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Thursday 1 October

Chemical Physics · Cross-listed from physics.comp-ph

Automated Many-Body Simulations of Strongly Correlated Systems Using a Correlation-Aware Agentic Framework

Tenghui Li and Chong Sun

We present CAFES, a correlation-aware agentic framework for electronic-structure simulations of strongly correlated systems. CAFES addresses two challenges: the fragmented software landscape for many-body calculations and the difficulty of selecting appropriate methods across diverse correlation regimes. It combines correlation diagnostics, adaptive method selection, and large language model (LLM) assistance for molecular, crystalline, and model-Hamiltonian systems. A study-task architecture separates study-level planning from task-level execution, while a curated scientific knowledge layer provides reusable guidance for method selection, workflow design, and result interpretation. We demonstrate CAFES through three research-level studies: calculating the low-lying electronic states of lutein using DMRG-CASSCF, probing phase competition in the extended honeycomb Hubbard model using DMET, and generating a quantum-chemical dataset with CCSD labels. These calculations demonstrate the potential of agentic workflows for strongly correlated electronic-structure problems, a regime that has received limited attention in existing agentic computational frameworks.
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Chemical Physics · Cross-listed from physics.comp-ph

From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials

Chenyu Wang, Yangshuai Wang and Lei Zhang

Machine-learned interatomic potentials are commonly fitted by weighted-sum scalarization, which combines energy and force errors in a single loss. A nominal weight, however, identifies a potential only relative to the complete fitting protocol. We therefore treat energy–force balancing as a protocol-dependent problem of physical model selection. For fixed-basis atomic cluster expansion models of molten LiCl, liquid H$_2$O, and Si, the resulting scalarization paths contain dominated states at extreme weights. Their nondominated subsets depend on the solver, and the out-of-distribution Si path is nonmonotone. We replace direct weight selection by minimizing the regularized energy objective subject to an upper bound on the normalized force loss. Projected dual ascent adjusts the Lagrange multiplier from the force-constraint residual. A frozen-multiplier limited-memory quasi-Newton refinement then returns the final model. Across the three systems, the constrained procedure reaches the solver-matched low-error region and gives measured speedups of $6.7$–$8.9$ over completed scalarization scans under the stated timing convention. Physical-property calculations show that first-shell geometry is comparatively insensitive to the selected balance, whereas transport and solid-state observables vary more strongly. These results identify the fitted model–protocol pair as the relevant object of energy–force model selection and support force-constrained fitting as an explicit selection rule.
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Wednesday 30 September

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

Reference-free certification of machine-learning interatomic potentials

Jonas Hänseroth and Christian Dreßler

Universal machine-learning interatomic potentials now reach held-out energy and force errors so small that they no longer predict how a model behaves in simulation. Here we show that a potential can be graded without any reference calculation, against properties the exact Born-Oppenheimer surface satisfies by mathematical or physical necessity. We organise these properties into four families, symmetry and invariance, self-consistency and integrability, statistical-mechanical equilibrium and regularity, and turn them into fourteen inexpensive probes, each certifying against a target that is exact and independent of chemistry and reference method. Every probe therefore returns an absolute, architecture-comparable score from the energies, forces and stresses a potential already exposes, and reveals failures a fixed test set cannot. Applied to 64 pretrained potentials, the suite resolves two orders of magnitude of certified quality at comparable reported accuracy, and flags a fine-tuned potential whose reference errors improve while its surface degrades until molecular dynamics fails. An interactive leaderboard of all 64 models is available at https://jhaens.github.io/pescert-bench.
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Chemical Physics

Electronic Symmetry and Femtosecond Coherence Dynamics of Renner-Teller-Mixed Neutral Vibronic States in Carbon Dioxide

Eric Liu, Shashank Kumar, Siddhant Pandey et al. (+7 more)

When non-adiabatic coupling mixes electronic states of a molecule, vibronic states of different electronic symmetry become interleaved across the same spectral region. The symmetry of these states cannot be accessed in conventional spectroscopy due to orientation averaging. We show that molecular-frame angular signatures, which are preserved even when states with different symmetry overlap in energy, can be used to resolve them. Using vacuum-ultraviolet transient absorption spectroscopy of aligned carbon dioxide molecules, we separate two electronically distinct classes of vibronic states ($A^{'}$ and $A^{''}$) that arise from Renner-Teller splitting of the $^1\Delta_u$ state. We reconstruct molecular-frame anisotropy continuously across the vibronic manifold showing that the anisotropy crosses over from perpendicular to parallel transition character and maps the interleaved electronic symmetry landscape. By scanning the femtosecond delay between the VUV and a near-infrared pulse through temporal overlap, we measure symmetry-dependent coherence dephasing times of these states and find that the parallel ($A^{'}$) states dephase faster than the perpendicular ($A^{''}$) states. We attribute this to faster departure of the $A^{'}$ wavepacket from the Franck-Condon region due to the distinct topology of the $A^{'}$ excited-state surface. These measurements probe symmetry-resolved ultrafast wavepacket dynamics on the coupled excited-state potential energy surfaces.
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All authors Eric Liu, Shashank Kumar, Siddhant Pandey, Tzu-Hsien Tan, Russell Zimmerman, Kousik Bera, Anthony Fiore, Paul Hockett, Varun Makhija, Niranjan Shivaram

