Papers

Hydrogen tunneling is central to many chemical and biological processes. Herein, we introduce the vibronic-SHAKE (V-SHAKE) approach to comprehensively sample energy-conserving molecular configurations enabling hydrogen tunneling. A constrained form of nuclear-electronic orbital multistate density functional theory (NEO-MSDFT) dynamics, where the tunneling hydrogen nucleus is quantized, is used to sample geometries in the diabatic seam space corresponding to the intersection of the reactant and product NEO-DFT diabatic vibronic surfaces. V-SHAKE is applied to hydrogen and deuterium tunneling in 4-cyanobutanolate and Z-4-hydroxybut-3-en-2-one. The vibronic coupling is found to vary significantly in the diabatic seam space, mainly due to changes in the donor-acceptor distance. The reaction coordinate and gradient of the vibronic coupling at the minimum energy crossing point are nearly orthogonal and are dominated by motions stabilizing the product relative to the reactant or decreasing the donor-acceptor distance, respectively. V-SHAKE provides fundamental insights and validation for assumptions underlying rate theories.

For a simple model fluid, the Yukawa fluid, the condition for dynamic crossover, known as the Frenkel line, is defined. The condition is related to the fact that the roton minimum in the dispersion relation of longitudinal acoustic-like excitations exists only when the collective vibrational dynamics of particles dominates in the liquid. Based on the self-consistent relaxation theory for the Yukawa fluid, thermodynamic states are determined in which the roton minimum disappears. The obtained values of the state parameters for the Frenkel line are consistent with the results of studies in which the position of this line on the phase diagram of the Yukawa fluid was determined using molecular dynamics simulations. It is shown that the Frenkel line in this system can be determined directly from the structural characteristic – the static structure factor. A physical interpretation of the roton minimum frequency for the simple liquids near the Frenkel line is proposed.

The multi-state Bennett acceptance ratio (MBAR) equations combine the data collected under different thermodynamic conditions in a statistically optimal way. Due to their practical importance, several solution strategies have been devised to optimize the convergence of the resulting set of coupled equations. However, even with graphics processing unit (GPU) acceleration, the convergence of these methods can still be either unstable or slow. We propose a new approach where the equations are interpreted as the limit of infinitesimal bin width of the respective weighted histogram analysis method (WHAM). In the proposed binned-to-unbinned MBAR (BU-MBAR) method, the bin width is adapted dynamically to ensure a stable and efficient convergence to the asymptotic solution.

Bubble detachment from solid surfaces governs heat, mass, and charge transport across technologies vital to clean energy, including high-current-density water electrolysis and boiling thermal management. At high gas fluxes, however, bubbles remain trapped at active surfaces despite immense buoyancy, severely restricting mass transfer and increasing energy losses. Here, we show that this unexpected surface retention originates from cascade coalescence between unequal-sized bubbles. High-speed observations around microelectrodes demonstrate that when a rising bubble merges with a smaller surface-attached successor, its trajectory abruptly reverses, accelerating toward the substrate at nearly two orders of magnitude above its rising speed. Direct numerical simulations and scaling analysis reveal that asymmetric interfacial retraction during merging generates non-canceling viscous stresses, producing a net downward impulse toward the smaller bubble. Repeated cascade coalescence events accumulate these transient impulses into a steady, time-averaged retaining force capable of opposing buoyancy three to four orders of magnitude beyond quasistatic limits. Our findings establish bubble coalescence as a previously unrecognized mechanism that dynamically sustains bubble retention under high gas flux.

Second-order Moller–Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional $O(N^5)$ cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quantum chemistry. Here, we apply STC to the exchange contribution of Laplace-transformed MP2, which is the source of $O(N^5)$ scaling in the formulation. The resulting STC exchange algorithm has an $O(N^3)$ deterministic setup cost and an $O(N^2)$ stochastic cost at fixed absolute error. The estimator is unbiased and provides a way to specify the target stochastic error by estimating the number of required samples before the full calculation. We implement the algorithm using a hybrid deterministic–stochastic evaluation strategy, with grouped index sampling, to reduce the computational prefactor. Over a range of benchmark molecules containing up to $\sim 7000$ basis functions, the stochastic exchange evaluation takes as little as $1/270$ of the time of a complete DF-MP2 calculation on the same system. Within the Laplace-transformed formulation, the $O(N^4)$ scaling direct contribution is thus the only significant cost. Our results further substantiate the power of STC to serve as a general tensor-contraction engine for quantum chemistry.

