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Sunday 9 August

Chemical Physics · Cross-listed from quant-ph

What solvable models reveal about Born-Oppenheimer, Born-Huang, and exact factorization

Stephen Wiggins

The Born-Oppenheimer (BO) and single-surface Born-Huang (BH) approximations replace the full molecular problem by nuclear motion associated with one clamped electronic state, whereas the full BH expansion and exact factorization (EF) are exact representations. We use two analytically transparent models to identify what controls one-surface error. For Fernández's bilinearly coupled oscillators, the Brattsev ground-state bracket was already known; we derive closed-form differences proving strictness for every positive nuclear mass and nonzero stable coupling. Using Hunter's conditional-amplitude construction, we place the closed-form ground-state EF potential alongside BO and single-surface BH. Exact excited-state calculations show that the bracket does not extend through the spectrum. The diagonal BH correction is the quantum-metric coefficient of the retained electronic state divided by twice the nuclear mass. Fernández's sixth-order expansion shows that the next omitted correction contains the same derivative couplings, an additional inverse electronic-energy separation, and a factor depending on the nuclear state. We therefore add a two-state model with a position-dependent gap. Decreasing its minimum separation makes the metric higher and narrower while leaving the total Fubini-Study length equal to pi/2. The exact EF, BO, and BH potentials can differ visibly even when their nuclear ground-state amplitudes are nearly identical. The two models show that one-surface accuracy depends on nuclear mass, electronic-state variation, energy separation from omitted states, and the nuclear wavefunction.
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2608.08668 · 9 Aug · Revised and expanded version; title changed from "Born-Oppenheimer, Born-Huang, and exact factorization: quantum geometry and error in analytically transparent benchmark models." Historical attribution and terminology have been clarified, and the exact and numerical comparisons and error-budget discussion have been strengthened. Main conclusions unchanged. 37 pages, 5 figures, 5 tables

Abstract PDF $QNTM ×1

Chemical Physics

Bayesian Localization and Uncertainty Quantification of Trace Species in Two-Dimensional SIMS Imaging

Mengchi Wang, Binsheu Shieh, Ichiro Akai et al. (+4 more)

To enable accurate localization of trace species on material surfaces, we propose a Bayesian framework for analyzing two-dimensional (2D) secondary ion mass spectrometry (SIMS) imaging data. SIMS is widely used in semiconductor manufacturing, materials science, geology, environmental science, and life sciences because of its high sensitivity and excellent elemental and isotopic specificity. However, precise localization remains challenging because of primary ion beam broadening, overlap between neighboring ion distributions, and limited ion counts. The underlying distribution of trace species is modeled as a superposition of two-dimensional Gaussian peaks. To account for the stochastic nature of low-count measurements, the detected ion counts are assumed to follow a Poisson likelihood within a Bayesian framework. Posterior distributions of the peak parameters are estimated using replica-exchange Monte Carlo (REMC), enabling stable inference together with quantitative uncertainty estimation under low-count conditions. The proposed method is first validated using synthetic datasets with known ground truth and is then applied to SIMS measurements of semiconductor samples containing regularly arranged gold (Au) dots with diameters ranging from 0.4 to 2.0~$\mu$m, using scanning electron microscopy (SEM) images as the reference. Optimization of the measurement conditions reduced the relative localization error for 0.4~$\mu$m dots from 5.1$\%$ to 1.9$\%$. These results demonstrate accurate submicrometer localization with statistically rigorous uncertainty quantification in 2D SIMS imaging.
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All authors Mengchi Wang, Binsheu Shieh, Ichiro Akai, Masahiro Hara, Masao Yoshioka, Takeshi Hashishin, Toru Aonishi

Chemical Physics

Canonical-Ensemble Response from Spin-Adapted Time-Dependent Density Functional Theory

Xiaoyu Zhang

Low-lying electronic states of different spins can be thermally populated in open-shell molecules, allowing optical absorption to begin from several initial states. We formulate their canonical optical response using the electronic levels and one-body matrices of spin-adapted time-dependent density functional theory. Boltzmann populations determine the contribution of each initial state, while transition density matrices determine its coupling to the final states. Absorption and stimulated emission then give the temperature-dependent strength of every allowed transition. Transition matrices are evaluated from the calculated first-order excitation amplitudes, with the additional mixed second-order electronic response omitted by this approximation identified explicitly. The resulting response function provides spin-resolved polarizabilities and absorption spectra with definite total spin assigned to each participating electronic state. Applications to diradicals provide a qualitative interpretation of their temperature-dependent absorption spectra.
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Saturday 8 August

Chemical Physics

Chemical potentials from structure factors: I. Neutral multi-component mixtures

Roya Savoj, Xiaoyu Wang, Musahid Ahmed et al. (+1 more)

The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.
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All authors Roya Savoj, Xiaoyu Wang, Musahid Ahmed, Bingqing Cheng

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

An Interfacial Balance Rule Governs Binder-Electrolyte Coupling in Lead-Free Perovskite Energy Storage

Arun Kumar, Ayush Kumar Pandey, Ankur Yadav et al. (+4 more)

