissociation dynamics. TR-CEI projects suddenly, via ultrafast Strong Field Ionization (SFI), the evolving neutral ground state wavepacket onto the repulsive doubly charged cationic state which then impulsively fragments into the NO$^{+}$ + O$^{+}$ ion pair. Within the Coulomb recoil approximation, which applies at longer NO–O (Jacobi) separations, the ion pair CEI-energy is inversely proportional to the scalar distance between the neutral ground state NO and O moieties at the moment of SFI, thus providing a direct measure of the evolving average NO–O distance during unimolecular decay. Results from QCT simulations agree favourably with the TR-CEI measurements. Phase space evolution, recurrence time distributions and Lyapunov exponent analysis are presented which support the conclusion that TR-CEI directly observes phase space evolution during unimolecular decay. For the present system, the time scale for the phase space distribution to achieve statisticality is estimated to be 2 to 3 ps.

Spin-orbit coupling and electron correlation play important roles in a broad range of chemical and physical phenomena. In this work, we systematically assess the performance of single-reference algebraic diagrammatic construction combined with state interaction (SI-ADC) for describing electronic structure in the presence of spin-orbit coupling. We find that second-order SI-ADC methods (SI-ADC(2)) achieve zero-field-splitting accuracies comparable to those of higher-level and more computationally demanding approaches, including variational four-component ADC and two-component ADC methods based on spin-orbit effective Hamiltonians. We further demonstrate the broad applicability of SI-ADC by evaluating a diverse range of spin-orbit-coupled properties, including electron affinities, ionization potentials, neutral excitation energies, core-ionization energies, and magnetic g-tensors, for systems ranging from small molecules to transition-metal complexes and f-block compounds. Across these applications, SI-ADC(2) methods provide consistently accurate results, likely benefiting in part from favorable error cancellation between electron-correlation and spin-orbit effects. In contrast, SI-ADC(3) methods are generally less accurate, particularly for systems containing transition-metal and heavy elements. Overall, these results establish SI-ADC as an efficient and broadly applicable framework for incorporating spin-orbit coupling into ADC calculations of electronic and spectroscopic properties.

Ultra-high-volume slag-cement (UHVS) blends can substantially reduce clinker ratio, but slow slag reaction limits strength development; Na2CO3 is a less caustic and lower-carbon activator for alkali-activated materials, yet its moderate alkalinity can similarly delay strength development. Here, dry-blended Na2CO3 (0-20 wt.% relative to water) was incorporated into a one-part binder with 90 wt.% ground-granulated blast-furnace slag and 10 wt.% Portland cement (PC) to determine whether this integration could mitigate slow strength development in both systems. Compressive strength testing, isothermal calorimetry, time-resolved quantitative X-ray diffraction, Fourier-transform infrared spectroscopy, aqueous solution analysis, and thermodynamic modeling were combined to determine how Na2CO3 controls phase assemblage, alkalinity, calculated porosity and strength development. At 5-10 wt.% Na2CO3, calcite-dominated carbonate reactions promoted a rapid alkalinity increase and accelerated early-age reaction and strength development; however, subsequent alkalinity decline limited later-age slag reaction and gel formation. In contrast, 20 wt.% Na2CO3 favored substantial early formation of gaylussite and carbonate-AFm phases, which moderated the initial pH rise and delayed early gel accumulation. The larger alkali inventory and evolving phase-solution partitioning helped sustain alkalinity at later ages, enabling continued slag dissolution and gel formation. This pathway produced higher reaction extent and lower calculated porosities, with compressive strength approaching that of neat PC. Screening-level cradle-to-gate life-cycle assessment indicates a 66-72% reduction in global warming potential relative to PC, depending on Na2CO3 production pathways. These results identify carbonate-mediated control of alkalinity persistence as key to sustaining slag reaction in UHVS systems.

Molecular dynamics simulations are used to compute the potential of mean force (PMF) between two united-atom methane molecules at several temperatures and integration time-steps. At a fixed time-step, the contact minimum of the PMF deepens with increasing temperature, as expected for hydrophobicity driven association. Compared with a time-step of 0.5 fs, one that preserves equipartition, larger time-steps alter the PMF and make the contact minimum more favorable. Thus even relative free energy values are sensitive to time-steps that break equipartition. Using quasichemical theory, we partition the free energy of association into hydrophobic and hydrophilic contributions. The hydrophilic contribution opposes association and is insensitive to the time-step. Thus the artificial enhancement of association at larger time-steps comes entirely from the hydrophobic contribution. We explain this behavior through the temperature dependence of the internal pressure of the liquid. The same analysis also accounts for earlier observations of the liquid's p-V behavior under conditions that break equipartition.