Chemical Physics · Cross-listed from quant-ph

Quantum oblique eigenprojection

Alexander M. Dalzell and Yuan Su

Every square matrix decomposes its underlying Hilbert space into generalized, nonorthogonal eigensubspaces. We show that a quantum computer can perform such an oblique eigenprojection $\Pi$ given block encoding access to the input matrix. Our approach has a query complexity nearly linear in the inverse gap and a normalization factor close to $\lVert\Pi\rVert$ under a spectral-set condition on the input. This covers common assumptions on the numerical range or diagonalizability and matches known results for orthogonal eigenprojections. We achieve this with a two-sided block preconditioning that uses a discrete Fourier transform of the matrix resolvent. We describe applications to: (i) preparing eigenstates of matrices with complex eigenvalues, extending the quantum eigenvalue transformation algorithm of Low and Su beyond real spectra; (ii) solving continuous-time algebraic Riccati equations, cubically speeding up a prior solver of Rodenas-Ruiz, Zhao, and Lee; and (iii) solving ordinary Sylvester equations, quadratically improving a direct augmented method of Wang and Liu. Our result suggests a promising route to applying nonanalytic matrix functions on quantum computers.
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Chemical Physics · Cross-listed from physics.bio-ph

Computational Insights into Mechanostability and Dissociation Dynamics of the Dengue Virus Envelope Protein Ectodomain Dimer Across pH and Temperature Gradients

Georcki Ropón-Palacios, Luís G. F. Crespi, Jakub Rydzewski et al. (+2 more)

Dengue virus (DENV) is an enveloped flavivirus of major public health importance. Its envelope (E) protein mediates viral entry through homodimer dissociation and subsequent membrane fusion, making it one of the principal targets for antiviral strategies. To investigate the molecular determinants of this process, we performed steered molecular dynamics (SMD) simulations of the E protein ectodomain (ecE) dimer from DENV-2 and DENV-3 under variable pH and temperature conditions. Force-extension analyses revealed a highly stable interface for both serotypes, with rupture forces exceeding 1000 pN. pH and temperature had modest effects on overall mechanical resistance. However, DENV-3 displayed a distinct sensitivity to thermal stress compared to DENV-2. We identified a robust, asymmetric dissociation pathway across all conditions, characterized by a metastable intermediate state involving partial dimer opening and exposure of the fusion loop (FL). This intermediate exposes an immunodominant epitope and persists under physiological conditions, suggesting it as a viable target for therapeutic intervention. The primary contributions to the interfacial interaction network were found to arise from van der Waals interactions, followed by hydrogen bonds, salt bridges, and pi-cation interactions. DENV-3 exhibited a slightly greater contribution from polar interactions involving domains EDI, EDII, and EDIII. Furthermore, pairwise occupancy analysis identified pH-sensitive contacts that are disrupted under acidic conditions, particularly in DENV-3, providing mechanistic insight into the early stages of ecE dissociation. Together, these findings provide structural insights into the dissociation mechanism, identifying key metastable states and pH-sensitive interactions that could be exploited to develop antivirals that stabilize the dimer and prevent viral fusion.
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All authors Georcki Ropón-Palacios, Luís G. F. Crespi, Jakub Rydzewski, Walter Rocchia, Alexandre S. de Araujo

Chemical Physics · Cross-listed from cond-mat.soft

Fluctuation-induced ionic friction at solid-electrolyte interfaces

Damien Toquer, Baptiste Coquinot, Nikita Kavokine et al. (+1 more)

In this article, we explore how transport at a solid–electrolyte interface is affected by the internal fluctuations of the solid and quantify the role of solid excitations on ionic friction. Combining molecular dynamics simulations with theoretical modeling, we show that the presence of ions enhances the dynamical response of the electrolyte compared to pure water. This leads to an ionic contribution to the fluctuation-induced interfacial friction. We show that collective ionic modes dominate the dissipation, such that ionic friction arises from many-body effects rather than from individual drag forces. Furthermore, high-frequency molecular modes are found to be ion-specific, suggesting possible routes toward spectral separation of ions, with implications for desalination, filtration, and energy conversion.
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All authors Damien Toquer, Baptiste Coquinot, Nikita Kavokine, Lydéric Bocquet

Chemical Physics

Smite: A quasiclassical trajectory (QCT) program for bimolecular collisions and unimolecular dynamics on ab-initio, and machine-learned potential energy surfaces

Péter Szabó, Jenne van Veerdeghem, Jérôme Loreau et al. (+2 more)

We present Smite, a Python toolkit for quasiclassical trajectory (QCT) simulations of reactive and inelastic bimolecular collisions, unimolecular dynamics, and molecular collisions with finite surface models. A central feature is the explicit control of reactant preparation: harmonic normal modes admit fixed quantum numbers, prescribed energies, thermal quantum populations, or ground-state Wigner sampling; rotation is prepared at fixed angular-momentum magnitude or from thermal distributions appropriate to diatomic and polyatomic rotors. Rotating-Morse sampling provides a coupled anharmonic treatment of diatomic vibration and rotation, while saved molecular-dynamics phase points provide an alternative source of initial conditions. The same nuclear propagators operate with on-the-fly electronic-structure calculations or user-supplied analytical and machine-learned potential energy surfaces. Additional modules provide constrained rigid-fragment dynamics, thermostat-based preparation, photoionization initial conditions with energy and recoil constraints, and fewest-switches surface hopping (FSSH) on multiple PESs with approximate curvature-derived couplings. Geometry optimization, transition-state searches, crossing-point optimization, intrinsic-reaction-coordinate following, and thermochemistry use the same energy and derivative interfaces. Analysis tools connect trajectories to product energy partitioning, stereodynamical correlations, time-resolved vibrational signatures, and independent-atom x-ray scattering.
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All authors Péter Szabó, Jenne van Veerdeghem, Jérôme Loreau, Jean-François Müller, Jeremy N. Harvey