Electrode binders are conventionally regarded as inert structural components. Here, we show that in lead-free perovskite supercapacitors, the binder defines the optimal electrolyte composition. Across a factorial matrix of poly(vinylidene fluoride) (PVDF) loadings and LiTFSI concentrations in CsSnCl$_3$ electrodes, the capacitance optimum shifts systematically with binder content along a single linear relationship, described by the Interfacial Balance Rule ($\lambda+\theta=1$), where $\lambda$ and $\theta$ are the normalized lithium-supply and polymer contributions at the optimized interfacial state. The same relationship holds for hybrid MASnCl$_3$, showing that the optimum is governed by the polymer-electrolyte interface rather than the perovskite lattice chemistry. Simulations using a pre-trained MACE machine-learned interatomic potential show that PVDF adopts a planar configuration on CsSnCl$_3$ and simultaneously interacts with cationic and anionic sites. This configuration homogenizes lithium adsorption energetics, introduces fluorine-mediated coordination, and confines lithium to a two-dimensional interfacial region while preserving lateral mobility. Tuning polymer coverage through surface density and chain length reveals a finite interfacial lithium accommodation capacity that marks the onset of out-of-plane aggregation. The Interfacial Balance Rule provides a macroscopic descriptor of this finite interfacial resource, balancing polymer-mediated lithium stabilization against limited accommodation space. Binder loading is therefore an active design parameter for polymer-regulated energy-storage interfaces.
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All authors Arun Kumar, Ayush Kumar Pandey, Ankur Yadav, Vishnu Saraswat, Shiladitya Sengupta, Abhishek Tewari, Monojit Bag

Chemical Physics

A comparative study of sum-connectivity and product-connectivity Gourava indices for benzenoid hydrocarbons

Nagesh H.M, Vijaya Chandra Kumar U, Azghar Pasha B et al. (+1 more)

This study evaluates the sum-connectivity ($SGO$) and product-connectivity ($PGO$) Gourava indices as molecular descriptors for benzenoid hydrocarbons. Using a dataset of 30 benzenoid structures, we compare least-squares regression models for predicting $\pi$-electronic energies ($E_{\pi}$) and find that $SGO$ yields a markedly better fit than $PGO$ across molecular edge types. The indices are further assessed using three validation designs: (i) correlation analysis, in which $SGO$ exhibits strong yet non-perfect inverse correlations with standard descriptors ($M_1, M_2, SO, DSO,$ and $ABS$; $r\in[-0.9923,-0.8936]$), suggesting complementary structural information; (ii) degeneracy analysis on Octane, Nonane, and order-$10$ tree datasets, where $SGO$ attains low degeneracy rates (22.22%, 40.00%, and 42.45%); and (iii) structure-sensitivity analysis on trees of order $n=10$, showing 74% higher sensitivity than $DSO$ while maintaining a high structure-abruptness ratio ($SA = 0.474386$). Overall, $SGO$ offers a favorable balance between discriminative power and numerical stability, supporting its applicability in QSPR modeling and related theoretical studies.
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All authors Nagesh H.M, Vijaya Chandra Kumar U, Azghar Pasha B, Narahari N

Chemical Physics

Oxygen Reduction Reaction on Platinum Nanocatalysts Produces Long-Lived, Hysteretic Oxygenated Adsorbates

Jaehyeon Kim, Lalith Krishna Samanth Bonagiri, Fujia Zhao et al. (+1 more)

Aqueous electrocatalysis generates oxygenated intermediates at catalyst surfaces. While intermediate species on single-crystal catalysts have been observed, the nature and evolution of surface oxygenated species on industrially relevant nanoparticle (NP) catalysts remain largely unknown. Here, using in situ Raman spectroscopy, we tracked the formation and potential-dependent evolution of oxygenated adsorbates in alkaline media on NP catalysts with an active platinum (Pt) surface. By comparing spectroscopic features in Ar- vs O2-saturated electrolytes, we determined three key intermediates produced by the oxygen reduction reaction (ORR): adsorbed OOH, OH, and O2. In contrast to the conventional wisdom that intermediates exist only during catalytic reactions, we found these oxygenated adsorbates to be highly long-lived and hysteretic, and to persist even after the termination of ORR. This adsorbate-retention effect exhibits a modest dependence on the surface oxidation state and the electrolyte cations (K+ vs Li+), and is likely facilitated by the heterogeneous nature of the catalyst surface. The results highlight the complexity of surface adsorption structures on realistic catalysts, which often extends beyond that captured by measurements or simulations on model single-crystal surfaces.
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All authors Jaehyeon Kim, Lalith Krishna Samanth Bonagiri, Fujia Zhao, Yingjie Zhang

Friday 7 August

Chemical Physics · Cross-listed from physics.optics

Active Control of Extreme Ultraviolet Photon Flux and Resolution with a Plane Ruled Reflection Grating Spectrometer that Measures Diverging High-Harmonics

Caleb H. DeWitt, Nnamdi C. Okey, Griffin W. Hancock et al. (+1 more)

High harmonic generation is a versatile tabletop source for the investigation of ultrafast dynamics with atomic fidelity and few-femtosecond time resolution. However, intensity modulations in the high harmonic spectra due to an alternating peak-and-valley structure in femtosecond extreme ultraviolet (XUV) sources can lead to significant distortions in measured XUV absorption spectra. We report a broadband extreme ultraviolet (XUV) spectrometer with a plane ruled reflection grating that can be spectrally adjusted on the fly using a motorized iris aperture in the entrance slit to control the divergence of the XUV beam prior to its dispersive detection. The effective divergence is tuned between 0.03 to 1.9 milli-radian with concomitant variation in the detected XUV photon counts of 1.0 $\times$ 10$^6$ photons/s/eV to 1.5 $\times$ 10$^7$ near 40 eV. Metallic samples of Fe (10 nm) and Ti (10 nm) deposited on Si$_3$N$_4$ membranes (100 nm) are measured under these conditions, with a spectral coverage of > 30 eV and a resolution of up to 500 meV. A volcano plot between signal-to-noise ratio (SNR) and spectral resolution determines the trade-off point in measuring elemental near-edge absorption spectra of thin film samples. Consistent with ray tracing simulations, we show that this design parameter offers on-demand high resolution or high SNR configurations for the rapid acquisition of XUV absorption spectra of thin film solid-state samples, especially strongly correlated materials.
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All authors Caleb H. DeWitt, Nnamdi C. Okey, Griffin W. Hancock, Aditi Bhattacherjee

