Wednesday, 4 November 2026: 17 new papers, 3 cross-lists and 8 replacements in Chemical Physics (physics.chem-ph), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Accurate long-horizon battery ageing prediction remains challenging because degradation alters not only scalar health indicators but also the shape and duration of electrochemical cycling profiles. Here, we introduce an autoregressive deep sequence-learning framework to forecast voltage-current profiles and state-of-health (SOH) evolution in NVPF/hard-carbon sodium-ion batteries. Stacked LSTM (Batt-LSTM) and hybrid CNN-LSTM (Batt-LSTM-C) models learn from voltage, current, and cycle-index sequences to generate future cycling profiles iteratively from measured data. Scheduled sampling mitigates the training-inference mismatch during autoregressive rollout, extending stable forecasting horizons from a few tens of cycles to as many as 600 cycles while preserving local waveform fidelity. Horizon-dependent benchmarking against classical machine-learning models evaluates voltage mean absolute error (MAE), global and local dynamic time warping (DTW), and SOH MAE across five forecasting horizons. Batt-LSTM with scheduled sampling achieves the lowest voltage MAE at horizons from 5,000 to 20,000 time steps. The predicted profiles also enable extraction of capacity-, resistance-, and energy-based SOH indicators, together with internal-resistance evolution. Monte Carlo dropout and deep ensembles estimate predictive spread at local profile and global degradation-trajectory scales. Fine-tuning on an independent ageing dataset improves cross-cell adaptation, with rolling-horizon updates reducing SOH prediction error to 3.844 percentage points for a 200-cycle horizon. These results show that curve-level forecasting retains diagnostically relevant electrochemical information beyond scalar SOH regression and offers a route toward uncertainty-aware, adaptive battery-health monitoring.
Methyl acetate production via direct carbonylation of dimethyl ether and carbon monoxide involves strong nonlinear interactions among reactor operation, recycle structure, and energy consumption, making conventional simulation-based optimization computationally demanding. In this work, a data-driven framework integrating steady-state process simulation, surrogate modeling, and Bayesian optimization was developed for process intensification. A dataset of 2730 steady-state simulations was generated using a Python-based automated process simulation workflow and a structured design of experiments across the operating domain. Surrogate models were trained to predict carbon conversion yield (C-yield) and cost of specific energy consumption (C-SEC), with neural network-based models showing the best predictive performance and robustness. These models were integrated into a multi-objective Bayesian optimization framework to identify optimal operating regions. Results indicate that high C-yield (up to ~95%) can be achieved with reduced energy cost through lower recycle flow rates and compression demand. Pareto analysis revealed a nonlinear trade-off between C-yield and C-SEC, where further improvements near the conversion limit require disproportionately higher energy input. Validation against rigorous simulations showed deviations below 3% for C-yield and 10% for C-SEC.
Molecular design requires coordinated generation, property prediction and iterative editing, with each decision linked to the correct chemical structure and supporting evidence. Integrating these capabilities in practice presents three major challenges: retaining local control of molecular data, connecting specialized models across tasks and verifying that proposed changes satisfy chemical constraints. Here we present MolCrew, a locally deployable system that coordinates four separately trained molecular specialists with a language-model orchestrator and RDKit calculations. The specialists generate structures from descriptions, describe molecular structures, predict biological endpoints and propose edits towards specified goals. Separate model weights allow each capability to be evaluated and updated independently within the workflow. RDKit calculations check structural validity, quantify descriptor changes and assess specified structural constraints, while returned observations guide further tool calls. We evaluated low-rank adaptation of the orchestrator for tool-use decisions and failure recovery while leaving specialist weights unchanged. On 3,300 ChEBI-20 test examples, MolCrew achieved 96.4% exact structure recovery, compared with 38.7% for published Atomas-large results. Across eight MoleculeNet datasets evaluated using scaffold splits, mean area under the receiver operating characteristic curve was 0.851, compared with 0.770 for published Atomas results. In a recorded evaluation using the unadapted orchestrator, 1,554 of 1,699 requests (91.5%) passed every predefined workflow check. By preserving inspectable links between molecular proposals, model outputs and chemical calculations, MolCrew provides a modular framework for computational candidate assessment and refinement under local data control.
