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Monday 7 September

Chemical Physics · Cross-listed from quant-ph

Implementing QESEM's High-Accuracy Error Mitigation on a Quantum Computer: a Water Potential Energy Surface Study

Renato Olarte Hernandez, Emanuele Rossi, Sonia Coriani et al. (+5 more)

Quantum error mitigation (QEM) is essential for extracting chemically accurate results from near-term quantum hardware. Many widely used QEM methods rely on uncontrolled heuristics whose bias depends on the specific circuit and noise realization. In this work, we employ QESEM—a characterization-based, unbiased quasi-probabilistic mitigation method—on IBM's Aachen quantum processor to compute the ground-state potential energy surface (PES) of the symmetrically-stretched water molecule. We consider a classically-optimized, single-layer perfect-pairing tiled unitary product state ansatz. We map this ansatz to an 8-qubit register corresponding to a (4,4) active space and the STO-3G basis set. Compared to the statevector reference, we find that raw QPU results typically overestimate the ground-state energy by around 500~mHa across the scanned geometries. On the other hand, QESEM-mitigated results fall approximately within 100~mHa, 30~mHa, or “chemical accuracy” ($\sim$1.5~mHa), depending on the target precision. We benchmark QESEM at both loose (0.1~Ha) and tight (0.01~Ha) precision targets, evaluating both individual and merged batches of runs. As the precision target is tightened, the accuracy improves systematically, matching or exceeding results reported in the literature. We further quantify the sampling cost of these results, reporting the number of shots required at each precision level. Our results show how, with the current levels of hardware error, reaching the highest accuracies demands substantial QPU time. The results demonstrate that the characterization-based, unbiased error mitigation provided by QESEM allows to measure quantitatively meaningful potential energy surfaces on current quantum hardware. Concurrently, they highlight the sampling overhead associated with QEM, which remains a central bottleneck en route to larger chemical problems and higher precision.
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All authors Renato Olarte Hernandez, Emanuele Rossi, Sonia Coriani, Karl Michael Ziems, Erik Kjellgren, Jacob Kongsted, Tali Shnaider, Stephan P. A. Sauer

Chemical Physics

Mixed quantum-classical evolution in open molecular systems

Michael Galperin and Abraham Nitzan

We consider a mixed quantum-classical formulation for open nonequilibrium molecular systems. We employ the pseudoparticle nonequilibrium Green's function (PP-NEGF) method as a fully quantum description and a starting point for introducing classical nuclear dynamics for an open system. We use this formulation to derive a density-matrix equation of motion describing the quantum-classical evolution. We compare our results with previous formulations and highlight important differences related to the nonequilibrium and open character of the molecular system. We also analyze the approximations needed to reduce full quantum-classical dynamics to the fewest-switches surface hopping (FSSH) method and show that these approximations are unreasonable when adiabatic surfaces come close to each other.
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Chemical Physics

First-order Degenerate Symmetry-Adapted Perturbation Theory

Dominik Cieśliński, Tijs Karman, Anthony Scemama et al. (+2 more)

Degenerate electronic states govern the photophysics of excimers and the molecular dynamics near conical intersections. In regions where potential energy surfaces are degenerate or nearly degenerate, intermolecular interactions mix the corresponding electronic configurations. We introduce a first-order degenerate formulation of symmetry-adapted perturbation theory (dSAPT) with weak symmetry forcing that quantifies the mixing in terms of electrostatic and exchange (Pauli repulsion) contributions. We compare two alternative ways of resolving the degeneracy, in which antisymmetry is enforced either after or before diagonalization of the first-order eigenproblem. Using CASSCF monomer wave functions, we apply dSAPT to two model spatially degenerate systems, H$_2$–F(${}^2P$) and H$_2$–NO(${}^2\Pi$), and show that exchange effects contribute significantly to configuration mixing already at intermediate intermolecular separations. For the H$_2$–NO(${}^2\Pi$) complex, we further demonstrate that second-order dispersion energy is essential for qualitatively reproducing the angular dependence of the diabatic mixing angle. Finally, using water and benzene excimers with monomers described either by CASSCF or CIS wave functions, we show that Pauli repulsion controls delocalization of the excitation at short intermolecular separations.
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All authors Dominik Cieśliński, Tijs Karman, Anthony Scemama, Michał Hapka, Piotr S. Żuchowski

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Coupled structural and electronic evolution under pressure in CuIr2Se4, CuRh2S4, and CuRh2Se4