Chemical Physics

The origin of carotenoid triplets in purple photosynthetic bacteria

Juan J. Romero, Andrew Gall, Viola D'mello et al. (+4 more)

Photosynthetic antenna proteins harvest light energy while at the same time protecting the organism against photodamage. Carotenoid molecules are essential in the latter process, efficiently quenching unwanted (bacterio)chlorophyll excited states created after photon absorption. (Bacterio)chlorophyll triplets, formed by inter-system crossing, are particularly significant, since in the absence of carotenoid quenching, they sensitise the highly oxidative singlet oxygen. In light-harvesting complex 2 (LH2) from Rhodoblastus acidophilus, the pathways that populate carotenoid dark and triplet states remain controversial, involving bacteriochlorophyll-to-carotenoid triplet-triplet transfer and/or generation of triplets by the carotenoid molecules themselves through singlet fission. Transient absorption has been central to understanding photoprotection in these organisms, but spectral congestion limits the separation of the overlapping species needed to discriminate between these pathways. By applying femtosecond stimulated resonance Raman spectroscopy (FSRRS) in different resonance conditions to this protein, in combination with an extension of global analysis to four dimensions (wavenumber, time, intensity and resonance condition), we separate each component of the carotenoid dark-state manifold together with its kinetics. An entangled triplet pair S*/1(TT) is observed, which lives about 60 ps, some eight times longer than in solution. However, this stabilisation does not open a pathway to separated triplets or to carotenoid-BChl a heterofission. Triplet-triplet transfer from bacteriochlorophyll a to carotenoid is also resolved under BChl a excitation, and fits cleanly as a single 2100 ps component. The carotenoid triplet in LH2 is thus produced by photoprotective triplet-triplet transfer from BChl a, and not by singlet fission.
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All authors Juan J. Romero, Andrew Gall, Viola D'mello, Cristian Ilioaia, Andrew A. Pascal, Bruno Robert, Manuel J. Llansola-Portoles

Chemical Physics

Beyond the Four-Decade DIIS Default:Auxiliary-Curvature Acceleration of Self-Consistent-Field Calculations

Peng Bao and Qiang Shi

Direct inversion in the iterative subspace (DIIS), introduced in 1980, remains the practical default for accelerating Hartree-Fock and Kohn-Sham self-consistent-field (SCF) calculations. Many later methods improve robustness or iteration count, but the near-zero overhead of DIIS makes a broad reduction in total wall time difficult. We introduce AURORA-SCF (auxiliary-curvature unified Riemannian orbital-response acceleration), which evaluates energy and gradient only with the requested target Hamiltonian while obtaining most orbital curvature from an independent valence-STO-3G model. A target-level, transported L-BFGS history corrects the model mismatch; a shifted matrix-free solve, trust region, and geodesic orbital update control the step. In 16 direct CPU RHF pairs spanning 137-1484 atomic orbitals, AURORA-SCF was faster in every case, reducing mean wall time by 26.2% and target J/K builds by 33.3%. In a separate set of 63 converged, energy-matched density-fitted GPU pairs spanning seven molecules, closed- and open-shell formalisms, and HF, PBE, B3LYP, and M06-2X, it was again faster in every included pair, with mean reductions of 26.5% in wall time and 31.9% in target J/K builds. Focused direct CPU and GPU sweeps show mean wall-time reductions of 30-34%. These results establish a specific advance beyond the four-decade DIIS default: transferred, secant-corrected curvature can reduce total SCF wall time, not merely iteration count, without changing the target stationary equations. The same optimization pattern also suggests a route to faster orbital optimization elsewhere in quantum chemistry and to multifidelity optimization across scientific computing.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Homojunction-induced thermopower enhancement in polymer films

Zhen Xu, Hui Li, Guangzheng Zuo et al. (+7 more)

It has been more than twenty years since conductive polymers began to receive attention as an emerging thermoelectric material. However, the trade-off between electrical conductivity ({\sigma}) and thermopower (S) has proven to be a major challenge that has obstructed their use in actual devices. Here we report the discovery that the thermopower of the p- and n-type legs of organic thermogenerators can be substantially enhanced, without significant deterioration of {\sigma}, by constructing an in-plane segmented structure consisting of a homojunction with different doping levels on either side. In such segmented layers, the S is abnormally higher than the average value of the constituent parts when applying a forward temperature gradient (heating the heavily doped counterpart), while it is lower upon a reverse temperature gradient. Typically, for a two-stage segmented film of p-type PDPP-Se, an abnormally large S of 210 uV K-1 and {\sigma} of 2.5*10^4 S m-1 are obtained, resulting in a large power factor (PF) of 1100 uW m-1 K-2 and a record ZT of 1.36 at room temperature. The enhanced thermopower is attributed to an additional voltage developed at the homojunction under heating as explained by kinetic Monte Carlo simulations. This finding provides a breakthrough approach to the modulation of thermoelectric transport properties of conductive polymers.
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All authors Zhen Xu, Hui Li, Guangzheng Zuo, Xiaojuan Dai, Jincheng Liao, Guofeng Cheng, Jian Song, Wenqing Zhang, Martijn Kemerink, Lidong Chen

Chemical Physics · Cross-listed from cs.LG

How Molecular Generative Models Organize Molecular Identity

Raul Ortega-Ochoa, Tejs Vegge, Jens S. Bakander et al. (+3 more)