Electrochemical reaction rates are controlled by both interfacial charge-transfer kinetics and reactive-ion transport. In charged porous media, fixed charges enrich reactive counterions near pore walls, enabling transport beyond the classical diffusion limit via surface conduction (SC). Under overlimiting conditions, classical Butler-Volmer kinetics predict indefinitely increasing current with overpotential, contrasting with microscopic electron-transfer theories, which impose a finite reaction-limited current. Here, we couple the one-dimensional leaky membrane model to coupled ion-electron transfer (CIET) kinetics to examine the interplay between transport and reaction limitations. The limiting behavior is governed by the scaled surface charge $(\tilde{\rho}_s)$ and a Damköhler number $(Da)$ comparing reaction-limited and diffusion-limited currents. We derive analytical limiting-current expressions for neutral, positive, and negatively charged porous media, mapping underlimiting-to-overlimiting transitions in the $(Da,\tilde{\rho}_s)$ plane. By preventing reactive-ion depletion, SC restores polarization-curve sensitivity to charge-transfer kinetics that would otherwise be obscured by diffusion limitation. Fitting to published Cu electrodeposition data in charged AAO membranes yields $Da\approx24$. CIET parameters fitted to AAO($-$) also describe AAO($+$) and predict a finite AAO($-$) reaction limit beyond the applied voltage range. These results provide a framework for distinguishing transport-limited and reaction-limited responses in electrochemical systems and establish charged porous media as platforms to reveal electrochemical reaction-kinetic descriptors.

Calcium carbonate formation from aqueous solution is central to biomineralization and to carbon sequestration through mineral carbonation. At near-neutral pH, the process is highly reactive, with proton transfer mediating the interconversion between carbonate species. Most atomistic simulations to date either treat carbonate speciation as fixed or consider proton transfer only in small clusters. Here, we combine an ab initio trained atomic cluster expansion (ACE) machine-learning potential for molecular dynamics with enhanced sampling to enable reactive simulations of the early stages of calcium carbonate precipitation at previously inaccessible length and time scales. We study proton transfer and carbonate speciation in ion pairs and triplets, as well as in the collective aggregation of many ions. Our simulations with few ions show that ion association provides a favorable pathway for proton transfer, facilitating interconversion between carbonate, bicarbonate, and carbonic acid. In many-ion systems, proton transfer occurs spontaneously alongside aggregation, and we observe significant changes in the coordination environments as species evolve during the simulations. These results show that ion aggregation and chemical reactivity can be strongly coupled during the early stages of nucleation from solution.

Low-temperature operation imposes severe limitations on lithium-ion transport in battery electrolytes, yet the coupled effects of solvent composition and fluorinated additives in the cold-temperature regime remain insufficiently resolved. Here, we combine classical molecular dynamics (MD) and machine learning (ML) to investigate 1 M LiPF$_6$ electrolytes containing ethylene carbonate (EC), ethyl methyl carbonate (EMC), and EC/EMC (3:7), with 0-10 mol% fluoroethylene carbonate (FEC), from 298 to 233 K. MD simulations quantify Li$^+$ self-diffusion, Nernst-Einstein (NE) and Green-Kubo (GK) conductivities and local coordination, while Gaussian-process surrogates model conductivity across composition and temperature. Cooling produces a pronounced transport penalty, particularly in EMC-containing electrolytes, whose GK conductivity decreases by more than 98% at 233 K, compared with approximately 90% in EC-rich systems. Li$^+$ self-diffusion activation energies are 0.49-0.54 eV for EMC-containing systems and 0.27-0.29 eV for EC-based systems. Within the EC family, 0-2 mol% FEC gives comparable cold-temperature transport, whereas 5-10 mol% FEC shows lower conductivity retention at the coldest simulated temperature. The analyzed coordination channels remain solvent dominated, while direct Li$^+$-FEC coordination is not quantified in the present RDF set.The Gaussian-process surrogates achieve composition-disjoint cross-validated RMSE values of 0.56 and 0.57 mS cm$^{-1}$ for NE and GK conductivity. Within the simulated liquid-state trajectories, temperature and host-solvent composition dominate the bulk-transport response, with FEC acting as a secondary modifier.