M. Emi, M. Shiomi, K. Kojima et al. (+15 more)

Spinel chalcogenides provide a platform for investigating the interplay among metallic, superconducting, and pressure-induced insulating states. Here, we combine synchrotron powder X-ray diffraction and electrical-resistivity measurements to investigate the pressure evolution of CuIr2Se4, CuRh2S4, and CuRh2Se4 over pressure ranges extending beyond those previously explored. High-pressure diffraction reveals closely related monoclinic supercells in all three compounds. For CuIr2Se4 and CuRh2S4, constrained profile fits based on structural models relaxed using density functional theory are compatible with Phase-IV-type bond-disproportionated structures, whereas the data for CuRh2Se4 establish a compatible monoclinic unit cell without resolving its atomic-scale ordering pattern. Insulating-like transport develops abruptly over a narrow pressure range in CuIr2Se4 but more gradually over broader pressure ranges in the Rh-based compounds, in close correspondence with their respective structural transformations. We also establish previously unreported bulk superconductivity in CuIr2Se4 at ambient pressure: zero resistance is attained at 0.29 K, and the accompanying ac diamagnetic response is consistent with nearly complete superconducting shielding. These results establish a close relationship between the formation of the high-pressure monoclinic phases and the evolution toward insulating transport, and demonstrate that transition-metal and chalcogen substitutions tune the characteristic pressure scales and the competition with superconductivity within a closely related structural framework.
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All authors M. Emi, M. Shiomi, K. Kojima, K. Sugimoto, T. Karasawa, H. Suzuki, M. Takahashi, K. Oka, H. Kadobayashi, S. Kawaguchi-Imada, N. Hirao, T. Ohashi, D. Ito, T. Kubo, M. Matsushita, M. Nohara, K. Matsubayashi, N. Katayama

Chemical Physics · Cross-listed from physics.atm-clus

Core-Level Spectroscopy Decodes Bond-Alternation Dynamics of Cyclo[18]Carbon

Minrui Wei, Zeyu Liu, Sheng-Yu Wang et al. (+4 more)

The advent of X-ray free-electron lasers and high-harmonic generation has made time-resolved X-ray spectroscopy a powerful tool for probing local atomic environments, yet whether localized core excitations can report on global collective distortions remains open. Cyclo[18]carbon (C$_{18}$), with its polyynic ground state (D$_\text{9h}$) and cumulenic transition state (D$_\text{18h}$), provides an ideal model to address this long-standing issue in bond-length alternation (BLA) dynamics. Mapping two-dimensional potential energy surfaces by first-principles simulations, we find that core ionization symmetrizes the ground-state double-well potential along the BLA coordinate. Our calculated X-ray spectra reveal remarkable sensitivity to bond-length variations: C1s ionization potentials vary by up to 2.4~eV across the BLA coordinate (1.1–1.4~Å), with a 0.9~eV variation for minima predicted by different functionals, while NEXAFS $\pi^*$ peaks shift by up to 4~eV across the same coordinate. These predicted signatures provide a quantitative spectroscopy–structure dictionary for decoding transient structures in future ultrafast X-ray experiments and monitoring bond-alternation dynamics in real time.
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All authors Minrui Wei, Zeyu Liu, Sheng-Yu Wang, Jun-Rong Zhang, Lu Zhang, Guoyan Ge, Weijie Hua

Sunday 6 September

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Collective Excitonic Structure Governs Anomalously Weak Thermal Optical Dephasing in Conjugated Polymers

Henry J. Kantrow, Elizabeth Gutiérrez-Meza, Eric R. Bittner et al. (+2 more)

Conjugated polymer aggregates exhibit optical decoherence in the presence of strong vibronic coupling and substantial diversity in chemical structure, solid-state organization, and excitonic character. Here, we use coherence-detected and population-detected two-dimensional electronic spectroscopies to examine the temperature dependence of the homogeneous optical linewidth across a series of semiconducting polymers. Despite substantial differences in molecular architecture and solid-state organization, all polymers studied exhibit remarkably weak thermal linewidth scaling over the measured temperature range. Comparison between complementary detection modalities further shows that, while this weak temperature dependence is obust, the absolute homogeneous linewidth depends on the measured observable. This behavior reflects the different ways in which coherence- and population-detected measurements project population relaxation and pure dephasing onto the spectroscopic response. These results establish the weak thermal scaling of optical decoherence across a diverse series of conjugated polymers and show that its experimental manifestation must be interpreted in the context of the detection observable.
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All authors Henry J. Kantrow, Elizabeth Gutiérrez-Meza, Eric R. Bittner, Hao Li, Carlos Silva-Acuña

Chemical Physics

Out-of-Distribution Inverse Design of Elastic Networks with Differentiable Graph Neural Network Molecular Dynamics

Sergey A. Shteingolts, Salman N. Salman, Ron Levie et al. (+1 more)

Machine-learning-based inverse design can accelerate the discovery of materials with targeted properties, but conventional structure–property models often require large training datasets and generalize poorly beyond their training distribution. Here, we present a differentiable inverse design framework based on a graph neural network molecular dynamics simulator. By combining a short dynamical initialization with physics-based refinement during simulation, the framework enables optimization well beyond the conditions represented in the training data. Using disordered elastic networks, we show that a simulator trained only on non-auxetic systems with Poisson's ratios between 0.1 and 0.4 can design strongly auxetic networks with values as low as -0.3. The framework also produces stress–strain responses outside the range observed during training, creates localized mechanical defects absent from the training data, and generalizes across system size, enabling optimization of networks tested up to 5000 nodes despite being trained only on networks with fewer than 200 nodes.
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All authors Sergey A. Shteingolts, Salman N. Salman, Ron Levie, Dan Mendels

Chemical Physics

Tensor-based Approximation of Molecular Kinetics: Generator Learning, Reaction Coordinates and Incremental Updating