Generative models for matter are often evaluated as samplers over output representations, and their latent spaces are commonly used as proxies for navigating chemical space. Much less is known about how these models internally arrange discrete chemical identities within those representations. We study this arrangement by making molecular identity explicit and pulling it back through the generative process. Through these pullbacks we probe the regions that generate the same object, exposing the trained model's internal repertoire: a fixed partition that determines which objects (novel or not) the model can produce. Across three molecular generative architectures, we find that this repertoire is arranged into piecewise-constant regions separated by recurring coarse-to-fine boundaries. Its organization depends on the representation probed, the identity convention, decoder stochasticity, and the metric used to compare coordinates. During training, local chemical organization stabilizes while the number of distinct molecular identities represented within each neighborhood continues to change. Internal organization must therefore be characterized, rather than assumed, before a generative space can be treated as chemically navigable.
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All authors Raul Ortega-Ochoa, Tejs Vegge, Jens S. Bakander, Luis Mantilla Calderon, Alan Aspuru-Guzik, Tonio Buonassisi

Chemical Physics

High-temperature simulation of the Raman spectra of the isotopologues $^{13}$C$^{16}$O$_2$ and $^{16}$O$^{13}$C$^{18}$O

Eduardo Suárez, Christian Ostertag-Henning, Miguel Carvajal et al. (+1 more)

Recently, experimental Raman spectra of the isotopologues $^{13}$C$^{16}$O$_2$ and $^{16}$O$^{13}$C$^{18}$O at high temperature have been reported. Separately, a reliable approach for obtaining vibrational wave functions for most isotopologues has been proposed. These descriptions, based on the $SU_1(2)\times U(3)\times SU_2(2)$ dynamical group, provide spectroscopic-quality fits to vibrational term values with root-mean-square deviations of $0.06$ and $0.07~\mathrm{cm}^{-1}$, respectively. Using the resulting wave functions, we simulate the Raman spectra of both species by evaluating transition moments of the mean polarizability, represented as an expansion in curvilinear coordinates up to cubic terms within the same algebraic framework. The difference of two independent experimental Raman spectra under comparable temperature conditions is analyzed with the help of the simulations provided by our model. The resulting simulations show excellent agreement with experiment, reinforcing the reliability of the model, while the estimated transition moments are also consistent with experimental values.
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All authors Eduardo Suárez, Christian Ostertag-Henning, Miguel Carvajal, Renato Lemus

Chemical Physics

3D Molecular Representation Learning for Organic Mixtures: Viscosity and Density Prediction

Haicheng Qu, Yanyi Su, Ning Wang et al. (+4 more)

The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of possible species and combinations. Here we introduce a mixture-aware 3D molecular representation learning strategy, built upon a pre-trained molecular encoder, that jointly encodes component structures, mole fractions, and temperature to achieve accurate predictions for organic mixtures. Fine-tuning on publicly available datasets covering a wide range of binary organic mixtures yields test-set R2 values of 0.973 for dynamic viscosity and 0.996 for density, significantly outperforming traditional machine learning baselines. Beyond this overall accuracy, the model captures non-monotonic viscosity changes upon mixing, surpassing simple linear or logarithmic mixing rules. The architecture is extendable to ternary and multicomponent mixtures, as verified via preliminary experiments. Using this model, we quantitatively analyze how molecular structure-branching, cycloalkane, and aromatic rings-affects viscosity-temperature behavior, which benefits the design of lubricants with superior viscosity-temperature performance. Altogether, this work provides a practical, data-driven tool for mixture property prediction, accelerating the rational formulation of functional fluids in chemical engineering.
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All authors Haicheng Qu, Yanyi Su, Ning Wang, Shangqian Chen, Zhifeng Gao, Jun Cheng, Qi Ou

Chemical Physics

Physics-Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Yuhang Wang, Qian Wang, Seong-Hoon Jang et al. (+1 more)

Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and inconsistency with physical laws. Since materials behavior is fundamentally governed by thermodynamics, kinetics, electronic structure, transport processes, and operating environments, the next generation of materials intelligence must move beyond correlation-based prediction toward physics-grounded reasoning. In this Perspective, we systematically discuss Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure. Using representative examples from catalysis, solid-state electrolytes in solid-state battery, and hydrogen-storage materials, we illustrate how physical principles guide data representation, model reasoning, validation workflows, and knowledge management. We further present how AI agents can leverage these physics-aware components to perform mechanism-guided discovery within physically feasible search spaces. Finally, we outline a developmental roadmap from physics-aware AI to physics-reasoning AI and ultimately physics-autonomous AI. Looking forward, materials intelligence should evolve from predictive models toward autonomous scientific systems capable of integrating physical reasoning, multiscale simulations, experimental validation, and continuous knowledge updating for reliable materials discovery.
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All authors Yuhang Wang, Qian Wang, Seong-Hoon Jang, Hao Li

Chemical Physics

A robust and efficient solver for coupled cluster equations

Chanaka D. M. Mudiyanselage, Kangbo Li and Fabian M. Faulstich

The coupled-cluster (CC) equations are most frequently solved via fixed-point (FP) iterations. However, when formulated in a non-canonical gauge, as in local correlation CC, the FP iteration may converge slowly or even diverge. Practical fixes, such as level-shifting and a direct inversion of iterative subspace (DIIS), often improve the convergence, but remain fundamentally heuristic and gauge dependent. Yang et al.~demonstrated that preconditioned Newton–Krylov (PNK) methods provide substantial wall-time advantage for canonical CC. In this work, we generalize the preconditioner to arbitrary gauges by replacing the energy denominator with a gauge-invariant formulation. Combined with Krylov-based approximate Jacobian inversion, the resulting framework removes the need for level-shifting and yields robust and efficient convergence across various gauges and challenging chemical systems. Our numerical results indicate that PNK consistently outperforms carefully optimized FP-based approaches across a range of molecular systems, positioning the proposed PNK method as a promising new standard for solving the CC equations.
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Thursday 6 August