Chemical Physics · Cross-listed from physics.atom-ph

Nuclear moments, charge radii, and magnetization distribution parameters of Ag isotopes from laser spectroscopy and ab initio electronic-structure calculations

Leonid V. Skripnikov, Bram van den Borne, Michail Athanasakis-Kaklamanakis et al. (+2 more)

The nuclear electromagnetic moments and mean-square charge radii of several silver (Ag) isotopes deduced from recent laser spectroscopy studies in the mass region $A=$ 96-121 are determined using high-accuracy electronic structure calculations performed in this work. We report hyperfine structure and isotope-shift atomic factors calculated with the relativistic coupled cluster approach, including single, double, triple, and perturbative quadruple excitations, CCSDT(Q). Following a systematic theoretical uncertainty analysis, we show that at the precision now achieved (sub-percent uncertainties in some cases), quantum electrodynamic effects become significant. We also show that the isotope-dependent effect in the hyperfine structure due to the non-point-like nuclear magnetization distribution can be extracted with negligible dependence on the assumed nuclear magnetization model at the present level of precision. This also yields a nuclear magnetic dipole moment that is corrected for the hyperfine anomaly induced by the Bohr-Weisskopf effect. In terms of the nuclear electric quadrupole moments, the uncertainty in the electric-field gradient used to extract the quadrupole moments from laser spectroscopy has also been reduced by one to two orders of magnitude relative to values used in previous studies. Finally, the nuclear charge radii of Ag isotopes are extracted using field- and mass-shift factors from our coupled cluster calculations, and the difference in mean-square charge radii between $^{107,109}$Ag agrees with the value deduced from muonic X-ray spectroscopy.
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All authors Leonid V. Skripnikov, Bram van den Borne, Michail Athanasakis-Kaklamanakis, Gleb Penyazkov, Ruben P. de Groote

Chemical Physics

Smart Machine Learning for Solving the Full Electron-Nuclear Schrödinger Equation with a Quantum-Chemically Inspired Wavefunction Ansatz

Yaolong Zhang, Bin Jiang and Hua Guo

Directly solving the full electron-nuclear Schrödinger equation remains one of the grand challenges in quantum mechanics. Here, we propose a quantum-chemically motivated orbital-based neural network wavefunction ansatz with explicit many-body electron-electron correlation terms, with parameters depending on the entire electron-nuclear configuration, enabling an accurate description of strongly correlated and/or multi-reference electronic structures by a single Slater-determinant across a broad nuclear configuration space. To train this wavefunction, we further develop an efficient hybrid optimization strategy that combines the variational Monte Carlo technique with local-energy constraints, mitigating the intrinsic sampling bias of the former and considerably reducing the statistical error. These advances are embedded in a unified neural network framework, Schrödinger, enabling the determination of globally accurate potential energy surfaces with a single training for systems ranging from strongly correlated diatomic molecules, triatomic reactions containing conical intersections, to polyatomic multi-reference molecules. This work offers a practical machine-learning tool for solving the electron-nuclear Schrödinger equation and capturing full quantum effects beyond the Born-Oppenheimer approximation.
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Chemical Physics · Cross-listed from physics.comp-ph

MyTm: An Automated Melting Temperature Calculation Toolkit

Y. S. Huang, H. X. Song, Y. Sun et al. (+7 more)

Melting temperature calculation is one of the important topics in computational materials science. In high-throughput in silico screening and artificial intelligence assisted design of materials, it usually requires a rapid and autonomous assessment of the melting temperature of the target. Unfortunately, molecular dynamics (MD) simulations of the melting point require many cumbersome and manual operations, making large-scale calculation of the melting point challenging. In this work, we introduce MyTm, a toolkit that employs MD to automatically determine the melting point. The method is fully modularized, and by combining these modules, the program enables fully automated melting calculations by using commonly adopted approaches, including the direct-heating method, the void method, the modified void method, the solid-liquid coexistence method, and the Z method. Moreover, a machine learning (ML) method is proposed and employed to recognize and classify the solid like and liquid like atoms, which effectively resolve the low accuracy issue in conventional classification approaches, thus making the automated high throughput pipeline of melting-point calculation possible. The robustness and efficacy of MyTm have been demonstrated by several well studied systems.
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All authors Y. S. Huang, H. X. Song, Y. Sun, J. L. Li, Y. L. Xu, F. C. Wu, Y. C. Gan, Y. F. Wang, H. Wang, Hua Y. Geng

Chemical Physics · Cross-listed from quant-ph

From Wavefunction Regularity to Eigenvector Conditioning: Accuracy–Cost Trade-offs of Quantum Algorithms for Non-Hermitian Transcorrelated Hamiltonians

Cheng-Lin Hong, Emiel Koridon, Paul K. Faehrmann et al. (+3 more)