Minakshi Verma, Peter Benner and Feliks Nüske

We present an approach to analyze long-timescale kinetics of molecular dynamics simulations - meta-stable states and transition timescales - by a tensor-based approximation of the infinitesimal generator. We start from the variational approximation of low-lying generator eigenvalues, and discretize the associated eigenvalue problem using a tensor-product basis. We derive analytical expressions for the resulting tensor operators in tensor train (TT) format, and provide efficient algorithms to solve the corresponding linear problems. We also treat the case of a state-dependent diffusion field, which is relevant when working in reaction coordinates. We show how the number of reaction coordinates can be gradually increased without having to solve the variational problem from scratch. Using MD simulations of fast-folding proteins and TICA coordinates as reaction coordinates, we demonstrate the effectiveness of the proposed method at identifying long-timescale transitions and meta-stable states.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Barrierless Water Dissociation on Rare-Earth Sesquioxide Surfaces from First Principles

Shuxiang Zhou, Jay A. LaVerne and Hanna Hlushko

Water dissociation on metal oxide surfaces is a key elementary step in heterogeneous catalysis, photocatalysis, and radiation chemistry, yet its mechanistic details on rare-earth (RE) sesquioxides remain poorly understood. Here, we investigate water dissociation on the (110) surfaces of three cubic bixbyite oxides, Sc$_2$O$_3$, Y$_2$O$_3$, and Lu$_2$O$_3$, using molecular dynamics combining ab initio calculations with on-the-fly machine-learning force field acceleration. By sampling 25 independent trajectories per material, we obtain an unbiased picture of the reaction landscape inaccessible to conventional static calculations. Two distinct dissociation pathways are identified: a conventional proximal mechanism with a small but finite barrier of $\sim$0.1 eV, and a previously unreported distal mechanism that is effectively barrierless and energetically preferred at both the adsorption and dissociation stages. The low barriers are consistent with the periodic array of inherently undercoordinated RE$^{3+}$ sites in the bixbyite lattice, suggesting that ordered intrinsic coordination defects play a role analogous to stochastic oxygen vacancies in conventional oxides.
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Saturday 5 September

Chemical Physics

Performance of a minimally empirical local-hybrid density functional for molecular chemistry

Erin R. Johnson and Kyle R. Bryenton

Delocalisation error has been argued to be the greatest outstanding challenge in density-functional theory (DFT). One of the most promising routes to minimise this error is development of local hybrid functionals, in which the fraction of exact-exchange mixing is position dependent. However, existing local hybrids capable of good thermochemical accuracy are often highly empirical, and tend to have complicated functional forms that involve some combination of range separation, calibration functions, power-series expansions, or even neural networks. In this work, we explore the limits of what can be achieved with a minimally empirical local hybrid functional form that avoids such complexities. The “LHnz” functional is proposed, which uses dispersionless exchange and a local mixing fraction dependent on the correlation length and effective exchange–correlation hole normalisations. With only three empirical parameters, LHnz is shown to outperform all existing global hybrid functionals for the GMTKN55 molecular-thermochemistry benchmark with no large outliers.
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Chemical Physics · Cross-listed from quant-ph

Propagating sparsely supported states with time-dependent neural quantum states

Lexin Ding and Markus Reiher

Neural quantum states (NQSs) have emerged as a powerful ansatz for quantum dynamics. Their high entanglement capacity promises to overcome the entanglement barrier. However, existing time-dependent NQS methods are ill-equipped to propagate sparsely supported initial states due to the inherent mismatch between the supports of such states and their time derivatives. To address this issue, we introduce the interpolation sampling method as a novel form of importance sampling, where samples are drawn from a distribution that interpolates between the wave function and its time derivative. Combined with a global-in-time variational principle, we demonstrate with the example of the two-dimensional transverse-field Ising model that interpolation sampling allows for accurate propagation of extremely peaked states that standard wave function sampling could not tackle. We further improve sampling efficiency by introducing (i) a configuration-time joint sampling scheme where spin configurations and time are both treated as random variables, and (ii) a hybrid strategy for sample proposal that incorporates the knowledge of a Krylov subspace. Our work extends the scope of time-dependent NQS methods to a wider range of physically relevant scenarios, while challenging the prevailing reliance on the Born distribution and its close variants as the default basis for sampling.
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Friday 4 September

Chemical Physics

cuSOAP: a GPU-accelerated Generator of Smooth Overlap of Atomic Positions Descriptor

Hongyu Yan, Yuqing Xia, Yong Wei et al. (+2 more)