Chemical Physics

Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG

Rubén Darío Guerrero

Nonadiabatic dynamics needs an excited-state gradient and an interstate nonadiabatic coupling matrix element (NACME) at every nuclear geometry, and a double-hybrid functional's accuracy has been unavailable for the coupling. We report the first analytic derivative NACME for a double-hybrid excited state—deferred in the original hh-TDA method and supplied for hybrids only by Yu et al.—derived, with the hole-hole and particle-particle Tamm–Dancoff (\hhTDA/\ppTDA) gradients and NACMEs, as a single reverse-mode transpose of one contraction graph closed under a non-symmetric atomic-orbital-direct $J/K$ kernel. Its double-hybrid excitation energy lowers the vertical-excitation mean absolute deviation from bare-\hhTDA\ $0.86$ to $0.47$~eV and removes the $+0.53\!\rightarrow\!+0.05$~eV over-excitation bias, improving seven of ten states while over-correcting the ionic $\pi\pi^*$ states—the expected perturbative-doubles failure, reported not trimmed. Every coupling is validated to $\sim\!10^{-4}$ against an independent literal many-electron wavefunction-overlap oracle that shares no code path with the method, and is physically meaningful at the ammonia $n\!\rightarrow\!\sigma^*$ covalent conical intersection, where the \hhTDA/\ppTDA manifolds recover the $F\!-\!2$ seam and adiabatic linear-response TDDFT gives $\tau\!\equiv\!0$ by construction. Gradients, NACMEs, and the double-hybrid coupling all run device-resident and AO-direct through one shared Cholesky-decomposed $J/K$ engine within the 8\,GB of a consumer RTX~4060 (a profile-guided $\sim\!10^2\times$ launch collapse preserving double-precision bit-identity)—placing on a commodity desktop card a correlated excited-state derivative capability that has until now required datacenter hardware.
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Chemical Physics

Interference between multiple photoionization pathways in chiral molecules: Converging continuum results in Gaussian bases

Muhammad Sakhi, Alexander Blech, Corbin Allison et al. (+2 more)

An accurate description of photoionization observables is a central challenge for theoretical models of molecular photoionization. Within standard electronic-structure approaches, the continuum states are represented by unoccupied Hartree-Fock orbitals expanded in Gaussian basis sets. Since these basis sets are optimized for bound states, computed observables may exhibit a noticeable basis-set dependence. Here, we augment these basis sets with diffuse functions and investigate the convergence of photoelectron circular dichroism (PECD), anisotropy parameters, and the forward backward ionization time delay in the multiphoton ionization of randomly oriented chiral molecules. All observables converge systematically with the number of added diffuse functions, resolving previously observed basis-set discrepancies and indicating that the augmented basis sets provide a more accurate representation of the intermediate continuum states. In particular, our fully ab initio calculations yield forward-backward time delays in qualitative agreement with recent measurements, for which previous theoretical descriptions relied on empirical modeling. Diffuse augmentation thus provides a computationally efficient and transferable route to converged Gaussian-basis calculations for molecular multiphoton photoionization.
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All authors Muhammad Sakhi, Alexander Blech, Corbin Allison, Christiane P. Koch, Loren Greenman

Chemical Physics

Nuclear-Spin Statistical Weights from Young Diagrams

Eric Kubischta and Ian Teixeira

Nuclear-spin statistical weights and selection rules in molecular spectroscopy are governed by the permutation symmetry of identical nuclei, although rigid and semi-rigid molecules typically realize only a proper subgroup of the full symmetric group. Building on the Schur-Weyl framework of Schmiedt, Jensen, and Schlemmer, we extend this approach to molecular geometries whose rovibrational motion realizes cyclic and dihedral permutation subgroups. By combining the Schur-Weyl decomposition of the $n$-spin Hilbert space with the Kraśkiewicz-Weyman-Adin-Roichman major-index branching rule for the restriction $S_N \downarrow C_m$, we obtain a fully combinatorial method for determining nuclear-spin symmetry species and statistical weights. Each standard Young tableau corresponds to a definite cyclic character determined by its major index, with reflections yielding the associated dihedral symmetry, so that nuclear-spin species follow directly from tableau data without projection operators or case-specific constructions. Applications to $XeOF_4$, $SF_4$, benzene, deuterated benzene, and the tropylium cation demonstrate that the method applies uniformly to realistic molecular point groups and reproduces established statistical weights while providing a transparent spin-resolved structure. The approach supplies the symmetry information required for rovibrational line-intensity modeling and provides a practical, automatable framework for nuclear-spin symmetry analysis in polyatomic molecules.
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Chemical Physics

A Phase Space Electronic Structure View of The Solid State

Nadine Bradbury, Linqing Peng and Joseph Subotnik

We develop a phase space electronic structure view of the solid state, where electronic bands are parameterized by both nuclear position $\mathbf{Q}$ and nuclear momentum $\mathbf{\Pi}$ (rather than the standard practice of only $\mathbf{Q}$). Within this phase space, non-Born-Oppenheimer electronic structure context, we prove a nuclear wavevector ($\mathbf{q}$)-dependent version of Nafie's equality relating the change in electronic momentum as a function of nuclear momentum, $\partial \langle\mathbf{p}\rangle/\partial \mathbf{\Pi}_q$, to the change in electronic position as a function of nuclear position, $\partial \langle\mathbf{r}\rangle/\partial \mathbf{Q}_q$. Furthermore, we show how information about electron-phonon interactions, specifically nuclear-induced electronic momentum, can be extracted from simple band structure calculations – without Berry curvature calculations.
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Chemical Physics