Macroscopic materials function is determined not merely by equilibrium structure, but by how carriers, phonons, fields, defects, interfaces, and collective order dynamically evolve after perturbation. Ultrafast electron microscopy (UEM) uniquely bridges this gap, coupling femtosecond-to-nanosecond timing with real-space, reciprocal-space, and energy-resolved contrast. Here, we review how these integrated capabilities now quantitatively map energy flow and conversion in electronic materials, decode electronic-structural coupling in quantum and correlated systems, and isolate the localized propagation of carriers, polaritons, strain, and phonons across optoelectronic and nanomechanical architectures. We emphasize the predictive design rules now emerging from direct metrology of carrier-lattice coupling, momentum-resolved phonon thermalization, defect-controlled phase transformations, and authentic operando switching. We critically assess UEM's ongoing maturation from a qualitative, proof-of-concept experiment into a rigorous, quantitative materials platform. This paradigm shift is actively driven by integrated multimodal detection, advanced energy-resolved spectroscopy, high-repetition-rate sources, multidimensional (4D and 5D) acquisition, and physically constrained forward modeling. Ultimately, the central opportunity lies in establishing a complete, causal link from initial excitation to localized energy flow, transient structure, and macroscopic device function. By prioritizing quantitative reproducibility and robust mechanistic interpretation, UEM is positioned to engineer materials in functional states that equilibrium thermodynamics simply cannot describe.

Elemental substitution within existing structural frameworks is a widely applied strategy for developing advanced materials. Yet, optimizing target properties while maintaining phase purity usually demands extensive trial-and-error, which becomes substantially inefficient when navigating a complex design space. Here, we introduce a strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions that guide autonomous experimentation in a truly self-driving and self-learning mode. Specifically, we developed a cost-guided autonomous solid-state synthesis (CASS) framework and demonstrate its application in the discovery of Na superionic conductor (NASICON) solid electrolytes. CASS successfully optimizes ionic conductivity and phase purity, leading to the identification of 18 promising compositions in 78 trials conducted in an autonomous laboratory, the A-Lab. Among these, we identified two fast-conducting NASICONs yielding total (bulk) ionic conductivity of 0.7 (3.96) and 0.3 (3.17) mS/cm. The successful deployment of CASS reinforces the potential of coupling self-driving autonomous laboratories with physics-informed generative models to accelerate materials discovery. Moreover, the interpretability of the design factors, informed by outcomes from both successful and failed syntheses, enables model refinement and generation of new chemical insights.

The interaction between conjugated molecules and intense electric field pulses drives a plethora of intriguing nonlinear phenomena, including optical limiting. While excited-state absorption was recently identified as the primary mechanism for this effect in thiophene oligomers, open questions remain regarding field-dependent population dynamics under intense laser driving. Using real-time time-dependent density functional theory combined with a determinant-overlap population framework, we track the intensity-dependent nonlinear response of a single thiophene ring (1T) and quaterthiophene (4T) as prototypical members of the oligothiophene family with different length and symmetry. We demonstrate that extended $\pi$-conjugation in 4T lowers nonlinear excitation thresholds by orders of magnitude compared to 1T. State-resolved population dynamics reveal complete ground-state depletion accompanied by sequential excited-state absorption channels under intense driving. High-harmonic generation spectra explicitly reflect molecular symmetry constraints, while energy-resolved occupation densities confirm continuous orbital redistribution across the $\pi^*$-manifold. This work provides dynamic physical insights for engineering organic materials with tailored nonlinear optical properties.

The dynamical GW self-energy approximation is a key computational tool to provide the fundamental spectrum of electronic systems. We reformulate this approximation, representing the particle and hole parts of the GW self-energy through a highly compact set of Cayley-transformed moment constraints. The Cayley transformation maps real frequencies to the unit circle, keeping the moments bounded as their order increases, ensuring numerical stability and allowing resolution to be focused on an energy range of interest. We calculate these Cayley-transformed moments via an efficient O[N$^4$] scaling contour integration, and from them, construct a Hermitian upfolded Hamiltonian with a linearly scaling dimensionality with system size. A single-shot diagonalization of this effective Hamiltonian gives an explicit full-frequency G0W0 Green's function with manifestly real poles and non-negative spectral weights. This enables quasiparticle energies, satellite features, and their spectral weights to be obtained across the full G0W0 spectrum. Comparisons with exact G0W0 calculations and convergence across the GW100 test set and the larger Chlorophyll A molecule demonstrate substantially faster and more reliable convergence with moment order than an earlier monomial-moment approach. These Cayley moment representations therefore provide a stable, compact, and systematically improvable route to the complete spectral information of zero-temperature GW, without explicit frequency grids, plasmon-pole models and other common approximations, or analytic continuation.