The electron–electron Coulomb singularity forces cusp structure in electronic wavefunctions. This cusp structure limits wavefunction regularity and slows the convergence of expansions in smooth bases, requiring large basis sets to reach a target accuracy. The transcorrelated (TC) method incorporates this short-range behavior into the Hamiltonian, thereby improving the regularity of the transformed wavefunction and accelerating convergence toward the complete-basis-set limit. The resulting Hamiltonian is non-Hermitian and non-normal, which limits the direct applicability of standard quantum algorithms. For such non-normal operators, the complexity of several recent quantum algorithms depends explicitly on quantities related to eigenvector conditioning and eigenvalue sensitivity. Understanding these quantities is essential for assessing both algorithmic complexity and quantum resource requirements. In this work, we address these issues in two stages. We first study a solvable model, which provides a controlled setting for isolating how transcorrelation affects basis convergence and eigenvector conditioning. Within this setting, we investigate two distinct one-parameter Jastrow correlators. We derive the asymptotic energy-error scaling: $N^{-1/2}$ for the bare projection and $N^{-3/2}$ for both TC correlators at generic fixed parameters, with $N$denoting the basis size. Furthermore, we show that this faster convergence can yield a polynomial improvement in the asymptotic query-complexity upper bound. We then extend the analysis to second-quantized electronic-structure Hamiltonians and use particle-number-sector diagnostics to distinguish target-sector from auxiliary-sector conditioning. Our work provides a framework for understanding the central accuracy–cost trade-off in TC Hamiltonians.
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All authors Cheng-Lin Hong, Emiel Koridon, Paul K. Faehrmann, Thomas D. Kühne, Stefano Polla, Werner Dobrautz

Chemical Physics

Elasticity of polycrystalline davemaoite constrains its grain size in the lower mantle

Peiyu Zhang, Liang Yuan, Rong Huang et al. (+3 more)

Earth's lower mantle hosts large low shear-velocity provinces (LLSVPs) beneath Africa and the Pacific, but their origin remains debated. Davemaoite, a major lower-mantle mineral, has been proposed as a contributor to these anomalies; however, its experimentally measured shear modulus is approximately 30% lower than first-principles predictions, a longstanding discrepancy between theory and experiment. Here, using million-atom machine-learning molecular dynamics simulations, we show that nanoscale grain-boundary disorder, invisible to conventional x-ray diffraction, can reconcile this gap. Introducing 5.8 vol% disordered regions reduces the shear and bulk moduli by 37% and 12%, respectively. This shear-selective softening can reproduce LLSVP-like seismic anomalies in basalt-rich assemblages, but only at disorder levels corresponding to grain sizes smaller than those expected in the lower mantle. Combining the disorder-elasticity relationship with seismic constraints, we derive a minimum davemaoite grain size of approximately 100 nm. This limit is consistent with mantle grain growth models and with attenuation-based evidence for coarse-grained LLSVPs.
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All authors Peiyu Zhang, Liang Yuan, Rong Huang, Yu Ye, Xiang Wu, Junfeng Zhang

Chemical Physics

Computing electron overlap integrals for Gausslet orbitals on cubic lattices

Xianrui Yin, Fereshteh Ghasempour and Christian B. Mendl

Gausslet orbitals on a cubic lattice, introduced in [Steven R. White, J. Chem. Phys. 147, 244102 (2017)], represent a localized, smooth, and systematically refineable basis set featuring a "diagonal" approximability of the electron repulsion integral tensor. The present work develops efficient algorithms for evaluating overlap integrals required for electronic-structure simulations using these Gausslets, specifically the kinetic, nuclear, and electron repulsion integrals (both with and without the diagonal approximation). Computational efficiency improvements rest on the exploitation of translation, permutation, and octahedral symmetries, a reordering and precomputation of nested sums, as well as an early truncation of small coefficients. Our algorithms reduce the number of electron repulsion integrals on a $5 \times 5 \times 5$ grid from $125^4 = 244140625$ to $138187$ due to symmetries, and achieve a wall-clock runtime for evaluating the remaining integrals with a truncation tolerance of $10^{-5}$ in under 1 second on a laptop computer. We apply the developed methodology to compute the ground state of the hydrogen atom and molecule as a demonstration.
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Chemical Physics

A strategic roadmap for an atomistic machine-learning ecosystem

Jörg Behler, Michele Ceriotti, Cecilia Clementi et al. (+47 more)

Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging from first-principles electronic-structure calculations to molecular dynamics and statistical sampling, into which ML was integrated naturally to reshape long-standing trade-offs between accuracy, efficiency, and scale. Nevertheless, this integration raises both conceptual and practical challenges, from choosing between data-centric and physics-based modeling approaches to adapting established software stacks to modern hardware accelerators and ML libraries. As the field evolves rapidly, fueled in part by widespread enthusiasm but also by tangible impact, it seems appropriate to take a moment to consider the current state of the art and open challenges, and reflect on what can be done to better coordinate efforts across the community. With this goal in mind, several members of this community met in Lausanne in January 2026 at CECAM to discuss algorithms, models, software and hardware infrastructure, and the most promising scientific applications that have become possible thanks to the use of artificial intelligence in atomic-scale simulations. This strategic roadmap paper summarizes the outcomes of these discussions, suggesting some long-term goals, and some concrete actions, to establish a healthy, sustainable and impactful atomistic ML ecosystem.
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All authors Jörg Behler, Michele Ceriotti, Cecilia Clementi, Gábor Csányi, Alin-Marin Elena, Aditi Krishnapriyan, Joseph W. Abbott, Fabio Affinito, Albert P. Bartók, Ilyes Batatia, Filippo Bigi, Florian N. Brünig, Yannick Calvino Alonso, Giuseppe Carleo, Aurélie Champagne, Stefan Chmiela, Marc L. Descoteaux, Ralf Drautz, Alexandra Farcas, Meng Gao, Rohit Goswami, Michael F. Herbst, Christian Holm, James R. Kermode, Alexander L. M. Knoll, Tobias Kreiman, Marcel F. Langer, Hoang-Thien Luu, Yury Lysogorskiy, Mihai-Cosmin Marinica, Rocco Meli, Klaus-Robert Müller, Frank Noé, Mohamadhosein Nosratjoo, Simon Olsson, Christoph Ortner, Aldo S. Pasos-Trejo, Anyang Peng, Eric Qu, Andrea Rizzi, Mariana Rossi, Bassem Sboui, Gregor N. C. Simm, Alexandre Tkatchenko, Jacopo Venturin, O. Anatole von Lilienfeld, William C. Witt, Brandon M. Wood, Tigany Zarrouk, Fabian Zills