Liquid interfaces are both ubiquitous and also critically important for many commercial products, modern technologies, biological processes, and environmental phenomena. The properties of these interfaces can depend quite sensitively upon the composition of the bulk liquid phase. In this work, we develop a dilute solution theory (DST) for the influence of dilute cosolutes upon the interface between coexisting liquid and vapor phases. We employ a grand canonical perturbation theory to rigorously relate the interfacial free energy to the concentration of the liquid solution. We leverage a corresponding Gibbs ensemble to treat liquid-vapor coexistence and to eliminate the contribution of the bulk phases from this free energy. We express the coefficients of the resulting expansion in terms of microscopic partition functions. By treating solute-solute interactions to lowest order, we distinguish between the intrinsic and effective interfacial preferences of solutes. While the former reflects only solute-solvent interactions, the latter depends upon the solution composition and reflects the influence of solute-solute interactions. We assess this DST with molecular dynamics simulations of binary and ternary mixtures of Lennard-Jones spheres. These simulations demonstrate that DST accurately models the interfacial properties of these systems up to relatively high concentrations. Moreover, the simulations illustrate the impact of attractive solute-solute interactions in converting weak intrinsic surfactants into weak effective depletants.

Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecular design, and physics-based validation through molecular simulations, all unified by a single polymer representation. We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance. As an example, the generative design pathway is applied here to the discovery of sustainable alternatives to persistent fluorinated polymers, where it is possible to identify and independently validate PFAS-free candidates with target surface-energy properties. This work demonstrates how polymer-native AI can accelerate discovery by linking representation, prediction, and design across complex polymer chemistries.

Large-scale molecular dynamics simulations of particle systems on multi-GPU architectures are often constrained by the computational and communication costs of nonbonded force evaluation. We present RBMD 2.0, a major new release of the random batch molecular dynamics package designed for cross-node multi-GPU simulations of large-scale systems. It combines the improved random batch Ewald method with three-dimensional domain decomposition and ghost-particle communication to accelerate multi-GPU nonbonded force evaluation, while the DTK CUDA framework facilitates portability across heterogeneous accelerator architectures. Numerical experiments on multiple benchmark systems demonstrate both the accuracy and efficiency of simulations with RBMD 2.0. For simulations involving up to hundreds of millions of particles across multiple accelerator devices, one achieves speedups ranging from severalfold to approximately two orders of magnitude in nonbonded force evaluation while exhibiting over $97.5\%$ weak-scaling behavior. These results demonstrate the promising nature of RBMD 2.0 as a computational engine for future exascale molecular dynamics simulations.

Dipolar liquids combine two coupled degrees of freedom, translational and dipolar. Intriguingly, these two sectors can reside in distinct thermodynamic states, with the state of one sector possibly favoring a specific state of the other. The onset of ferroelectric order in dipolar liquids is an example of this mutual stabilization. Since this coupling is often encoded in interaction potentials, projecting out one sector to derive an effective interaction for the other can uncover the microscopic mechanism underlying this phenomenon. A coarse-grained theory in which the distance between nearest-neighbor particles depends on their dipolar interaction through a density-dependent mechanical compliance shows that a ferroelectric transition can trigger a liquid-liquid transition. It also predicts a first-order liquid-liquid transition in the paraelectric phase that can in turn induce ferroelectric ordering. The anisotropic dipolar-dependent soft-core interaction is shown to lead to an effective Jagla-like isotropic two-length-scale interaction, known to support liquid-liquid phase transitions. Here, however, the two-length-scale pattern depends on density and dipolar configuration, becoming sensitive to dipolar order. Analysis of supercooled TIP4P/Ice water numerical simulations shows that first-coordination-shell features of pair correlation functions are sensitive to dipolar interaction, supporting the coarse-grained theory foundations. Comparison of low- and high-density liquid water reveals enhanced spatial anisotropy of the first coordination shell in the low-density liquid, with increased effective excluded volume, showing that the liquid-liquid transition involves structural changes within the first shell, beyond reorganization of the interstitial region and tetrahedral order. These findings point to a role for dipolar interactions in supercooled water liquid-liquid phase transition.