Rotational spectra and de-perturbation analysis for ground state ytterbium oxide, YbO

Richard J. Mawhorter, Sean Jackson, Charles Brainin et al. (+4 more)

A combination difference analysis of Fourier transform microwave spectroscopy (FTMW) measurements of $^{174}$YbO combined with earlier near-infrared chemiluminescence data have resulted in much improved YbO rotational and centrifugal distortion constants. These have been confirmed in a multi-isotopologue analysis of further FTMW measurements of $^{172}$YbO and $^{176}$YbO and an analysis of nuclear-size-dependent Born-Oppenheimer breakdown effects in the molecule. Multiple excited electronic states overlap with low-lying excited vibrational levels of the electronic ground state leading to irregularities in its vibrational spacings and other previously observed electronic manifolds. This has been modeled with a matrix of the vibrational levels of the ground and multiple interacting states assuming Morse potentials and including electronic Hamiltonian matrix elements mixing the states. Vibrational level overlaps were calculated numerically assuming Morse vibrational wavefunctions. The resulting de-perturbed potentials for the states inform continuing experimental and theoretical work on this and other ytterbium-containing molecules.
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All authors Richard J. Mawhorter, Sean Jackson, Charles Brainin, Trevor J. Sears, Peter F. Bernath, Philipp Buschmann, Jens-Uwe Grabow

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

Computing Shear Viscosities from Molecular Dynamics Simulation: Comparing the OrthoBoXY Approach with the Green-Kubo Method

Marcel Brandt, Ralf Ludwig and Dietmar Paschek

We calculated shear viscosities of 15 neat molecular liquids from equilibrium molecular dynamics (MD) simulations using the OrthoBoXY approach and compare them to viscosities calculated via the Green-Kubo method. Data from both methods agree very well. Here, we show how to avoid pitfalls while computing the OrthoBoXY-data to obtain optimal results. From simulations of multiple system sizes, we could verify that the viscosity of molecular liquids is not influenced by finite size effects down to systems as small as 250 molecules. Moreover, we demonstrate that also the standard error of the viscosity is nearly independent of the system size. This is shown to be a consequence of a compensation effect of an increasing accuracy of the self-diffusion coefficients with increasing systems-size and the system-size dependent weighting according to the OrthoBoXY-equation. As a consequence, we suggest that it is preferable to run simulations of smaller systems with longer simulation times rather than larger systems with shorter simulation runs. In addition, we discuss a refinement of the recently introduced "recipe" for OrthoBoXY simulations block-lengths $\tau_\mathrm{block}$. Based on data from simulations with varying run-lengths, we suggest the following modification: for highly viscous systems, the value of $\tau_\mathrm{block}$ might safely be scaled by a factor of $1/8$, significantly reducing the computational resources needed. For less viscous systems, the value of $\tau_\mathrm{block}$ might safely be scaled by a factor of $1/4$. For systems with high fluidity, the value of $\tau_\mathrm{block}$ should not be scaled down in order to achieve reliable results. When using a smaller system size of 250 molecules, these refinements are leading up to a 24-fold reduction in computational cost compared to the previous recommended set-up without sacrificing numerical accuracy.
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Chemical Physics · Cross-listed from cs.HC

Temporal Tracking of Reeb-Space Sheets

Mohit Sharma, Petar Hristov, Talha Bin Masood et al. (+1 more)

Time-varying bivariate fields arise in many scientific applications, where the relationship between two scalar quantities evolves over time. While topological methods such as merge trees provide an effective framework for identifying and tracking features in univariate data, analogous approaches for bivariate fields remain comparatively underexplored. Reeb spaces extend topological analysis to multivariate data by representing fiber connectivity through a collection of interconnected sheets, making these sheets natural candidates for describing bivariate structures. However, establishing temporal correspondences between sheets is challenging due to the structural complexity of Reeb spaces, sensitivity to noise, and the difficulty of defining meaningful similarity measures across timesteps. We present a framework for tracking Reeb space sheets in time-varying bivariate fields. The method establishes correspondences between sheets in consecutive timesteps using complementary similarity measures defined in the spatial domain and the range space. We evaluate the method on a synthetic torus dataset and two time-varying molecular electronic structure datasets. The results show that Reeb space sheet tracking reveals persistent structures and highlights interesting intervals of temporal change. Overall, the results demonstrate that Reeb space sheets can serve as trackable topological structures and provide a foundation for the visual analysis of time-varying bivariate data.
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All authors Mohit Sharma, Petar Hristov, Talha Bin Masood, Ingrid Hotz

Chemical Physics · Cross-listed from math.OC

A Damped Subspace Splitting Algorithm for Constrained Density Functional Theory

Yuanming Su, Yukuan Hu, Xin Liu et al. (+1 more)

Constrained density functional theory (CDFT) provides a powerful framework for describing electronically excited and charge-localized states, which underlie a broad range of physical and chemical phenomena. However, the discretized optimization problems arising from CDFT calculations remain challenging, owing to the presence of both the Stiefel manifold constraint and additional nonconvex quadratic constraints. Existing algorithms either fail to enforce the quadratic constraints with high accuracy or face convergence issues due to double-loop iterative structures. In this paper, we first derive a subspace-splitting reformulation that decouples the two groups of constraints, by exploiting the inherent rotation invariance and introducing a nonlinear subspace alignment constraint. Based on this reformulation, we propose a single-loop damped alternating direction method of multipliers, called DASSP. To the best of our knowledge, DASSP is the first algorithm for CDFT calculations with rigorous convergence guarantees. Each iteration of DASSP comprises a spectral minimization step, a projected gradient step, and a damped dual ascent step, all of which admit efficient implementations. Numerical results on synthetic and realistic CDFT problems demonstrate that DASSP attains high feasibility accuracy and exhibits favorable efficiency without compromising robustness. We expect that this work will pave the way toward reliable and efficient large-scale CDFT applications.
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All authors Yuanming Su, Yukuan Hu, Xin Liu, Guanghui Hu