The Smooth Overlap of Atomic Positions (SOAP) descriptor is one of the most widely adopted representations of atomic environments in molecular machine learning, but the cost of evaluating it and its derivatives remains a principal bottleneck of SOAP-based interatomic potentials, particularly at the large radial and angular basis sizes demanded by complex, multi-species condensed-phase environments. We present cuSOAP, a GPU-accelerated generator of atom-wise SOAP vectors and their analytic derivatives. Built on PyTorch, cuSOAP evaluates closed-form expressions or quadratures for the projection coefficients and their Cartesian gradients for Gaussian-type-orbital and polynomial radial bases, through fused CUDA and Triton kernels that eliminate the multi-gigabyte intermediates of a naive tensor formulation. The package is a drop-in replacement for the CPU-based reference DScribe, reproducing its constructor signature, feature ordering, and output to within ${\sim}10^{-6}$, and accepts structures directly as Atomistic Simulation Environment (ASE) Atoms objects. On an NVIDIA Grace–Blackwell (GB200) node, a single Blackwell GPU generates the full descriptor-plus-Jacobian workload for a 1000-molecule water cluster up to two orders of magnitude faster than DScribe, i.e., from $13\times$ to $227\times$ across the entire $(n_{\max}, l_{\max})$ hyperparameter map, with the speedup growing with the angular band limit $l_{\max}$. Descriptor-only generation scales as $t \propto n^{1.34}$, close to linear, up to a million-atom water cluster, which is processed in 19.5s on one GPU and, through a shared-memory multiprocessing driver, in 5.7s on the four GPUs of the node, demonstrating $\sim$90% parallel efficiency with no sign of saturation as devices are added. These results bring on-the-fly SOAP evaluation for large-scale condensed-phase simulation within reach.
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All authors Hongyu Yan, Yuqing Xia, Yong Wei, Minghan Chen, Hanning Chen

Chemical Physics

Differentiable Solvation Shell Model for Rational Electrolyte Design

Hancheng Zhao, Hongyi Lin, Celia Kelly et al. (+1 more)

Tailoring Li+ solvation structures has emerged as a promising design principle across multiple Li metal battery (LMB) electrolytes, with localized high concentration electrolytes (LHCE) emerging as a leading candidate. However, rational design of these electrolytes remains limited by a poor quantitative understanding of how molecular properties govern Li+ solvation shell composition. Here we introduce a mean field modeling framework, built on an Ising model, that predicts solvation shell composition from donor number (DN), acceptor number (AN), molar ratio, and molecular size. This framework is end-to-end differentiable, which enables parameterization directly from molecular dynamics (MD) solvation structures via gradient-based optimization. Focusing on LHCE, the model achieves 10.7% RMSE and R2=0.87 for shell composition, 2.3% RMSE on free solvent ratio, and further reproduces solvation trends in real LHCE electrolytes, at a fraction of MD's cost. Our model also shows good generalizability on other electrolyte systems in addition to LHCE. Analysis of its interaction terms shows that DN dominates Li+ solvation energetics, providing a thermodynamic basis for empirical DN-based design rules. We further demonstrate the model's design utility on the LiTFSI/tetraglyme (G4) system absent from training. The model identifies fluorobenzene (FB) as a promising diluent and predicts a salt-concentration window with anion-rich solvation shells and low free solvent, favorable for stable anion-derived SEIs and high oxidative stability, respectively. This prediction is validated by higher-fidelity MD. This work establishes an interpretable differentiable framework for predicting Li+ solvation shell composition, with potential to extend to other electrolyte classes.
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All authors Hancheng Zhao, Hongyi Lin, Celia Kelly, Venkatasubramanian Viswanathan

Chemical Physics · Cross-listed from cond-mat.stat-mech

Diffusion under competing bulk and surface stopping mechanisms

Yilin Ye

We investigate reflected diffusion in a bounded domain subject to two independent, competing stopping mechanisms: an exponentially distributed bulk lifetime of rate $p$ and a surface reaction triggered when the boundary local time exceeds an independent exponential threshold of rate $q$. Denoting by $T$ the stopping time and by $L$ the acquired boundary local time at stopping, we derive their marginal distributions, joint Laplace transform, and complete hierarchy of mixed moments. These statistics are determined by the splitting probability $\phi$ that surface reaction occurs before bulk decay. In particular, we establish the identity $p\expect{T}+q\expect{L}=1$ and show that the cumulative risk $pT+qL$ is exponentially distributed with unit rate. We further obtain equivalent representations of $\phi$ in terms of the Robin-Laplacian and the generalized Steklov spectra. Explicit results for a three-dimensional ball reveal how competing rates $p,q$ control $\phi$ and the $(T,L)$ statistics. Monte Carlo simulations test the universal cumulative-risk law.
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Chemical Physics · Cross-listed from cs.LG

Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials

Bumju Kwak and Jeonghee Jo

While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and transition state search, rely heavily on the PES Hessian. Yet standard MLIPs are trained on energy and forces only, and using the Hessian as an additional training objective requires backpropagation whose memory cost grows quadratically with the number of atoms. To address these limitations, we propose two Hessian-derived data augmentation schemes: isotropic Gaussian displacement (UniAug) and normal mode-weighted displacement (ModeAug). Both methods utilize simple Taylor expansions, generating physically grounded molecular structures with highly accurate energy and force labels. By preserving the underlying MLIP architecture and training procedure, this approach contributes to improving energy and force predictions in various benchmarks. Comprehensive evaluations across non-equilibrium and equilibrium datasets demonstrate that our approach enhances model accuracy where reference forces are large and training data are limited.
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Chemical Physics

Polarizable Embedding for Photoelectron Spectroscopy and Photoelectron Circular Dichroism in Solution: Core-Level Ionization of Aqueous Alanine