We present a real-space formulation of DFT for extended systems in which localized Wannier-like functions are constructed directly from localized Gaussian basis functions without explicitly computing canonical Bloch-like states during the self-consistent cycle. Building on the formalism of Pederson and Lin [Phys. Rev. B 35, 2273 (1987)], a variational set of Wannier-like functions is generated self-consistently within a finite Wannier domain and used to construct the charge density, electrostatic potential, and per cell total energy of the extended system. The occupied space can be determined entirely from the Wannier-like functions. Electronic band structures can be recovered in a post-processing step by solving the full Hamiltonian in a Bloch-like basis constructed from Gaussian orbitals. A key feature of the method is that it can incorporate combined translation–rotation, or screw, symmetries, enabling efficient simulations of chiral and helical systems with finite twist angles at essentially the same computational cost as systems described by pure translational symmetry. The approach is validated through calculations on linear and twisted $\mathrm{{-}C{\equiv}C{-}}$ and $\mathrm{{-}Li{-}F{-}}$ chains, as well as graphene, where total energies and band structures show excellent agreement with reference periodic calculations. To illustrate the ability of the method to treat three-dimensional systems, it is further applied to AA graphite, in which carbon atoms in adjacent graphene layers are aligned directly above one another, as well as helically stacked AA graphite structures. The Wannier-first framework provides a practical route for treating extended systems with nontrivial translational, rotational, and screw symmetries, and provides a natural foundation for the implementation of orbital-dependent functionals such as the Perdew–Zunger self-interaction correction.

We study search processes in a complex environment using the strategy of stochastic resetting. A common occurrence in these environments is targets with finite reactivity. Stochastic resetting has emerged as a powerful mechanism for optimizing search processes by curtailing long, unproductive excursions inherent to diffusive dynamics. Most theoretical studies, however, assume perfectly absorbing targets – an idealization that overlooks the finite reactivity commonly encountered in realistic chemical and biological systems. In this work, we investigate the interplay between stochastic resetting and finite-target reactivity in reaction - diffusion processes. Considering a one-dimensional system with multiple reactive targets, we demonstrated that the target reactivity modifies the optimization landscape. We uncover distinct regimes in which resetting enhances transport efficiency towards specific targets based on their chemical kinetics. As a consequence, the optimal resetting rate becomes intrinsically reactivity dependent. Our results identify target reactivity as a crucial control parameter governing stochastic transport and provide insights into reaction-diffusion processes in complex media.

Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast and largely unlabeled. This challenge is amplified in lithium-ion batteries, where additive performance arises from coupled interfacial reactions rather than a single molecular property. Here, we develop a prototype-guided molecular intelligence, ProtoMI, a literature-driven framework that learns transferable structural priors from reported electrolyte additives and uses them to prioritize candidates in unlabeled chemical space. For boron-containing additives, ProtoMI combines 126 literature-reported molecules with 179,977 unlabeled candidates. Graph contrastive learning identifies seven chemically interpretable prototypes from the reported additives, and prototype guided semi-supervised contrastive learning adapts these prototypes to the candidate space under source-target distribution mismatch. In retrospective temporal validation, ProtoMI achieves enrichment factors of 9.2-45.6 while screening less than 2% of the candidate space. A subsequent translation step identifies four commercially accessible candidates. One representative candidate, 4,4,5,5-Tetramethyl-2-[10-(1naphthyl)anthracen-9-yl]-1,3,2-dioxaborolane (TNDB), improves high-temperature LiFePO4||graphite cycling at 55 {\deg}C by 34.93% relative to the baseline electrolyte. An arsenal of characterizations and operando optical fiber Fourier transform infrared spectroscopy suggest that TNDB forms B-containing, F/P/O-modified inorganic interphases, suppresses solvent decomposition and reduces Fe deposition on graphite. This case study shows how sparse literature knowledge can guide experimentally efficient molecular discovery in data-scarce battery-additive spaces.

Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-lattice simulations. We propose a logarithmic-scale variational quantum eigensolver (VQE) that reduces qubit requirements for N torsional degrees of freedom to O(log2N), enabling off-lattice, all-atom simulations. Our architecture extracts molecular torsions from relative phases in statevector simulations. On quantum hardware, a decoder maps the empirical cumulative distribution function (CDF) from basis-state probabilities to bounded torsional variables. These feed a classical algorithm to build heavy-atom coordinates. We use an EfficientSU2 ansatz and multi-stage relaxation to mitigate barren plateaus. Structures are evaluated via a custom hybrid quantum-classical Hamiltonian, alongside Rosetta and OpenMM benchmarks. Evaluation on chignolin and Trp-cage yielded native-like conformations. Chignolin reached a 0.623 Å C{\alpha} RMSD in retained snapshots and 1.199 Å in final models; Trp-cage achieved a 2.501 Å RMSD among snapshots (3.512 Å in final models). Execution on IBM processors (ibm_cleveland, ibm_miami) successfully recovered native-like structures with a best RMSD of 1.758 Å. The custom energy function performed best overall, though energy-ranking imbalances persisted across sampled landscapes for all functions. This introduces the first all-atom, continuous-space quantum algorithm for QPSP. By converting physical qubit constraints into circuit depth constraints, it proves high-resolution prediction is feasible with exponentially fewer qubits. Despite current limits like computational overhead and energy function sensitivity, it establishes a scalable foundation for hybrid quantum biophysics.