We investigate pair-weighted lattice energies constructed from the reciprocal ratio cost $J(x)=\frac{1}{2}(x+x^{-1})-1$ and their connection to the von Weizs\"acker kinetic-energy functional. When the pair weight depends only on the common multiplicative amplitude scale and is quadratic under uniform amplitude rescaling, it takes the form $B(u,v)=Cuv$. For the normalization $C=1$, the weighted edge cost reduces exactly to $\frac{1}{2}(u-v)^2$ for arbitrary positive neighboring amplitudes, without requiring a small-deviation expansion. On a Cartesian lattice, the resulting discrete energy converges to $\frac{1}{2}\int |\nabla u|^2,d\mathbf r$, with leading correction $-\frac{h^2}{24}\sum_k\int(\partial_k^2u)^2,d\mathbf r+O(h^4)$ under the stated regularity conditions. Identifying $u=\sqrt{\rho}$ and restoring the factor $\hbar^2/m$ yields the von Weizs\"acker functional. By contrast, the unweighted ratio cost approaches a scale-invariant log-Dirichlet action and does not have kinetic-energy dimensions. Numerical benchmarks confirm the expected lattice convergence and assess the discretization error in smooth, nodal, cusp, and molecular cases. For $\mathrm{H}_2^+$ densities obtained from aug-cc-pV5Z orbitals, a four-grid least-squares $h^2+h^4$ extrapolation agrees with orbital kinetic energies within $1.5\times10^{-4}$ relative over $R=1$--$6,a_0$. Alternative extrapolations indicate a conservative sensitivity of up to approximately $5\times10^{-4}$. The construction therefore provides a conditional exact discrete representation of the von Weizs\"acker sector rather than an approximation to the complete noninteracting kinetic functional.
Accurate prediction of phase equilibria is essential for chemical process design, particularly in systems characterized by complex intermolecular interactions. This work investigates the influence of conformer selection and sigma-profile characteristics on COSMO-RS predictions of liquid-liquid equilibria (LLE), using the tetrahydrofurfuryl alcohol (THFA)-methyl isobutyl ketone (MIBK)-water system as a case study. Calculations were performed at the TZVP and TZVPD-FINE levels of theory. While TZVP failed to capture the experimentally observed phase behavior, TZVPD-FINE provided substantially improved predictions. The differences are attributed to the relative importance assigned to less-polar conformers and more-polar conformers capable of hydrogen bonding. Although the TZVPD-FINE calculations reproduced the main experimental trends, the predicted organic-phase composition showed a mean percentage deviation of 5.27%, slightly exceeding the adopted 5% agreement threshold. Investigations using individual conformers demonstrated that predictions can be strongly influenced by conformer selection and that representative conformer ensembles are necessary for reliable results. Further analysis showed that diversity in sigma profiles is more important for predictive accuracy than the total number of conformers. These findings emphasize the importance of carefully selecting conformers when applying COSMO-RS to liquid-liquid equilibria involving phases of contrasting polarity.
This chapter accompanies the lectures on Hartree--Fock theory and on basis sets and molecular integrals at the \href{https://www.esqc.org}{European Summer School of Quantum Chemistry (ESQC) 2026}. Intended for doctoral students, it develops the single-determinant description of electronic structure from antisymmetry and the variational principle to the orbital equations and their finite-basis matrix representation. Detailed derivations connect the energy functional, the Fock operator, excited determinants, and the interpretation of orbital energies. Restricted, unrestricted, and generalized formulations show how the choice of variational space affects spin symmetry, symmetry breaking, and the limitations of the Hartree--Fock approximation. The computational development covers orthogonalization, self-consistent-field iteration, convergence acceleration, overlap-based occupation selection, and orbital analysis, distinguishing orbital stationarity from stability. A connection to second-order M{\o}ller--Plesset perturbation theory explains the correlation-energy expression and the cost of transforming electron-repulsion integrals from atomic to molecular orbitals. The basis-set and integral development examines radial and angular flexibility, contraction, basis-set incompleteness, and superposition error. The Gaussian product theorem leads to one- and two-electron integrals, recurrence relations, integral screening, and low-rank representations. Worked examples and exercises with detailed solutions connect these derivations to practical calculations. Historical perspectives and explicit notation conventions provide context for the approximations and algorithms that underpin molecular electronic-structure theory.