Giovanni Nottoli and Piero Decleva. Chiara Cappelli

We present a polarizable quantum-mechanics/molecular-mechanics formulation for the calculation of molecular photoionization observables in solution. The approach couples the static-exchange density-functional-theory treatment of bound and continuum electronic states with a fully atomistic polarizable embedding described with the fluctuating-charge force-field. The fluctuating charges are determined self-consistently with the ground-state quantum-mechanical density and are subsequently included in the static-exchange Hamiltonian to account for the outgoing electron. The numerical behavior of the coupled scheme is validated and applied to the X-ray photoelectron spectrum and photoelectron circular dichroism of zwitterionic L-alanine in aqueous solution, using an ensemble of configurations extracted from molecular dynamics. Calculated spectra reproduce experimental profiles remarkably well, demonstrating that atomistic solvent structure and environmental polarization must be treated together to reliably model photoelectron observables in aqueous solution.
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Chemical Physics

Measurements of Diffusion Coefficients of CO$_{2}$ in 1-Butanol with a New Laminar Jet Apparatus and PFG-NMR, Pointing to Interfacial Mass Transfer Effects

Oliver Großmann, Sarah Mross, Jens Wagner et al. (+3 more)

A novel laminar jet apparatus (LJA) was constructed for precise gas-liquid mass transfer measurements up to 12 bar, significantly extending the technique's operating window. It was applied to carbon dioxide + 1-butanol between 283 K and 333 K. Corresponding measurements were performed using pulsed field gradient NMR spectroscopy (PFG-NMR), which does not involve interfacial mass transfer. Fick diffusion coefficients from LJA and self-diffusion coefficients from PFG-NMR were compared in the limit of infinite dilution, where both must coincide. Both methods show consistent trends, but LJA data are systematically lower. Experimental errors cannot explain the deviations. We therefore hypothesize an additional gas-liquid interfacial mass transfer resistance. PCP-SAFT combined with density gradient theory predicts high CO$_{2}$ enrichment at the interface, and the deviations correlate with this enrichment. Whether such an interfacial resistance exists and is caused by enrichment remains to be established in future studies.
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All authors Oliver Großmann, Sarah Mross, Jens Wagner, Kerstin Munnemann, Fabian Jirasek, Hans Hasse

Chemical Physics

Operando imaging of intercalation memory in MXenes

Franz Groebmeyer, Mohsen Beladi, Christoph G. Gruber et al. (+6 more)

Ion intercalation enables reversible control of charge, structure and function in layered solids. Yet layered materials contain nanosheets with different thicknesses and stacking, whose intercalation pathways are averaged out in ensemble measurements. This becomes critical under Angstrom-scale confinement, where ion transport couples to solvent reorganization and host deformation, leaving the origins of kinetics, reversibility and activation unresolved. Here we combine operando interferometric scattering and interference reflection microscopy to resolve proton-driven dynamics in individual single- and multilayer Ti3C2Tx MXene flakes and show that even a few stacked layers introduce a significant kinetic barrier. Cycling then separates nanosheets into three persistent behaviors: reversible monolayers, restacked multilayers with incomplete recovery and delayed deintercalation, and coherently stacked multilayers that self-stabilize through reversible folding. Operando synchrotron X-ray diffraction shows this memory at electrode scale, where the interlayer structure converges towards a reproducible state. Our findings reveal cycling-induced structural memory that governs subsequent intercalation in layered MXenes.
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All authors Franz Groebmeyer, Mohsen Beladi, Christoph G. Gruber, Ruocun Wang, Pol Salles, Nhu Quynh Nguyen, Jakub Drnec, Yury Gogotsi, Emiliano Cortes

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Nuclear quantum effects in the thermal conductivity of solid hydrogen

Hengtai Zheng, Zezhu Zeng and David E. Manolopoulos

We use a combination of path integral and lattice dynamics methods to calculate the thermal conductivity of solid parahydrogen. Path integral molecular dynamics is first used to calculate a centroid potential of mean force at each temperature, on which a harmonic phonon basis is constructed using the finite displacement method. We then calculate anharmonic force constants on the centroid potential and solve the linearised phonon Boltzmann transport equation for the thermal conductivity. The resulting renormalised phonon dispersion curves, heat capacity, and thermal conductivity are all in remarkably good agreement with experimental measurements. We find that the heat transport is dominated by collective relaxon modes at low temperatures and cannot be captured by making the phonon relaxation time approximation. Indeed, quantitative agreement with the experimental thermal conductivity is only achieved when the nuclear quantum effects in the centroid potential of mean force, phonon boundary scattering, and hydrodynamic transport are all explicitly considered. This is also likely to be the case for other quantum crystals.
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Chemical Physics · Cross-listed from cond-mat.soft

Liquid-liquid phase separation precedes crystallization in supercooled water-glycerol solutions

Iason Andronis, Sharon Berkowicz, Mariia Filianina et al. (+10 more)