Quaternion algebra provides a natural representation of time-reversal-symmetric matrix structures in relativistic electronic-structure theory, whereas complementary time-reversal-antisymmetric structures extend this representation to complex quaternions, or biquaternions. Here, we explicitly exploit biquaternion algebra for fundamental objects, including operators, kinetically and magnetically balanced basis functions, and their expectation values, within a unified framework encompassing real, complex, and real quaternion subalgebras as special cases. To realize this framework computationally, we have developed HMATLIB, a biquaternion matrix library implemented within the ReSpect package for pure CPU and hybrid CPU/GPU execution. A key development is a bilinear algorithm for matrix-valued biquaternion multiplication that reduces the number of real matrix–matrix multiplications from 64 to 24 compared with the conventional component-wise approach, while the biquaternion representation effectively doubles the maximum accessible matrix dimension under the same 32-bit indexing constraint. Numerical benchmarks on modern CPU and GPU architectures demonstrate that the biquaternion formulation consistently outperforms its isomorphic complex-algebra counterpart. For the largest matrices reported, hybrid CPU/GPU execution accelerates bilinear matrix multiplication by approximately 5-8 times over pure CPU execution and matrix diagonalization by approximately 12 and 61 times relative to CPU oneAPI MKL and NVHPC OpenBLAS, respectively. These results establish biquaternion algebra as a general and computationally advantageous framework for modern relativistic electronic-structure calculations.

Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the loss of geometric and orientational information during coarse-graining. In this work, we present a generalized framework for incorporating molecular geometry and directionality into coarse-grained machine-learned potentials through two complementary approaches: anisotropic density-based descriptors (AniSOAP) and symmetry-adapted equivariant message-passing neural networks (MACE-CG). Using Gay-Berne particles and coarse-grained representations of benzene, formamide, and water, we demonstrate that explicitly retaining molecular anisotropy substantially improves the prediction of energies, forces, and torques relative to isotropic representations. AniSOAP provides an effective linear baseline when molecular shape is well approximated by ellipsoidal symmetry, while symmetry-adapted MACE-CG enables the incorporation of arbitrary molecular point-group symmetries. For water, whose orientational degrees of freedom are poorly represented by ellipsoidal descriptors alone, symmetry-adapted rigid-body features improve energy, force, and torque prediction by resolving orientational degeneracies inherent to isotropic and moment-of-inertia-based representations. These results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.

We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems. By indirectly learning a latent, implicit basis representation of the electronic-state Hamiltonian, the model offers a unified treatment of multiple electronic states, conical intersections, and non-adiabatic couplings. The formalism can be further extended to learn consistent latent representations of additional operators such as transition dipole moments, for example. To demonstrate the general capabilities of our architecture, we train and evaluate networks on two realistic photochemical systems, thymine and azobenzene. The resulting models accurately reproduce energies and oscillator strengths for the ground- and low-lying excited states relevant to the photochemistry of these systems. We highlight the performance of the trained networks by studying critical molecular geometries, including conical intersections and excited state minima. By construction, the proposed framework also recovers the emergence of Berry phase accumulation around conical intersections. By pairing key mathematical structure from quantum chemistry with the representation learning power of transformers, the presented architecture offers a qualitatively new path toward fast and accurate ground- and excited-state simulations.