Redox control in molten fluoride salt reactors determines actinide solubility and corrosion of structural alloys, but the role of local fluoride coordination in this control remains poorly understood. We use density functional theory with explicit LiF microsolvation to study the coordination chemistry, intrinsic redox energetics, and fluoride-exchange kinetics of Th(IV), U(IV), and U(III) fluoride complexes. The coordination response to solvation primarily follows the formal charge of the complex rather than the actinide at its center: we find that cationic and neutral complexes gain fluoride ligands, most anionic complexes keep their coordination number, and $4-$ complexes lose fluoride. Coordination number then strongly controls the intrinsic U(IV)/U(III) reduction energy. From CN 4 to CN 7, this energy rises by up to $11$ eV and changes sign; a continuum-solvation estimate opposes this trend and therefore establishes the intrinsic value as an upper bound. Fluoride exchange barriers are low, so redox partners of different charge do not keep the same coordination numbers in solvated clusters. Finally, above a threshold between CN 5 and CN 6, single-reference DFT converges to a ligand-reduced U(III) state inconsistent with formal $5f^3$ occupation, however the physical nature of this is still unresolved. Therefore, local coordination is a primary variable in the prediction of redox behavior in molten actinide-fluoride salts.
Charge equilibration assigns atomic charges by equalizing an atomic chemical potential built from electronegativity, hardness and the Coulomb interaction. Under one global constraint it lets charge flow without limit between molecules, which fluctuating-charge models prevent with one neutrality constraint per molecule. A metal needs the opposite: a global constraint and a hardness near zero, and there the charge operator becomes indefinite unless it includes each charge's own self-energy. Here, we present samQEq (solids-and-molecules QEq), a unified charge-equilibration package for LAMMPS that combines the two constructions in one energy functional and one matrix-free solve. An orthogonal projector imposes one neutrality constraint per molecule, and the finite-width self-energy of a Gaussian charge on designated conductor types, narrower than the conductor's own Gaussian, keeps the conductor's part of the operator positive definite at any hardness. On a gold slab carrying a water film, the operator stays definite down to the conductor limit of the model, the metal screens a point charge by a constant amount at every height beyond 4 {\AA}, and the film's charges stay bounded under dynamics. Reciprocal-space electrostatics is evaluated inside the matrix-vector product, and three distinct shielding kernels are run-time settings. We verify the implementation by reproducing a published fluctuating-charge water model without refitting, and the native ReaxFF charge-equilibration step to approximately $10^{-9}\,e$. On a single four-V100 node the largest tested systems run at approximately twice the throughput of a 128-core CPU node. samQEq thus provides one charge-equilibration layer for molecules, conductors and the interfaces between them.
Expert chemists perform retrosynthetic analysis in a strategic manner, where each reaction in a route is employed with a specific purpose, combined with mechanistic reasoning represented as electron-pushing arrows to visualize how bonds are formed or broken via electron redistribution. Yet contemporary single-step retrosynthesis models exhibit neither of these properties: their learned reaction distribution is fixed to that of the training data, and reactions are predicted via de-novo generation of the precursors or molecular graph edits, omitting the electron redistribution that governs the chemical transformations. We introduce Verso MAELLE, the first electron-pushing-based single-step retrosynthetic model, representing disconnections as continuous-time Markov chains (CTMC) comprised of edits in the electron space, and is trainable using a discrete flow matching formulation. Being a generative model, Verso MAELLE is optionally steerable at inference time through the Feynman-Kac (FK) framework, using rewards that promote certain types of disconnections. We show that besides in-distribution learning, Verso MAELLE can be steered toward cyclization reactions which are scaffold-constructive but underrepresented in the training corpus, via a ring-breaking reward. As a proof of concept, we show how the electron-pushing reasoning, learned from patented data, combined with a domino incentivizing reward, could steer Verso MAELLE toward creative and strategic disconnections, demonstrated on the key domino sequence that was used to synthesize Punctaporonin U, a complex natural product of the sesquiterpene family.
Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax\_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation resolves candidate channels and records sampled chain maxima. Single-search benchmarks on three two-dimensional potentials recovered an intermediate and competing routes without transition-state guesses. For eight of nine gas-phase reactions on GFN2-xTB, sampled chain maxima differed from independently refined saddle energies by less than 0.76~kJ\,mol$^{-1}$. Alanine dipeptide instead revealed incomplete conformational relaxation, showing that route connectivity can persist despite structural and energetic errors. Neural committor learning and local straddling-pair sampling connect the resulting paths to transition-region sampling, although molecular shooting diagnostics indicate limited committor calibration. These results show how energy-only exploration and continuation recover candidate pathways, while distinguishing route connectivity from geometric localization, barrier resolution and finite-temperature transition statistics.