Chemical Physics

X-ray Driven Trihydrogen Formation on Silica Nanosurfaces

Samuel Sahel-Schackis, Adam Summers, Ritika Dagar et al. (+29 more)

The trihydrogen cation ($\mathrm{H_3^+}$) initiates the ion-molecule reactions that build molecular complexity in interstellar space. Whether its canonical formation reaction, $\mathrm{H_2^+ + H_2 \rightarrow H_3^+ + H}$, proceeds on inorganic surfaces under radiation-driven ionization has remained untested. Here we drive $\mathrm{H_3^+}$ formation on hydrated silica nanoparticles using intense 1.88 keV X-ray pulses, combining ion velocity map imaging, electron time-of-flight spectroscopy, and single-particle coherent diffractive imaging to resolve this chemistry on individual particles. The self-induced surface electric field on the V/nm scale drives interfacial charge transfer and water fragmentation. This field is the dominant parameter governing the relative yields of $\mathrm{H^+}$, $\mathrm{H_2^+}$, and $\mathrm{H_3^+}$ across particle size, composition, and aggregation. Density functional theory and nonadiabatic quantum molecular dynamics simulations trace this field-driven charge transfer, directly analogous to band bending at semiconductor photoelectrodes. These results establish surface-field-driven charge transfer as a unifying mechanism between radiation dominated astrophysical environments and field-driven surface catalysis.
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All authors Samuel Sahel-Schackis, Adam Summers, Ritika Dagar, Alexandra Feinberg, Martin Grassl, Simon Dold, Rebecca Boll, Yevheniy Ovcharenko, Chris Aikens, Cesar Costa Vera, Alberto De Fanis, Avijit Duley, Felix Gerke, Daniel Jost, Regina Leiner, Michael Meyer, Ilana J. P. Molesky, Razib Obaid, Jeffrey Powell, Nils Rennhack, Björn Senfftleben, Hendrik Tackenberg, Paul Tuemmler, Sergey Usenko, Christian Peltz, Thomas Fennel, Markus Gallei, Eckart Rühl, Artem Rudenko, Daniel Rolles, Thomas Linker, Matthias F. Kling

Wednesday 5 August

Chemical Physics

Ground-State Energy Estimation of HeH$^{+}$, ArH$^{+}$, and H$_2$O via Sample-Based Quantum Diagonalization

Jubin Park, Chae-Hyun Yoon, Minkyu Lee et al. (+1 more)

Accurate ground-state energies are essential for understanding molecular structure, chemical bonding, and reaction energetics in quantum chemistry. In this work, we investigate the ground-state properties of the molecular systems HeH$^+$, ArH$^+$, and H$_2$O using Sample-Based Quantum Diagonalization (SQD), a hybrid quantum-classical framework designed for near-term quantum devices. Unlike variational approaches such as VQE, which require deep parameterized circuits and repeated expectation-value measurements, SQD reconstructs a low-energy determinant subspace directly from measured bitstrings. For the present calculations, bitstrings were generated on IBM quantum hardware using shallow local unitary cluster Jastrow (LUCJ) circuits whose parameters were constructed from the $t_1$ and $t_2$ amplitudes of coupled-cluster singles and doubles (CCSD) calculations based on restricted Hartree–Fock (RHF) references. From these samples, we compute ground-state potential-energy curves of HeH$^+$, ArH$^+$, and H$_2$O with the 6-31G and cc-pVDZ basis sets. For all three systems, the SQD results obtained with the cc-pVDZ basis closely follow the corresponding same-basis CCSD energies and reproduce the equilibrium-region trends of the potential-energy curves. HeH$^+$ and ArH$^+$ were chosen as simple yet astrophysically important molecular-ion benchmarks, while H$_2$O was included as a representative polyatomic molecule to assess the applicability of SQD beyond diatomic ionic systems. At the adopted equilibrium geometries, the deviations from the same-active-space CASCI references are 0.00, 2.51, and 6.34 mHa for HeH$^+$, ArH$^+$, and H$_2$O, respectively. These results demonstrate the feasibility of hardware-assisted SQD for the present benchmark systems and motivate further studies of its accuracy and computational scaling for larger molecular active spaces.
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All authors Jubin Park, Chae-Hyun Yoon, Minkyu Lee, Myung-Ki Cheoun

Chemical Physics

Competing Energetics Govern Gas Permeation in Polymer of Intrinsic Microporosity (PIM) Membranes