A new icosahedral superatom UH$_{12}$ is reported and analyzed using post-Hartree-Fock multiconfigurational methods. A RASSCF/CASPT2/RASSI/SINGLE_ANISO workflow is employed to determine the electronic energy levels and magnetic properties. Superatomic characteristics, such as orbital topologies and shell filling order, are discussed. The high hydrogen-to-uranium ratio and potential for optically stimulated decomposition make UH$_{12}$ worth investigation in the context of hydrogen storage. The electric-dipole transition oscillator strengths of the molecule vanish, while its lowest electronic transitions fall in the THz and infrared ranges. The lowest septet manifold exhibits crystal-field splitting into a 1+3+3 pattern, as expected from icosahedral symmetry. In turn, the interlevel gaps within these crystal-field triplets are 30-150 GHz (1-5 cm$^{-1}$). The molecule is magnetically isotropic at low fields, while its levels exhibit higher-order Zeeman anisotropy that becomes significant around 2 Tesla. These properties suggest potential relevance for quantum computing, quantum communication, and quantum memory, including qubit- and qudit-based architectures.

We investigate the refractive index $n$ of LiF using DFT+G$_0$W$_0$+BSE, with ionic-temperature effects included through QMD. We calculate photon-energy dispersions $n(\omega)$ and $k(\omega)$ at ambient pressure and $n(\rho,T)$ at 532 and 1550 nm under shock compression, where $\rho$ and $T$ vary together. Quasiparticle band structures at ambient and compressed conditions and the ambient-pressure orbital-projected density of states connect the optical response to the electronic structure. At ambient pressure, G$_0$W$_0$ yields a quasiparticle gap of 14.25 eV, close to the experimental 14.2 eV, while BSE reproduces the main excitonic feature at 12.5 eV, close to the observed 12.6 eV. Quasiparticle and excitonic effects are thus essential for accurate optical dispersion; the hybrid HSE functional does not reproduce the optical spectra. Along the principal Hugoniot up to 140 GPa, $n(\rho)$ agrees closely with shock data at 1550 nm and acceptably at 532 nm, and agrees better overall than earlier first-principles calculations. Above approximately 110-120 GPa, QMD produces a downturn relative to cold $n(\rho)$ curves, suggesting that ionic dynamics may contribute to deviations from the linear $n$-$\rho$ Gladstone-Dale relation. QMD-sampling and finite-k-grid uncertainties are estimated, and transition-peak broadening is assessed using a recently proposed criterion. The gap increases under pressure, with a transition to a $\Gamma\to\mathrm L$ indirect gap near 50 GPa. An exploratory calculation at approximately 1400 GPa finds a gap of about 24 eV, disfavoring gap-closure metallization. Structural and elastic benchmarks of the underlying models are also provided.

Gausslets are among the few electronic-structure bases that permit the four-index electron–electron interaction to be replaced by an accurate two-index integral diagonal approximation (IDA). Near a many-electron nucleus, however, the nuclear attraction and core-electron Hartree field are individually large and substantially cancel. Treating the first as a full finite-basis matrix while treating the second with IDA leaves an avoidable imbalance. We introduce reference-density Hartree screening: the Hartree field of a chosen reference density is represented accurately, and IDA is applied only to density fluctuations about it. Tests on He, Ne, atomic F, F$_2$, and Cr$_2$ show large reductions in direct Hartree errors, including transfer of fitted neutral-atom fields to molecules. For Cr$_2$, atomic-core screening prevents the spurious HF collapse found with the unscreened $q=5$ and $q=7$ Hamiltonians, whereas finite-reference matching without screening does not. Screening leaves exchange and residual correlation unchanged. We therefore also introduce a low-rank one-particle correction that uses an accurate conventional Gaussian-basis Hartree–Fock calculation to match either occupied-space exchange information or the complete occupied Fock vectors. In F$_2$ and Cr$_2$, $X_{\rm HF}$ reproduces the finite-reference energy and occupied Fock vectors to numerical precision and the selected states return after orbital perturbations. For Cr$_2$, the corrected $q=5$ basis uses one quarter as many functions as the $q=7$ control while retaining sub-mHa mean-field accuracy. Screening provides the physical improvement to the direct field; the state-specific correction then restores the remaining accuracy of the Gaussian-basis mean-field reference.