Level-set simulators of etching and deposition move the surface with a velocity that depends on adsorbate coverages. These coverages are usually computed from balances solved and coded by hand for each chemistry, whereas voxel-based Monte Carlo profile simulators read the reaction mechanism as data. For the open-source level-set simulator ViennaPS, this work presents a surface model whose rate laws, Jacobian and velocity are derived from a reaction file and evaluated with ray-traced fluxes. Steady-state coverages are obtained with Newton's method and transient coverages with a conservative integrator. One compiled model is applied to eight chemistries for chemical vapour deposition, selective epitaxy, plasma etching, sputtering and atomic layer deposition. It reproduces a hand-written silicon etch model to seven significant digits at a single point and within the Monte Carlo noise in a masked trench. In a high-aspect-ratio cavity, alumina conformality is determined by a trimethylaluminium reaction probability within the measured range.
Metalloprotein docking requires a ligand pose to satisfy both global pocket fit and the identity and geometry of metal-coordinating atoms. General-purpose diffusion docking models do not explicitly condition generation on externally specified complete ligand donor sets, and their predicted poses can exhibit incorrect metal-donor relationships. Metal-aware models address coordination during training and generation; here we ask whether external donor information can correct already-trained general-purpose models without retraining. We propose Complete-Identity Contract Guidance (CICG), which applies a supplied complete donor set through translation, rotation, and torsion updates, and Historical Contract Transfer (HCT), which supplies donor identities from earlier complexes at compatible receptor sites. We compare HCT with trajectory-local controls retaining the historical donor count with or without elemental composition. On 86 assessable HCT-covered queries, HCT-guided CICG increases frozen-confidence-selected joint success from 21.5% to 36.6% on SurfDock and from 25.0% to 34.9% on DiffDock-L. The joint criterion requires correct global ligand placement, complete native donor recovery, coordination geometry, preserved stereochemistry, and absence of fatal receptor clashes. Composition-matched guidance reaches 33.14% and 34.30%, leaving the additional benefit of fixed historical membership unresolved. Our results therefore support donor-set guidance as a conditional correction for frozen docking models, while identifying donor-source reliability and physical-quality preservation as the main barriers to broader use.
Foundation machine-learning interatomic potentials (MLIPs) offer one model for many chemistries, but their deep, globally coupled architectures do not fit the spatial decomposition that scales classical and short-ranged MLIPs. We present pair_style uma, a LAMMPS pair style that evaluates the Universal Model for Atoms (UMA) through LibTorch on NVIDIA and Intel GPUs from a single source tree. It reimplements FairChem's edge-sharded graph parallelism in C++ over exported TorchScript modules, with one MPI rank per device, NCCL or oneCCL collectives, and the whole energy and force path in double precision. The strain-gradient virial is carried through the exported modules and the cross-device reductions, so that fix npt runs on both vendors' hardware. We measure NPT capacity and cost on one 4xA100-40GB node, one 8xH200 node, and one 12-tile Intel node, and on up to 32 Intel nodes. Without activation checkpointing, one node holds NaCl cells of about 8,000 atoms (A100, 0.21 ns/day), 22,000 atoms (Intel, 0.10 ns/day), and 47,000 atoms (H200, 0.16 ns/day). Graph parallelism raises the capacity of a single device by 8.0x on H200 and 4.6x on A100. Across nodes, capacity grows to about 779,000 atoms on 32 Intel nodes, with a slower speed at 0.007 ns/day. On H200 and A100, a FLOP count of FairChem's graph-parallel predictor gives lower bounds of 31% and 51% of FP64 peak. From one omat checkpoint and with no refit, nine 100 ps NPT heating ramps (NaCl, equiatomic Pt-Ru, and equiatomic (HfNbTaTiZr)N, on all three node types) ran to completion.
Carbonic acid (H2CO3) is connected with the dissolved CO2 and bicarbonate (HCO3-) equilibria in aqueous solutions. Its two elementary reactions in water, acid dissociation and dehydration to CO2, are difficult to resolve because both are coupled to proton transfer through hydrogen-bonded networks. In this work, we combine 0.5 ns of unbiased PBE-D3 molecular dynamics of 1 molar solution of H2CO3 with metadynamics on the O - H coordination (H2CO3 = HCO3- + H+) and the C - O distance of the leaving hydroxyl (H2CO3 <=> CO2 + H2O). These unbiased trajectories show frequent cis-cis <=> cis-trans interconversion through long-lived bicarbonate (well separated from solvated proton, H3O+, and also mediated by a short-lived three-water [H2O...H3O+...H2O] ring that acts as a solvent-shared ion pair (SSIP); only 16-23% of deprotonation events reach a solvent-separated ion pair (SShIP). Resolving ion-pairs as SSIP for proper reaction leads to an improved estimate of pKa1 from 1.1 to 2.4, toward the experimental 3.45-3.59 (measured at supposedly infinite dilution). Further, metadynamics simulations provide a dehydration barrier of 17.8 (1D) and 15.3 kcal/mol (2D), suggesting that the final product CO2 + H2O is 2.8 kcal/mol below H2CO3. Our two-dimensional free energy surface also shows that C--O cleavage proceeds through the CO2 + OH- which further goes down by 9.9 kcal/mol to product CO2 + H2O.