Jianhao Qian, Ruoyu Wang and Menachem Elimelech

Polymer membranes, particularly polymers of intrinsic microporosity (PIMs), hold great promise for gas separation applications. However, the long-dominant solution-diffusion model, which treats the membrane as a nonporous homogeneous medium, does not resolve how gas-solid atomic interactions govern molecular transport in intrinsic micropores, limiting rational bottom-up membrane design. In this work, we employ non-equilibrium molecular dynamics simulations to investigate the permeation of various gases (He, H2, CH4, N2, O2, and CO2) through PIM-1 as a representative PIM membrane across a range of temperatures. By analyzing the scaling of gas permeability with molecular mass, we identify a temperature-induced transition in the dominant transport mechanism. We demonstrate that this transition is governed by the competition between gas-wall interaction potential energy and thermal kinetic energy: weak interactions or elevated temperatures facilitate Knudsen-type ballistic transport, whereas strong interactions and lower temperatures favor adsorption-mediated surface diffusion. Furthermore, molecular trajectory analysis at the membrane interface reveals two distinct entry pathways: direct entry through pore openings and surface-diffusion-assisted entry. The surface-diffusion-assisted pathway greatly promotes the entry of strongly interacting gases into the membrane, contributing to higher overall permeability, albeit this enhancement diminishes with increasing temperature. These findings offer a mechanistic picture of gas permeation in PIM-1 and explain the dependence of gas permeation on both gas type and temperature. More broadly, they highlight the importance of adopting a pore-flow perspective to understand gas transport in microporous polymer membranes.
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Chemical Physics

Next Generation of Ultra-Coarse-Graining: Self-Consistent Inference of Critical Internal States

Weizhi Xue, Xiao Shuai and Gregory A. Voth

Bottom-up coarse-graining expands the length and time scales accessible to molecular dynamics (MD) simulations, but information loss can hinder accurate representation of multistate phenomena in complex biomolecular dynamics. Ultra-Coarse-Graining (UCG) projects discrete "quantum-like" extended degrees of freedom, or "internal states," onto coarse-grained (CG) molecules, extending CG model expressiveness. The rapid-local-equilibrium (RLE) approximation in UCG depends on user-defined collective variables (CVs, e.g., local density) and neglects correlations between internal states within and between CG molecules. We present Self-Consistent UCG (SC-UCG), which uses the underlying UCG interactions directly to assign internal states without designing CVs in the CG ensemble. During simulation, internal state probabilities are determined self-consistently through graph message passing. We enhance the RLE Hamiltonian with the Bethe approximation and AI-based inference to represent explicit correlations between UCG beads. For force-field training, we develop Multilayer Internal State Consistency (MISC), a machine-learning method derived from relative entropy minimization that avoids iterative sampling of intermediate force fields. We apply SC-UCG to a tetramer exhibiting a second-order symmetry-breaking phase transition from a supercritical racemic fluid to subcritical D-rich and L-rich fluids. SC-UCG captures collective switching of internal states in the subcritical region and recapitulates the phase transition across temperatures, despite being trained on a single-temperature dataset.
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Chemical Physics

Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity

Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich et al. (+1 more)

Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite the known importance of 3D structure to determine chemical and physical properties, the most widely used molecular fingerprints encode only two-dimensional connectivity. Such representations fail to distinguish similar but distinct stereoisomers and conformers. Alternative 3D methods are typically defined pairwise, making their application to large chemical spaces prohibitive, while deep learning embeddings are expressive but uninterpretable and limited by their training data diversity. Here, we introduce novel physics-inspired molecular fingerprints based on principles from spectral graph theory. We represent molecules as a complete graph in 3D space, with edge weights encoding heuristic physical interactions. Eigenvalue decomposition of the resulting graph Laplacian matrix results in a computationally efficient fixed-length chemical fingerprint that encodes 3D structure while obeying necessary physical symmetries of permutation and E(3) invariance. Spectral fingerprints differentiate between unique molecular structures with identical 2D connectivity, overcoming a limitation of 2D descriptors, while maintaining the low computational cost needed for efficient screening of vast chemical spaces. We evaluate our fingerprints with community detection algorithms and observe strong performance against representative baselines across datasets from organic, inorganic, biological, reticular, and reaction chemistry. Nearest-neighbor property estimation and applicability domain analyses reveal the utility of our molecular representation in machine learning and cheminformatics. We anticipate that spectral fingerprints will serve as generalizable, interpretable, and efficient measures of chemical similarity that incorporate 3D information at minimal cost.
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All authors Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich, Heather J. Kulik

Chemical Physics · Cross-listed from quant-ph

Machine learning for sample-based quantum diagonalization: a review of generative configuration recovery and the classical-simulability frontier

Nicolás Bonilla Vargas

Sample-based quantum diagonalization (SQD), equivalently quantum-selected configuration interaction (QSCI), has become a centre of gravity of pre-fault-tolerant quantum chemistry: a processor samples electronic configurations and the Hamiltonian is diagonalized classically in the resulting subspace. Accuracy is governed entirely by which configurations enter it – a machine-learning selection problem, made acute by a coupon-collector bottleneck. We review the generative and learned selectors by what each generates and the signal it exploits, and identify one gap: no reward-proportional generative-flow-network proposer has been built for tail discovery. On the field's central question – whether the quantum sampler beats classical selected CI – the negative verdict is not ours to claim: priority belongs to Reinholdt et al. [JCTC 21, 6811 (2025)], and polynomial-time classical estimation of the flagship circuits has reinforced it. We state that verdict at the precision a falsifiable claim requires – it concerns reproducible, same-active-space comparisons on molecular electronic structure – and weigh the claims outside those qualifiers. We show that alpha-string weights are not invariant under rotations inside degenerate orbital shells, so determinant counts are undefined until the orbital gauge is declared. We distil a ten-element benchmarking standard and apply it to our own deposit, which returned defects that changed numbers printed here and retracted one from v1. FCI-exact experiments confirm one prediction and refute another: the single generative advantage we find keeps no consistent sign along the dissociation coordinate at device-calibrated noise and reverses under a symmetric readout model. It does beat a noise-matched classical recovery loop on N2 by a margin five seeds cannot resolve, and loses by over a factor of two to a classical selector that needs no sampler.
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