Complex organic molecules, including large cyclic species, are prevalent in interstellar space and play a key role in various astrochemical processes. Cyclopentadiene (c-C5H6) is a five-membered cyclic hydrocarbon recently detected in TMC-1 and some other interstellar molecular clouds. While accurate spectroscopic data were available, collisional rate coefficients for its rotational transitions were missing so far, introducing a potential limitation in the interpretation of the observations. This study aims to provide a comprehensive set of state-to-state thermal rate coefficients for the rotational excitation of c-C5H6 due to collisions with helium, crucial for non-local thermodynamic equilibrium (non-LTE) radiative transfer modelling in astrophysical environments, and to examine how far the molecule is from thermalisation under the physical conditions of cold molecular clouds. The research employed accurate quantum scattering calculations using the close-coupling (CC) and coupled states (CS) methods, based on a highly-correlated three-dimensional potential energy surface for the [c-C5H6 + He] collisional complex. Calculations were performed for a wide range of rotational states (from j = 0 to j \leq 25) and kinetic temperatures (from 10 to 50 K). We calculated a complete set of thermal rate coefficients for both ortho- and para-C5H6. Radiative transfer simulations demonstrated that most rotational levels of cyclopentadiene are fully thermalized under typical cold cloud conditions and exhibit minor non-LTE effects. Nevertheless, this study is the first to utilise accurate state-to-state rate coefficients for radiative transfer simulation of a large, five-membered cyclic species detected in space. This allows to draw some general conclusions and paves the way for future studies of complex astromolecules that will enable a more precise interpretation of upcoming detections.

Polymer gelation involves the emergence of a system-spanning network from growing polymer-rich domains, yet conventional percolation models typically prescribe particle size independently of material consumption. We formulate gelation as a locally mass-conserving growth-percolation process in which Voronoi capture zones define finite material reservoirs for individual nuclei. Local depletion determines the evolving supersaturation and limiting particle size, while particle contacts generate a dynamic network whose first spanning cluster defines the gel point. Three mechanisms, namely, interface-, diffusion-, and polymer-blob-controlled growth produce distinct gelation kinetics while sharing the same accessible final state. Spatial fluctuations in nucleation further generate a distribution of gelation times. The framework separates gelation from saturation and naturally captures post-gel aging as continued growth and topological maturation.

Density Functional Theory (DFT) is indispensable for materials science and drug discovery, yet the exchange–correlation (XC) evaluation remains a major bottleneck due to its cubic scaling. Although linear-scaling methods exploit electronic nearsightedness to reduce asymptotic complexity, they produce irregular sparse workloads that hide implicit sparsity and prevent efficient use of modern AI accelerators. We present MakoXC, a modular matrix-aligned XC evaluation engine that rearchitects nearsightedness-induced sparsity into regular, accelerator-friendly computations. MakoXC co-designs three key techniques: (1) Matrix-Aligned Cells reorganize nearsightedness-induced interactions into dense, accelerator-aligned data clusters; (2) Sparsity-Guided Activation translates deeper implicit sparsity into numerically correct structured execution for practical linear scaling; and (3) Kernel-Fused Pipeline consolidates fragmented workloads into a unified, compute-intensive execution path that fully unleashes accelerator throughput. Extensive evaluations show that MakoXC achieves average speedups of 67.8$\times$ speedup over standard XC evaluation and 4.7$\times$ over state-of-the-art linear-scaling methods. When integrated into a production-grade commercial DFT package, MakoXC scales XC evaluation to ubiquitin (1,231 atoms, def2-SVP) on 64 GPUs, enabling the end-to-end DFT calculation to complete in under five minutes. By restructuring XC evaluation into a unified, structured computation, MakoXC demonstrates how scientific workloads can achieve genuine low complexity while maximizing parallel efficiency on AI accelerators.

Metal–organic frameworks (MOFs) are promising photocatalysts whose visible-light absorption can be extended through linker functionalization; however, a red-shifted absorption edge does not guarantee enhanced efficiency. Here, we establish a multi-spectroscopic framework to decipher the photophysical fate of photoexcited states in diazo-sensitized UiO-66 using 2D photoluminescence (PL/PLE) mapping and wavelength-resolved continuous-wave X-band photo-EPR. By correlating visible absorption with photo-EPR and PLE action spectra, we distinguish a productive red shift from a non-productive emissive red shift. In highly active UiO-66-Anisole (97% activity relative to \ch{TiO2}), photo-EPR tracks the new absorption band, confirming that excitation populates a charge-transfer pathway yielding persistent, spin-separated states. Conversely, poorly active UiO-66-$\beta$-naphthol (4%) exhibits an extended visible absorption tracked by PLE but not photo-EPR. This reflects excitation trapping in a localized state caused by an ortho-$\mathrm{OH}$ group forming a rigid intramolecular hydrogen bond, which locks the keto-hydrazone tautomer and disrupts the conjugated azo bridge. A new optical overlap descriptor ($S_{\mathrm{Exc}}$) quantitatively captures this trade-off across the series. We demonstrate that photosensitizer design must suppress rigid tautomeric traps and target specific charge-transfer manifolds ($\lambda \le 500$~nm) rather than merely maximizing apparent panchromatic absorption breadth.