Chemical Physics

Autocata: Reaction-network-guided autonomous catalyst screening via capability-centric AI

Ruili Li, Rui Qi, Qingqing Mao et al. (+3 more)

Artificial intelligence (AI) has expanded the accessible space for catalyst discovery, yet executing complex screening campaigns still requires constant human intervention to bridge high-level reaction objectives with underlying computational pipelines. Here, we present Autocata, a knowledge-embedded autonomous agent that scales catalytic expertise by encoding multistep reaction pathways into reusable, language-invocable capabilities. Powered by a Transformer pretrained on two million catalyst-adsorbate structures, the system incorporates a capability registry of 79 adsorbate-specialized generative models spanning complex reaction networks across carbon, nitrogen, oxygen, and hydrogen intermediates. Given a high-level natural-language prompt, Autocata deconstructs reaction networks, queries and configures matching generative models, generates candidate surface configurations, enforces physical and geometric validity filtering, and coordinates machine-learning potentials evaluations to rank promising materials. Crucially, the agent adaptively reconfigures workflows when encountering capability boundaries or task bottlenecks. We demonstrate these capabilities across CH4 activation, nitrogen reduction reaction (N2RR), and CO2-to-methanol pathways. By shifting from static toolkits to an adaptive, language-driven computational infrastructure, Autocata lowers the barrier for expert-level catalyst discovery and offers a scalable paradigm for self-driving catalytic research.
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All authors Ruili Li, Rui Qi, Qingqing Mao, Ritankar Das, Beien Zhu, Yi Gao

Chemical Physics

An atom-based machine-learned dipole-moment model and application to conjugated systems

Yuansheng Zhao, Tamio Yamazaki, Ryohei Hosoya et al. (+2 more)

We introduce a method for predicting the dipole moments of molecular systems by systematically decomposing the total dipole moment into the sum of atomic contributions using the wavefunction from density-functional-theory calculations, and then use graph neutral networks to predict this effective atomic dipole moment from input atomic structure. It is demonstrated that the dipole moments and dielectric function can be accurately predicted even for complicated conjugated systems where the previous bond-based model [Phys. Rev. B 110, 165159] fails. The inference cost of our model scales linearly with the number of atoms and is about 3 times faster compared with Born-effective-charge-based schemes, but shows similar or better accuracy for dielectric function at terahertz range.
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All authors Yuansheng Zhao, Tamio Yamazaki, Ryohei Hosoya, Yu-ichiro Matsushita, Shinji Tsuneyuki

Chemical Physics

A Visual Understanding of Circular Dichroism Spectroscopy: The Case of Interacting Chromophores

Braden M. Weight, Aaron Forde and Sergei Tretiak

Exciton coupling shapes the photophysical and chiroptical properties of molecular aggregates, but a microscopic, real-space understanding of how intermolecular interactions generate circular dichroism (CD) remains incomplete. Here, we computationally investigate the microscopic origins of CD in coupled molecular dimers to separate intermolecular (topological) from intramolecular (intrinsic) chirality. A combination of time-dependent density functional theory and transition chiral tensor (TCT) analysis provides a quantitative, visually intuitive framework showing how electronic coupling strength, intermolecular separation, and relative angular orientation govern chiroptical response. We find that pairs of individually achiral chromophores exhibit robust, topological chiroptical signals which give rise to the conventional Cotton effect, a standard interpretation of derivative features in circular dichroism spectra. We next establish that intrinsically chiral chromophores, on the other hand, disrupt the Cotton effect due to the competition between intrinsic chirality and topological chirality. This work establishes a foundation for interpreting exciton-coupled CD spectra in molecular aggregates, supramolecular assemblies, and nanostructures using the TCT approach, which offers an attractive tool for analyzing chiroptical responses in complex multi-chromophore systems relevant to emerging quantum technologies.
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Chemical Physics

Nuclear quantum effects enhance diffusion in supercooled ammonia through accelerated pyramidal inversion

Minwoo Kim, Hido Woo, Taeyong Park et al. (+2 more)