General-purpose machine-learning interatomic potentials (MLIPs) for organic reactions need to be accurate on both the minimum energy path (MEP) for static evaluation of basic properties and the broader configurational space for simulating reaction dynamics. Existing general datasets for gas-phase organic reactions rely on quasi-static relaxation that confines configurations to the MEP vicinity, so models trained on them could fail on direct molecular-dynamics trajectories; the gap is methodological, not a question of dataset size. We introduce a spatiotemporally resolved, system-independent collective variable (CV): Cartesian RMSD within randomly partitioned local domains against an expanding list of time-averaged reference geometries. The CV drives metadynamics as the main exploration engine, supplemented by structural relaxation towards transition state (TS) to augment the coverage around TS. Within a concurrent-learning workflow, this produces OpenRxn26, a dataset of 1.8~M DFT-labeled configurations covering neutral singlet unimolecular reactions in the H/C/N/O chemical space ($N_\mathrm{heavy} \leq 30$), containing reactive atomic environments underrepresented in community datasets. Trained on OpenRxn26, a DPA3 model (denoted DPA3_rxn) achieves transferable accuracy on barrier heights and reaction energies. On off-MEP reactive trajectories, DPA3_rxn is the only model in the benchmark suite to reach 1.0 kcal/mol energy accuracy compared with the labeling method, where the domain MLIP leading on static benchmarks degrades several-fold (e.g. MACE_OMol25), showing the insufficiency of quasi-static sampling and MEP-anchored benchmarks for guaranteeing dynamics reliability of MLIPs. OpenRxn26 thus provides MD-ready reactive training data for gas-phase neutral singlet organic reactions, verifying the generality and efficiency of the sampling strategy.

Vibrational spectral prediction can become inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate characteristic spectral fingerprints, making prediction across external chemical space difficult. SO(3) Equivariant Neural Kalman Networks (SENK) form a response-state cascade that combines an equivariant transformer backbone for Hessian, dipole-derivative and polarizability-derivative learning, an Equivariant Neural Kalman bridge for state-dependent refinement and reliability sensing, and an NBO-informed electronic-prior pathway coupling consistency regularization with bounded, branch-specific guided spectral calibration. SENK outperforms DetaNet on QM9S and QMe14S while preserving full-spectrum IR and Raman fidelity from small molecules to drug-like systems. SENK remains stable and selectively improves spectrally sensitive features in biomolecular systems with complex stereoelectronic effects. It therefore integrates tensor prediction, reliability diagnosis and physics-informed calibration, supporting transferable vibrational spectroscopy from molecular systems to functional molecular materials.

Raman preprocessing and enhancement methods are often assessed by comparing their output with a clean reference spectrum using measures such as mean squared error (MSE). Whether these spectral quality measures reflect downstream analytical performance is rarely tested. We propose a controlled-perturbation framework for this test. Five perturbation types (baseline distortion, independent noise, correlated noise, global wavenumber shift, and nonlinear axis warping) at eight strengths produce paired changes in a quality measure (metric harm) and in downstream performance (task harm). The alignment gap (AG) quantifies how much a single monotone metric-to-task relationship, fitted by isotonic regression, improves when each perturbation type receives its own relationship. Ordering concordance (OC), a clustered Kendall-type index, measures how often a measure ranks conditions of different perturbation types in the same order as their task harm. Cluster-bootstrap intervals and Holm-adjusted sign-flip tests compare twelve candidate measures with MSE. Three public datasets are used: bacterial classification by principal component analysis and logistic regression, sugar-mixture quantification by partial least squares regression, and mineral identification by cosine library matching. Models are fitted to unperturbed or to correspondingly perturbed training spectra. For bacterial classifiers fitted to unperturbed spectra, Wasserstein distance improved both statistics relative to MSE (Holm-adjusted p < 0.05), also after axis perturbations were removed or spectra were compared on a common grid; refitting to perturbed spectra reversed this advantage. Peak-based measures and a structure-to-noise ratio improved both statistics only for refitted sugar calibrations, and no candidate did so for mineral identification. Open code supports testing new measures.