Understanding the structural evolution of supercooled water-glycerol solutions is important for cryopreservation, yet distinguishing liquid-state transformations from ice crystallization remains challenging. Here, we investigate a deeply supercooled water-glycerol solution by X-ray photon correlation spectroscopy (XPCS) in ultra-small-angle X-ray scattering (USAXS) geometry, combined with wide-angle X-ray scattering (WAXS). This combination simultaneously captures the structural and dynamical evolution of the supercooled liquid upon quenching to cryogenic temperatures (172 K). We observe discontinuous changes in the liquid structure on molecular length scales and formation of microscale domains. The dynamics slow down during this stage and exhibit hyper-diffusive, ballistic-like relaxation. This transformation precedes ice crystallization, which we identify from the emergence of ice Bragg peaks in WAXS, allowing the two processes to be temporally separated. Phase-field (Cahn-Hilliard) simulations qualitatively reproduce the experimental observations and show that a spinodal-decomposition scenario is consistent with the measured scattering evolution. These findings are consistent with a liquid-liquid phase separation scenario preceding ice crystallization and provide a route to disentangle the two processes in supercooled aqueous systems.
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All authors Iason Andronis, Sharon Berkowicz, Mariia Filianina, Maddalena Bin, Robin Tyburski, Robert Bauer, Yuriy Chushkin, Federico Zontone, Michael Sprung, Wojciech Roseker, Fabian Westermeier, Felix Lehmkühler, Fivos Perakis

Chemical Physics

Derivation of the Sample-Size Scaling of TWO-NN Intrinsic-Dimension Estimates from Molecular Dynamics Trajectories

Riccardo Capelli

The intrinsic dimension of a dataset is the number of independent directions needed to describe the space occupied by its data. Estimators based on nearest neighbors infer this number from how the probability to find a neighbor point grows around each sampled point. Because the distances $r$ between neighbor points decrease as the sample grows, the estimated dimension can depend strongly on the number of available points. Here, we derive the large-sample behavior of the TWO-NN estimator for data drawn from a smooth $d$-dimensional space. The typical nearest-neighbor distance scales as $N^{-1/d}$, and smooth deviations from a locally uniform distribution produce successive corrections proportional to $N^{-2/d}$. We test this result using the trajectories coming from ten independent $100~\mu$s simulations of alanine dipeptide. Configurations are represented by all pairwise distances among the ten heavy atoms. This representation has a known geometric dimension of $3n_{\mathrm{at}}-6=24$. Over the investigated range, the TWO-NN estimate shows no systematic dependence on the temporal spacing between configurations, but increases from approximately $7.5$ to $15.6$ as the sample size grows from $10^2$ to $2\times10^5$. Extrapolations that retain corrections through $r^2$, $r^4$, and $r^6$ give limiting dimensions of $25.23$, $22.89$, and $27.00$, respectively. All three estimates lie close to the known dimension and collectively bracket it, supporting the proposed scaling. Their spread provides a direct estimate of the systematic uncertainty associated with the truncation. The derived scaling therefore explains the strong sample-size dependence of TWO-NN and provides a practical route from finite sample estimates to the underlying geometric dimension.
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Chemical Physics · Cross-listed from physics.atom-ph

Analytical model for polarization transfer during gas-phase collision events in spin-exchange optical pumping: Spin-$\frac{1}{2}$ $^{129}$Xe versus spin-$\frac{3}{2}$ $^{131}$Xe

Perttu Hilla, Rajgowrav Cheenikundil and Juha Vaara

Spin-exchange optical pumping (SEOP) is a method for producing spin-hyperpolarized noble gas nuclei, such as 129Xe and 131Xe, which are used in various magnetic resonance applications from fundamental physics to quantum sensing and medical imaging. In SEOP, optically polarized alkali-metal atoms transfer their spin polarization to the noble gas nuclei in gas-phase collision events via the hyperfine coupling (HFC) between the alkali valence electron and the noble gas nucleus. While the polarization transfer physics of spin $I = 1/2$ nuclei, such as 129Xe, is relatively well understood, that of spin $I > 1/2$ nuclei, such as 131Xe ($I = 3/2$), has been far less studied, and no rigorous theoretical model has been presented to date. To this end, we derive a simple analytical model for the upper limit, neglecting relaxation, of the SEOP polarization transfer, applicable to noble gases with arbitrary nuclear spin. Analytical evaluation of the Baker-Campbell-Hausdorff expansion for the time evolution of the spin density operator $\hat{\rho}(t)$ reveals that only even-order terms in the HFC contribute to the polarization transfer, with the leading-order quadratic term being the most significant. We obtain a result similar to that derived for the spin-exchange cross section by Herman [Phys. Rev. 137, A 1062 (1965)], but in a more general framework for the time evolution of $\hat{\rho}(t)$ that is also more familiar to magnetic resonance researchers. The model is applied to understand the difference in the polarization transfer efficiency between 129Xe and 131Xe, yielding results in agreement with previous experiments. We also validate the model by comparison to detailed numerical multiscale simulations of the SEOP process, where full quantum-chemically computed spin Hamiltonians sampled from molecular dynamics simulations of the gas-phase collision events are used to propagate the spin dynamics.
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Chemical Physics · Cross-listed from physics.plasm-ph

Activated carbon-assisted anionic dye decomposition using a dual-liquid post-double dielectric barrier discharge plasma reactor