In liquid ammonia, an imbalance between available hydrogen-bond donor and acceptor sites limits water-like tetrahedral connectivity, leaving sparse and short-lived associations within a densely packed liquid. We ask whether quantum-sensitive local rearrangements can nevertheless contribute appreciably to diffusion in this weakly connected liquid. We compare classical and thermostatted ring polymer molecular dynamics from 170 to 250 K using an r$^2$SCAN-trained machine learning force field. Ammonia can change its molecular geometry as the nitrogen atom passes through the plane of the three hydrogen atoms, a motion known as pyramidal inversion. Nuclear quantum effects increase the inversion rate and reduce its apparent activation energy. Inversion events are accompanied by transient reductions in hydrogen-bond coordination and local density within the first solvation shell. Although nuclear quantum effects lower density and viscosity throughout the studied range, diffusion remains only weakly affected in the warmer liquid. The quantum enhancement of diffusion emerges below approximately 210 K and reaches 18% at 170 K, tracking the enhancement of cage escape. These observations support an inversion-assisted contribution to diffusion, in which a spatially localized, quantum-sensitive internal motion couples to cage relaxation without a persistent tetrahedral network.
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All authors Minwoo Kim, Hido Woo, Taeyong Park, Ji Woong Yu, Won Bo Lee

Tuesday 29 September

Chemical Physics

RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology

Lingli Ge, Yubin Wang, Junyuan Gao et al. (+9 more)

Chemical reaction-condition optimization – choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity – is a central, judgement-laden subtask of organic methodology research that large language models are increasingly expected to support. Yet existing chemistry benchmarks evaluate reaction-class labelling, retrosynthesis, or SMILES manipulation, and do not ask models to read a real condition-screening table and pick the best set. We introduce RxnOptBench, a benchmark whose every option and precedent is a real wet-lab entry mined from the optimization tables of organic-methodology papers published in 2025, graded by a continuous relative score derived from a declared headline utility that combines reported yield with enantiomeric excess (ee), diastereomeric ratio (dr), and regioisomeric ratio (rr), and equipped with a paired precedents-vs-no-precedents design that isolates in-context use of literature evidence from parametric memorization. Across nine frontier LLMs and three Chemistry LLMs, even the best models leave substantial headroom: chemistry-specialized models fall to the random-baseline floor on multi-axis selection, while open-weight models have closed most of the gap to proprietary frontier models. We release the final human-reviewed benchmark test set and evaluation code.
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All authors Lingli Ge, Yubin Wang, Junyuan Gao, Jiahe Song, Jiaxing Sun, Boyu Zhu, Haote Yang, Jingchao Wang, Lixin Ma, Jiang Wu, Yuqiang Li, Conghui He

Chemical Physics · Cross-listed from quant-ph

Normal-ordered perturbative expansion for matter systems interacting with continuous-mode quantum photon fields

Liwen Ko, Robert L. Cook and K. Birgitta Whaley

We develop a normal-ordered perturbative expansion method to compute the reduced dynamics of a matter system interacting with a general continuous-mode photon field state. In this expansion, all field correlation functions are automatically normal-ordered, while the free evolution is modified by spontaneous emission. Our normal-ordered expansion elucidates the physical meaning of non-normal-ordered correlation terms in the conventional expansion. Furthermore, under intermediate coupling strengths, the normal-ordered expansion converges significantly faster than the conventional expansion, allowing one to extend the applicability of perturbative expansion to much larger matter-light coupling strengths. Interestingly, given an $m$-photon Fock state input, the normal-ordered expansion truncates exactly at the $2m$-th order. For special classes of input states, we show the normal-ordered expansion is consistent with the corresponding master equations. We demonstrate the normal-ordered expansion numerically for a two-level system interacting with coherent state, $m$-photon Fock state, and cat state inputs.
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Chemical Physics · Cross-listed from quant-ph

Input-output formulation of quantum light spectroscopy

Liwen Ko, Robert L. Cook and K. Birgitta Whaley

We develop an input-output formulation of quantum light spectroscopy (QLS) by combining the input-output theory, commonly used in quantum optics, with the perturbative expansion method for nonlinear spectroscopy, commonly used in chemical physics. The use of the input-output relation provides physical intuition for how matter systems alter the photon input field. As examples, we consider linear spectroscopy, fluorescence, intra-beam $G^{(2)}$, and Hong-Ou-Mandel correlation. For certain initial photon states, we show that signal expectation values can be evaluated non-perturbatively. For general initial photon states, the signals can be computed via a perturbative expansion of the input-output relation. The input-output relation reduces the work needed to perturbatively expand the optical signal, allowing for cleaner derivations and clearer physical interpretations.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family

Jonas Hänseroth, Rose Asuka Baroness von Stackelberg and Christian Dreßler

Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of electronic-structure theory. Here we present a potential that is universal within the class of water-free solid-state hydrogen-bond network mediated proton conductors rather than across chemistry and a database of 4.4 million first-principles configurations spanning 55 materials. It surpasses leading general-purpose potentials across this domain, recovering measured activation energies and the ordering of anion rotational dynamics that those models miss; agreement on static structure does not imply agreement on transport. Accuracy falls for compositions far from the training set but stays competitive for close structural relatives, and a higher level of electronic-structure theory is reached with a few hundred additional configurations per material. We further introduce a compact variant carrying a fifth of the parameters, faster still and yet more accurate than every general-purpose model tested. It sustains more than a quarter of a million atoms on a single graphics processor, placing grain boundaries and the transition into the highly conducting phase within reach, and making a quantum treatment of the protons affordable. Reference accuracy and accessible system size thus become largely independent, offering a template for other compound classes.
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Chemical Physics

A Machine-Learned Interatomic Potential Free-Energy Surface for the First Step of MIL-101(Cr) Secondary Building Unit Formation