AI agents are advancing the automation of molecular dynamics (MD) research, with the potential to accelerate discovery and design of lithium-battery electrolytes. However, whether they can autonomously complete scientifically reliable electrolyte MD studies remains insufficiently evaluated. We introduce ElectrolyteMD-Bench, which unifies dilute, high-concentration, and localized high-concentration electrolytes in a comparative study across distinct solvation regimes. The benchmark connects local coordination, ionic association, single-particle dynamics, and collective transport within one continuous research process. Agents autonomously perform system construction, equilibration, production sampling, property analysis, and interpretation under a frozen scientific model, while deciding whether to correct errors, continue sampling, or stop. We audit each study along four dimensions: simulation validity, analysis validity, evidence integrity, and outcome calibration. Across eight model–harness configurations, all 24 system runs produced valid production trajectories, yet none of the eight studies satisfied all scientific requirements. Failures included missing analyses, incorrect physical definitions or numerical implementations, insufficient statistical support, and completion claims inconsistent with retained evidence. These results expose a gap between successful MD workflow execution and reliable scientific study completion. ElectrolyteMD-Bench therefore provides a foundational test and defines a key capability threshold that autonomous electrolyte simulation must cross to advance toward computation-first materials exploration.

Methane pyrolysis experiments were performed in a micro flow reactor to investigate the chemical mechanism sensitivities at elevated pressures and temperatures. The micro flow reactor used in this study provides independent control over reaction parameters. Experiments were performed at temperatures from 1400 to 1800 K and pressures from 1 to 10 bar at a fixed residence time of 60 ms. The yields of hydrogen, unreacted methane, and solid carbon were quantified for comparison to predictive models. The mass of heterogeneous depositions was also quantified. The presence of C2 and PAH species were measured by GC-MS. The results were compared to 1D simulations using a discrete sectional model for capturing the kinetics of particulate formation and surface deposition. The simulations replicated the pressure trend well at high temperatures (1800 K), but the sensitivity to temperature was in disagreement. Simulations generally found lower carbon and hydrogen yields and a stronger sensitivity to temperature than the experiment. An abundance of PAH intermediates was measured at lower temperatures and higher pressures. The simulated surface deposition rates were lower than the measured values but showed similar profiles. The results demonstrate the utility of micro flow reactors in recreating pyrolysis environments for hydrogen production from methane. The predictive capabilities of simulations could be improved by advancing the fuel decomposition and PAH formation and consumption kinetics at low temperatures and by detailed treatment of surface site density.

In scientific machine learning, $\Delta$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate that residual scale alone is an insufficient heuristic for learnability. Evaluating molecular graph neural networks on total energy targets, we show that complex local descriptor baselines can yield small residual targets that are disproportionately rough within architecture-informed proxy spaces and harder to learn relative to their scale. Conversely, semi-empirical baseline reduces both scale and normalized roughness, improving in-domain and out-of-domain prediction. We introduce scale-normalized graph Dirichlet roughness ($D_{\text{IQR}}$) as a pre-training diagnostic for residual learnability and establish baseline complementarity as a core target-design principle, elevating target space formulation alongside model architecture as a key axis for scientific machine learning.

A new simulation approach (J. Chem. Phys. 163, 164113), based on the seamless combination of the electron transfer at the electrode/electrolyte interface with the charge and mass transport in electrolyte solutions, holds the promise to advance the comprehensive modeling of basic electrochemical devices such as electrolytic cells and batteries. The underlying model also includes a fully consistent treatment of structural, thermodynamic and statistical properties of the electrical double-layer at non-ideal electron-conducting interfaces. As a result, subtle aspects such as Tafel and Butler-Volmer equations, the overpotential concept, as well as (stationary) non-equilibrium features such as the entropy production, arise naturally from the model, with a minimum of ad-hoc assumptions and elaborations of the simulation results. We illustrate these aspects for a simple model of electrochemical interface, and discuss further improvements of the method meant to enhance its ability to model systems and phenomena of interest for electrochemistry. One such improvement, concerning the effect of fluctuating electric fields on the electron transfer rate, is explicitly developed.

The transition state barrier is a foundational concept in chemical kinetics, yet its physical origin is seldom investigated. In this study we analyze the barrier to hydrogen atom transfer in collinear H$_2$+H$^{\bullet}$ on its Born$-$Oppenheimer surface. We extend upon the energy component analysis of chemical bonds to develop an electronic kinetic energy-based interpretation of the transition state barrier. This approach reveals that Pauli exclusion is necessary for the existence of the energy barrier to H$_2$+H$^{\bullet}$ atom transfer, which is consistent with a complementary analysis using absolutely-localized molecular orbitals.