Transport in certain systems fails when diffusion cannot replenish or remove material from a boundary rapidly enough, causing the boundary concentration to reach a critical minimum or maximum. Motivated by experiments in electrochemical systems that demonstrate this diffusion-limited failure of charge transport, we determine dynamic current protocols that maximize charge transfer subject to a prescribed concentration constraint. The optimal protocol has a "bang-ride" structure: the maximum feasible current is used until the boundary concentration reaches its critical value, after which the current is progressively reduced to maintain that value. For semi-infinite domains, we derive the optimal currents analytically and obtain system-independent upper bounds on their improvement over constant-current operation. We also extend the framework to finite and multilayer domains and show that the same formulation applies to both charging and discharging.

Machine-learning interatomic potentials (MLIPs) can reproduce potential energies and forces accurately, but their internal energy allocation is often difficult to interpret. E3D-IQA is introduced as a diagnostic framework connecting the latent edge-energy representation of an Allegro-type MLIP with Interacting Quantum Atoms (IQA) energy decomposition. The Allegro edge-energy path is retained as a latent pair contribution, while a node-energy path is trained against the IQA intra-atomic energy. IQA interatomic energies are not direct training targets; instead, the learned edge energies are evaluated after training against the IQA pair terms. Tests on H/C/N/O organic reaction structures show that intra-atomic supervision is essential: energy and force training alone does not recover an IQA-like one-body/two-body allocation. With intra-atomic supervision, node energies reproduce IQA intra-atomic terms, and latent edge energies show meaningful correspondence with IQA interatomic terms. Residual errors are concentrated in positive or weak pair interactions, exposing internal allocation failures that remain hidden in total-energy and force metrics. Adding structures labeled only with energies and forces improves transfer to larger molecules and reduces decomposition errors. E3D-IQA therefore provides a route for diagnosing and guiding latent scientific representations using partially labeled quantum-chemical datasets.

Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the frozen cameras and re-solving cross-view correspondence recovers exactly the answer the images support. We prove the ceiling fails only through an enumerable set of projection coincidences and certify that set empty on 2,160 rendered crystal structures, so every point of a model's deficit belongs to the model. Across fourteen vision-language models, supplying exact geometry as text lifts every model yet closes under half the gap for thirteen, while a supervised vision model with no language component reads the same images at 0.8952, above every vision-language model. The instrument exposes extraction-stage fabrication that downstream accuracy would misattribute to reasoning, yields camera-placement rules for benchmark builders, and transfers to any benchmark with an invertible forward rendering.

The electron self-energy is central to quasiparticle theory, yet how an optical cavity enters it remains unclear. We address this question for a molecule in a single-mode cavity using the dipole-gauge Pauli-Fierz Hamiltonian and a coherent-state QED Hartree-Fock reference. The cavity enters through three channels: the static dipole self-energy (DSE) shift of reference orbital energies, direct DSE augmentation of the screened interaction, and the polariton pole carrying the bilinear electron-photon coupling. We benchmark QED-$GW$ ionization potentials (IPs) and electron affinities (EAs) against a cavity $\Delta$-method ladder from QED-HF to correlated wave-function methods, whose cavity-induced shifts agree within 1 meV where directly comparable. For closed-shell molecules with unbound anions, $GW$ systematically overestimates cavity-induced IP redshifts, whereas EA shifts are reproduced nearly quantitatively, although this does not imply comparable accuracy for absolute EAs. For ionic molecules with bound anions, this ordering reverses, consistent with published QED coupled-cluster results. Coupling and detuning scans show that the error is predominantly quadratic in $\lambda$ and DSE-driven rather than resonant. The spectral function develops a polariton-replica photoemission sideband with weight scaling as $\lambda^2$. In the static screened interaction used in the Bethe-Salpeter equation, bare-photon exchange cancels the matching DSE contribution to the direct interaction, while exchange and polariton-screening corrections remain. Their net effect on the lowest excitation is appreciable only for ammonia in the molecules studied. Exciton-binding energies involving unbound anions are strongly basis-dependent and should therefore be viewed as diagnostics of electron-hole interactions rather than basis-converged molecular quantities.