Orlando A. Mendible-Barreto and Yamil J. Colón

Metal-organic framework (MOF) self-assembly mechanisms remain poorly characterized because ab initio molecular dynamics (AIMD) are accurate but too computationally expensive to converge the free-energy surfaces (FES) that govern secondary building unit (SBU) nucleation and growth. This work addresses that limitation for the first step of MIL-101(Cr) SBU formation. Using MACE-POLAR-M, a long-range-aware equivariant machine-learned interatomic potential fine-tuned on a compact DFT reference dataset built from exploratory metadynamics, this work obtains a converged 2D FES for this step at a level of theory ({\omega}B97M-V/def2-TZVPP) at which AIMD would be prohibitively expensive. The pre-trained model samples relevant configurations but gets the thermodynamics wrong, predicting an endothermic reaction and a false global minimum. Fine-tuning corrects both, recovering the expected exothermic process and the correct product basin in agreement with proposed literature mechanisms. The resulting FES also refines the mechanism. It shows that water release from the chromium center proceeds stepwise, through two sequential energy barriers, rather than the single concerted step originally proposed. We find that model accuracy is specific to the reaction coordinate and does not extend to the dissociated fragment configurations excluded from fine-tuning, a direct consequence of the training-data choice that protects accuracy along the reaction path for the FES prediction task. Together, these results establish a validated, computationally tractable route to obtain free-energy surfaces for MOF formation chemistry, transferable to other reactions where AIMD remains prohibitive, and identify training-data composition as the key determinant of which reaction properties a fine-tuned checkpoint can reliably describe.
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Chemical Physics

Chiral Temporal Structures in Molecular Rotational Dynamics

A. G. Löhr, O. Smirnova and M. Mirahmadi

We introduce chiral rotational wavepackets: coherent superpositions of a few low-lying rotational states of a molecule for which the expectation value of a molecular axis traces a three-dimensional chiral trajectory in the laboratory frame. The handedness is a dynamical property of the quantum state rather than of the equilibrium structure, and we show that it can be imprinted on linear rotors as well as on spherical and symmetric tops. We derive the action of space inversion, time reversal, and their product on such wavepackets in the Wigner $D^J_{MK}$ basis, and define the rotational enantiomers of a given wavepacket. To quantify their handedness we introduce two derivative-based pseudoscalar measures: a geometrical measure built from the curvature and torsion of the trajectory, which is even under time reversal, and a dynamical measure, which is odd under time reversal. The latter is shown to be proportional to the geodesic curvature of the trajectory projected onto the unit sphere, which links it, via the Gauss–Bonnet theorem, to the solid angle enclosed by the projected trajectory and to the parallel-transport holonomy of a molecule-fixed vector. Finally we show that a nonzero geometrical measure is a sufficient condition for the wavepacket to exhibit true chirality through its orientation trajectory in the sense of Barron.
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Chemical Physics

An F12 Correction for Multicomponent MP2

Mary J. Richardson, Gabrielle B. Tucker and Kurt R. Brorsen

Multicomponent methods treat both electrons and nuclei quantum mechanically using the standard machinery of electronic structure theory. Previous work has demonstrated that quantitatively describing electron-nuclear correlation in multicomponent many-body perturbation theory methods requires large electronic basis sets with high-angular momentum atomic orbitals, and calculations on large systems can therefore become impractical. Similarly, the slow convergence of the electron-electron correlation energy with respect to highest atomic-orbital angular momentum is a well-known issue in standard electronic structure theory in which only the electrons are treated quantum mechanically, and numerous explicitly correlated methods have been introduced to mitigate this poor convergence. Motivated by the success of single-component explicitly correlated methods, we generalize the single-component MP2-F12/3C(FIX) approach for electron-electron correlation to electron-proton correlation to reduce the need for large electronic basis sets in multicomponent calculations. We also implement the complementary auxiliary basis set (CABS) singles correction for the electrons and protons. Our calculations show that the multicomponent MP2-F12 electron-proton correlation energy is within a few percent of the complete-basis-set (CBS) estimate obtained by QZ-5Z inverse-cubic extrapolation with PB4-D held fixed at every electronic basis-set cardinality tested, with the aug-cc-pVDZ basis set recovering 96-98% of the extrapolated CBS electron-proton correlation energy and exceeding the conventional multicomponent MP2 value at the aug-cc-pV5Z level. These results indicate that explicitly correlated methods can substantially reduce the electronic basis-set requirements of multicomponent calculations.
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Chemical Physics

Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization

Lizhong Fu, Jianan Wei, Wenguan Wang et al. (+1 more)

Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a shared representation of the geometry-dependent wavefunction coefficients. We introduce geometry-conditioned foundation neural-network quantum states for molecular electronic structure in second quantization. A single autoregressive model learns a family of ground states from sparse anchor geometries and provides wavefunctions at untrained geometries without further optimization. Orbital alignment matches orbital identities and transports their phases, establishing an aligned orbital basis across geometries. Frozen energies reach chemical accuracy at every untrained query geometry for N$_2$, CO, and H$_4$. On additional molecular paths, the energy-trained wavefunctions yield dipoles, quadrupoles, and natural occupations without property labels. Across three paired N$_2$ training seeds, orbital alignment lowers the mean absolute energy error over all untrained query geometries from 34-37 mHa to 0.049-0.085 mHa. At approximately 1 mHa mean absolute error, frozen evaluation reduces the per-geometry cost by $986\times$ relative to independent optimization, yielding an estimated $25.8\times$ end-to-end GPU-cost reduction on a 161-point N$_2$ grid.
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All authors Lizhong Fu, Jianan Wei, Wenguan Wang, Honghui Shang