Fast-neutron scintillators must efficiently convert neutron energy into detectable light while enabling selectivity between neutrons and the {\gamma}-ray background. Here, we report a synthesis strategy for layered two-dimensional (2D) lead halide perovskite scintillators with independent control of the macroscopic detector geometry and microscopic phase composition. Our synthesis approach produces mm-thick, large-area scintillators with a deliberately engineered multi-quantum-well energy landscape, creating an effective spectral separation while retaining nanosecond emission dynamics. Our scintillators exhibit an energy resolution of 5.4% at 662 keV under 137Cs irradiation. Under deuterium-deuterium fusion neutron irradiation, we directly demonstrate neutron and {\gamma}-ray event separation by pulse shape discrimination, with a figure of merit of 3.51. Taken together, these results establish multi-quantum-well 2D perovskites as a scalable materials platform that combines fast-neutron sensitivity and selectivity within a controllable detector form factor.

Simulations of electrochemical materials and systems accelerate technological progress, but are still limited by computational power. In particular, quantum computing offers prospects for higher resolutions, due to the exponential amount of data that can be stored in a quantum state. As current quantum computers are still noisy, we consider a hybrid quantum-classical algorithm, that divides the problem into smaller computational tasks. We describe how to implement such an algorithm for non-linear partial differential equations, the Feynman-Kitaev Hamiltonian, in a scalable way for an electrochemical system. We show how it can evaluate general electrochemical models and present a quantum simulation of the Single Particle Model with electrolyte (SPMe) as the first quantum simulation of a battery cell.

Strong light-matter coupling offers a powerful framework for modifying molecular structure, dynamics, and optical response, yet achieving this regime at the single-molecule level remains a central challenge. Scanning Tunneling Microscope Break Junctions (STM-BJ) provide a promising route by combining single-molecule addressability with extreme nanoscale field confinement. However, the metallic environment that enables such confinement also introduces substantial plasmonic and excitonic losses, raising the question of how strong coupling can be realized in a metal-molecule-metal junction. Here, we identify the voltage as the key control parameter. We show that the applied bias not only drives the formation of an interfacial exciton through resonant charge transport but also suppresses its coupling to metallic loss channels. Simultaneously, the plasmonic cavity retains extreme mode confinement while the availability of modes is moderately tuned by voltage. Together, these results establish voltage-driven STM-BJs as electrically tunable single-molecule plasmonic cavities and open new routes for using the powerful STM-BJ toolbox, including quantum transport, tracking chemical dynamics, optoelectronics, and molecular-scale quantum control under strong light-matter coupling.

Broadband quantum memory devices are essential elements for future quantum networks. Here we propose a broadband quantum memory scheme called Hole Anti-hole Grating Echo Memory (HAGEM) for rare-earth ions in solids, which can be implemented with most of the present rare-earth ions hosted in various types of solids. Using HAGEM, a quantum memory device with one billion temporal modes per second can be developed with current available technologies. To validate the memory protocol, we conducted some proof-of-concept experiments. We provide a Eu$^\text{{3+}}$ molecular complex with special hyperfine level structures of which the hyperfine level separations are in a specific mathematical correlation that can be obtained by harnessing chemical engineering. Using the proposed memory protocol and material, we experimentally demonstrate a single-mode quantum optical storage efficiency of 14.9% and a memory bandwidth of 200MHz, which can easily be extended to a few GHz. In addition, a multi-mode storage of four temporal modes is achieved with a storage efficiency of 1.3%. Broadband & large-multimode quantum memory device with a long storage time enabled by HAGEM integrating with the current available entangled photon pair source and tunable delay lines, a quantum repeater network with an entanglement distribution rate on the order of 1kHz - 10kHz can be created with a communication distance around 100km. This will greatly facilitate the development of quantum networks in near future.

We address the interpretation of transient spectroscopy signals proportional to average excitation number in terms of discrete number of excitations. Building on our recent intensity cycling approach, we derive a general scheme to isolate multi-particle dynamics from any transient signal under minimal assumptions. The key result is a general formula expressing the extracted nonlinear signal terms by multi-excitation propagators. We demonstrate the approach on exciton transport and annihilation in organic nanoparticles, and carrier recombination in silicon nanocrystals.