Krishna V S, Surya and Mangilal Choudhary

Non-thermal Plasma (NTP) technology, including the Dielectric Barrier Discharge (DBD) reactor, has emerged as an effective advanced oxidation process (AOP) for treating persistent organic pollutants in industrial wastewater. Based on this objective, a dual-liquid dielectric-barrier-discharge plasma reactor was built and used to degrade a ternary anionic dye mixture comprising Reactive Red 120, Congo Red, and Methyl Orange in its post-discharge configuration. The adjustable length of the outer liquid electrode (grounded) provides flexibility in controlling the discharge length and cooling, thereby achieving higher degradation efficiency and greater reactor stability. The conducting-liquid high-voltage electrode (a dielectric tube filled with a conductive solution) helps mitigate metal corrosion and oxidation caused by reactive species in the discharge zone. A detailed study of the degradation of ternary anionic dye mixtures (simulated dye wastewater samples) in the presence of activated carbon (AC) was performed to explore the potential applications of post-discharge treatment. The activated carbon (granules) enhances the degradation rate of ternary anionic dye mixtures in basic solutions rather than in acidic or neutral solutions. A comparative study of the degradation reaction kinetics of the ternary anionic dye mixtures in acidic and basic solutions was conducted after analyzing UV-Vis absorption spectra. The role of other parameters, such as solution pH, the mass and size of AC particles, dye concentrations, and AC reuse, was investigated in relation to degradation efficiency. The degradation mechanism was elucidated by the formation of strong oxidizer (hydroxyl radicals) on the surface of activated carbon during post-discharge treatment. However, in acidic or neutral anionic dye solutions, the degradation mechanism was dominated by ozone.
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Chemical Physics

Voltage-embedded equivariant machine learning potential for open system simulations

Yiming Guan, Rongzhi Gao, ChiYung Yam et al. (+2 more)

Modeling electrochemical interfaces under operational non-equilibrium conditions is vital for energy technologies but remains bottlenecked by the expensive cost of ab initio methods. Current machine learning potentials, largely designed for closed systems under homogeneous electric fields, are limited in open quantum transport applications. To overcome this, we present an E(3)-equivariant graph neural network that embeds voltage bias for open-system simulations. Our approach decouples the system energy and forces into zero-bias and bias-dependent contributions, assigning distinct vector encodings to electrode and scattering-region atoms to capture non-equilibrium conditions. Trained on limited discrete bias data, the model achieves high predictive accuracy and robust extrapolation transferability. When applied to a lithium/water interface, our model successfully captures the field-induced dynamic reorientation of water molecules and reproduces asymmetric electrochemical behavior at different electrodes.
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All authors Yiming Guan, Rongzhi Gao, ChiYung Yam, GuanHua Chen, Ziyang Hu

Thursday 3 September

Chemical Physics · Cross-listed from cs.AI

La Agente Óptima: Towards Agentic Self-Driving Laboratories

Marcel Müller, Jiaru Bai, Willi Gottstein et al. (+14 more)

Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on human specialists who translate scientific objectives into executable closed-loop campaigns. Specialists adjust them as data and operating conditions change. Here, we present La Agente Óptima, an agentic framework that constructs and supervises Bayesian optimization campaigns across computational and experimental systems while maintaining a persistent optimization state. By separating large language model (LLM) reasoning from executed campaigns, Óptima runs repetitive optimization loops consistently, returns control to the agent only when progress requires interpretation or campaign revision, and keeps every decision auditable. We evaluate Óptima across ablation studies, five digital discovery tasks, and two physical platforms. Throughout, Óptima maintained executable campaigns as both the scientific problem and execution environment evolved. In a closed-loop contact angle optimization campaign, Óptima identified and corrected a mid-run measurement failure, bringing the contact angle from 71.4 to 67.8 degrees, just above the 64-66 degree range. From this result, Óptima correctly inferred that the target was likely unattainable with the available reagents and recommended changing the formulation. In a five-day multi-objective flow-chemistry campaign, Óptima increased the yield from 30% to 59% over 23 experiments. Despite substantial inference costs, it cost less and used substantially less starting material than a human-directed campaign, while selecting a more mass-efficient operating point. These results show that LLM-based agents can make rigorous, long-running optimization campaigns accessible to domain scientists without specialist setup, expanding the scope of SDLs.
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All authors Marcel Müller, Jiaru Bai, Willi Gottstein, Abhijoy Mandal, Mohammad Nazeri, Elia Savino, Yanlin Fang, Sujoy Das, Sergio Pablo García Carrillo, Yeonghun Kang, Juan B. Pérez-Sánchez, Simone Pilon, Martin Fitzner, Timothy Noël, Frank Gu, Varinia Bernales, Alán Aspuru-Guzik

Chemical Physics

Learning Memory-Dependent Neural Network Correlation Potentials for Accurate Electron Dynamics