Chemical Physics · Cross-listed from cond-mat.soft

Defect Organization in Coexisting Hexagonal and Square Lattices on Ellipsoids

Wenyu Liu, Han Xie, Baohui Li et al. (+2 more)

Curvature and topology jointly organize defects in two-dimensional crystals, but their combined role remains unresolved when competing lattice symmetries coexist with spatially varying curvature. We use simulated-annealing Langevin dynamics to study Hertzian particles forming coexisting hexagonal (Hex) and square (Sq) lattices on prolate and oblate ellipsoids. Mapping reduced density and aspect ratio reveals a broad sequence of scar and domain-based morphologies in both Hex-dominant and Sq-dominant backgrounds. Latitude-resolved comparisons show that Gaussian curvature biases defects toward its maxima under weak deformation. Strong prolateness, however, confines high curvature to small polar caps that cannot independently accommodate all defect motifs. Defects then spread toward lower-curvature latitudes to relieve defect crowding and elastic repulsion. In the Hex-dominant regime, this competition drives vertex-contacted domains with neutralized corner contacts, and compensating positive defects locate away from the poles. The Sq-dominant regime features Hex-rich triangular domains, bridged states, and linear or open scars similarly reorganized by curvature anisotropy. On oblate ellipsoids, the extended equatorial high-curvature belt allows defects to separate azimuthally while remaining curvature-localized. This work elucidates that nonuniform curvature can engineer rich defect patterns by selecting the spatial distribution of topological charge and the connectivity of finite defect motifs.
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All authors Wenyu Liu, Han Xie, Baohui Li, Jeff Z.Y. Chen, Yao Li

Chemical Physics

What Does a Semilocal Machine-Learning Correction Actually Learn? Size-Dependent Errors across Four Parent Functionals

Abhishek Bhattacharjee, Kishan Kumar Mohanta, Subrata Jana et al. (+1 more)

Machine-learning (ML) corrections to density functional approximations (DFAs) offer a route to improving electronic-structure predictions while retaining the efficiency of semilocal functionals. An important question is whether such corrections learn transferable improvements or instead compensate for errors specific to the parent functional. Here, we address this question by applying the semilocal ML correction of Wang et al. [J.~Chem.~Phys.~158, 154107 (2023)] to PBE, B3LYP, SCAN, and r$^2$SCAN, while keeping the network architecture, loss function, training set, and optimization protocol unchanged. We find that the learned correction develops a systematic size-dependent contribution to atomization energies for all four parents, with $|r|\geq0.9999$ along the $n$-alkane series, where r is the Pearson coefficient of the linear fit. Its magnitude and sign depend systematically on the parent: the correction partially compensates the size-dependent errors of PBE and B3LYP, but introduces substantial size-dependent contributions for SCAN and r$^2$SCAN, whose parent errors show little size dependence. In contrast, ionization potentials, electron affinities, and isomerization energies are largely unaffected. Spatial decomposition further reveals distinct origins of this extensive contribution, with bonding regions dominating for B3LYP, SCAN, and r$^2$SCAN and core regions dominating for PBE. These results show that the global-energy ML correction is strongly dependent on the error structure of its parent DFA, highlighting size-dependent error accumulation as a critical consideration in the assessment and development of ML-enhanced density functionals.
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All authors Abhishek Bhattacharjee, Kishan Kumar Mohanta, Subrata Jana, Prasanjit Samal

Chemical Physics

The NewMag crystal-field code for f-element systems: Implementation for extended active spaces, second-order correlated energies, and generalisation to fn configurations

Gwenhaël Duplaix-Rata, Dumitru-Claudiu Sergentu, Boris Le Guennic et al. (+1 more)

We report a massive update of the NewMag program, which enables to extract Stevens crystal-field parameters (CFPs) after relativistic and multiconfigurational calculations are performed. The code can now post-treat ORCA and OpenMolcas outputs that contain state-average complete active space self-consistend field (SA-CASSCF) calculations with minimal or extended active spaces, second-order NEVPT2 or CASPT2 calculations, and spin-orbit configuration interaction (SOCI) calculations. Stevens parameters are extracted following the original Stevens convention with the so-called "extended" parameters. Rotation invariant indicators are also computed, after applying a correction of the parameter values to ensure normalization, following Rudowicz. At the SOCI level, the spin-orbit coupling (SOC) constant is also extracted within a spherical approximation of the SOC operator. For all the reference calculation levels, the model spectrum is reconstructed, allowing a direct assessment of its quality. The reported implementation is successfully applicable to f-element systems with a non-void, non-half-filled/empty or non-full f shell, that is for f^n configurations with n != 0, != 7 and != 14. Similarities and differences with the SINGLE_ANISO and AILFT codes are discussed. With selected examples, we showcase the interest of determining CFPs in f-element systems to understand their magnetic and optical properties in general, and more specifically the added value of NewMag. Finally, application of this approach to d-element systems is discussed, to reveal when it readily and successfully applies and when it may fail in reproducing satisfactorily the ab initio energies.
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All authors Gwenhaël Duplaix-Rata, Dumitru-Claudiu Sergentu, Boris Le Guennic, Rémi Maurice