Harish S. Bhat and Christine M. Isborn

Time-dependent density functional theory, though exact in theory, is in practice applied in an adiabatic approximation using exchange-correlation functionals with only local temporal dependence. Simultaneously, the exact correlation potential formally depends on, among other quantities, the time-history of electron densities. Here we develop a framework to learn neural network models of correlation functionals that feature explicit memory-dependence. Our framework includes two different approaches, both linked through their use of adjoint-based optimization. One approach decouples the learning of the functional from inversion to find ground truth values of the correlation potential. The other approach learns the functional directly without requiring inversion. We apply these methods to modeling the electron dynamics of two-electron systems in two spatial dimensions. In both cases, our methods yield correlation functionals with low test set propagation error, outperforming standard local density and generalized gradient approximation functionals by one to two orders of magnitude. Overall, our framework points at one strategy to move beyond the adiabatic approximation and develop memory-dependent correlation functionals that yield accurate propagation for excited state and/or non-equilibrium dynamics.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Triboelectrification of a dense metal-organic framework for resilient mechanical energy harvesters

Chuzhan Jin, Debayan Mondal, Jiahao Ye et al. (+1 more)

Triboelectric nanogenerators (TENGs) incorporating metal-organic frameworks (MOFs) have largely been designed around porous architectures, based on the assumption that high internal surface area is the primary driver for triboelectric enhancement. Herein, we demonstrate that a dense, nominally nonporous MOF called ZIF-zni can instead function as an effective high-loading filler within a tribopositive polyurethane (PU) matrix, offering a design strategy for harnessing interfacial electromechanical effects. A 20 wt% ZIF-zni@PU composite delivers an output voltage of 470+/-15 V and a peak power density of 1.31+/-0.03 W m-2 against polydimethylsiloxane (PDMS). The device exhibits stable performance over ~94,000 cycles under a contact force of ~100 N, and it remains operational under higher impact beyond 500 N. This concept enables the demonstration of a proof-of-concept triboelectric floor tile. Combined experimental and theoretical studies indicate that the performance enhancement arises from favourable interfacial polarization, reduced dielectric screening, and surface accessible ZIF-zni rich domains, rather than porosity alone. These features are accompanied by reduced work of adhesion and modified surface roughness, hence improving contact electrification. These findings establish dense MOFs as an effective triboelectric filler and identify interfacial electronic structure and polarization as key design parameters for engineering mechanical energy harvesters and self-powered sensors.
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All authors Chuzhan Jin, Debayan Mondal, Jiahao Ye, Jin-Chong Tan

Chemical Physics

Learning multistate kinetics with a variational multistate committor network

Chenyu Tang, Cheng Giuseppe Chen, Benoît Roux et al. (+1 more)

The long-time dynamics of complex molecular systems often involves rare transitions across networks of metastable states. Building on transition-path theory, which provides a rigorous framework for describing rare transitions between two metastable states, we introduce the variational multistate committor network (VMCN), a neural framework that learns the probabilities of reaching each metastable state directly from molecular simulation data. From this representation, VMCN identifies state-specific commitment, candidate transition regions and committor-consistent pathways between state pairs, and an effective kinetic network characterized by transition rates. The model is trained using finite time-lag trajectory data together with boundary conditions defined on conservative state cores. Applications to a triple-well potential, trialanine isomerization, and the $c$–ring rotation in the V$_{\rm o}$ domain of a vacuolar ATPase show that VMCN recovers metastable organization, provides committor-consistent descriptions of transition mechanisms, and estimates state-to-state kinetics. VMCN further provides diagnostics for incomplete state decompositions and enables adaptive exploration of candidate metastable states and their connecting regions. By integrating VMCN with generative committor-guided path sampling (Gen-COMPAS) for chignolin, we start from two end point structures, identify a misfolded state and a candidate folding intermediate, and we direct subsequent sampling toward the resulting multistate transition network.
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All authors Chenyu Tang, Cheng Giuseppe Chen, Benoît Roux, Christophe Chipot

Chemical Physics

Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space

Luis Itza Vazquez-Salazar and Tristan Bereau

Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of distinguishable compounds by roughly three orders of magnitude, making high-throughput screening of thermodynamic properties tractable across soft matter, with drug–membrane permeability as a well-developed example. The compression is lossy and, so far, one-way: a screen returns a combination of beads, with no established route back to the compounds it stands for. Recovering those compounds–compositional backmapping–is a one-to-many inverse map, distinct from the better-studied conformational problem of rebuilding atomic coordinates from a known mapping. Here we formulate compositional backmapping as conditional graph generation by introducing juniper, a discrete denoising diffusion model over molecular graphs conditioned on the octanol–water partition free energy $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$, the principal driver of MARTINI bead type assignment and hence a proxy for bead identity. Trained on molecules of up to 9 heavy atoms mapped onto one or two beads, juniper generates molecules that are 93% valid and 92% unique for two-bead targets, and whose $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$ distributions track the target $\Delta G^{\mathrm{CG}}_{\mathrm{W} \mapsto \mathrm{O}}$ linearly ($r^{2} \geq 0.96$), departing only in the hydrophobic and hydrophilic tails. Although the model receives no chemical information beyond a single scalar, the functional groups shift systematically with the imposed free energy, from branched hydrocarbons at the apolar end to amides, imides, and isocyanates at the polar end. A bead combination flagged by a CG screen can therefore be turned into candidate molecules for atomistic study or synthesis.
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Chemical Physics · Cross-listed from cond-mat.soft

Mechanistic Framework for Multicomponent Nanoparticle Assembly: Predicting RNA-lipid and PEI-DNA nanoparticle assembly

Turash Haque Pial and Tine Curk

The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.
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