Many processes in chemistry and physics, such as autoionization, photoionization, or electron-impact collisions, involve unbound electrons. Despite their importance, theoretical methods for modeling continuum electrons remain significantly less developed than those for purely bound electronic states. Here we present a novel approach for treating continuum electrons within Gaussian-based quantum chemistry. The method represents continuum orbital in a discrete basis of standard Gaussian-type orbitals (GTOs), where the continuum wave function is obtained by solving the Lippmann-Schwinger equation with proper boundary conditions using an analytical representation of the Green's function in an auxiliary GTO basis set. The resulting continuum orbital is multicentered and multichannel, providing a rigorous description of scattering states. As an initial application of the new methodology, we compute Auger decay rates for several atomic and molecular systems, including Ne, H$_2$O, CO$_2$, and Ar. We demonstrate a consistent improvement of the results with increasing the quality of the continuum representation. We show also that higher-order electron-correlation effects in the bound domain are essential for an accurate treatment of satellite transitions. The present formalism therefore provides a systematically improvable Gaussian-based framework for continuum-electron problems that is fully compatible with standard quantum chemistry methods.
Proton transfer is responsible for the anomalous high conductivity in acidic or basic aqueous solutions. Yet, the contribution of the proton transfer mechanism to the conductivity of concentrated aqueous solutions is unknown. In this work, we compute the transport properties of KOH(aq) systems with different concentrations, using Molecular Dynamics simulations with a Machine Learning Interatomic Potential (MLIP), which includes proton transfer. We quantify the contribution of the proton transfer mechanism to the diffusivities of hydroxide ions and water molecules, and the ionic conductivity and transport numbers of the electrolytes. The contribution is substantial at lower concentrations, but diminishes at very high concentrations.
Quantum impurity calculations become challenging when multiple correlated orbitals are treated explicitly, particularly in nonequilibrium steady states. We introduce Coupled-Cluster Coupled Lindblad (C$^3$L), a deterministic approach that combines the recently developed coupled-Lindblad compact bath representation with coupled-cluster theory to compute steady states and Green's functions directly in imaginary and real frequency. C$^3$L has favorable polynomial computational complexity and avoids the sign problem of quantum Monte Carlo (QMC). In a real-time multiorbital benchmark, it agrees closely with state-of-the-art QMC while reducing computational cost by four to five orders of magnitude, with a further 10--100-fold speedup on a GPU. Beyond the practical reach of established real-time QMC solvers, four-orbital nonequilibrium impurities with general interactions and biased reservoirs become accessible with C$^3$L. We obtain resonance-resolved transport signatures relevant to scanning tunneling microscopy experiments on molecular junctions.
Long-range interactions in machine-learning interatomic potentials are commonly treated by coupling learned sources through either a prescribed electrostatic kernel or a learned but configuration-independent one. Whether such a kernel suffices depends on the underlying screened interaction. We address this question by using Fourier neural operators (FNOs) to learn how latent source fields, built from the descriptors of a local backbone (here MACE), interact across the simulation cell. For a synthetic ionic system with tunable screening, a prescribed Coulomb kernel underestimates the long-wavelength charge fluctuations by 43% under strong screening, whereas the FNO reproduces them within 3% and recovers 95-99% of the variance of the residual left by the local backbone. A linear FNO, which learns a configuration-independent kernel, performs equally well in the homogeneous case. Under inhomogeneous screening, rearranging the remote medium modulates the coupling between distant ions, a dependence that no configuration-independent kernel can represent. Among the models tested, only the nonlinear FNO responds to this modulation, and its force errors are 27-41% lower than those of configuration-independent models. In first-principles benchmarks of liquid water, lithium lanthanum zirconate, and Au2/MgO(001), MACE-FNO is comparable to a prescribed-kernel model for the bulk systems and more accurate for the interface. It further captures the reversal of the Au2 wetting preference induced by remote Al dopants, with deviations below 5 meV per cell from density functional theory. These results indicate that a configuration-dependent kernel, as learned by FNOs, becomes necessary once the screening medium varies, even though the energy remains quadratic in the charges for a fixed medium.
Sample-based quantum diagonalization (SQD) accuracy remains limited for strongly correlated systems like iron-sulfur clusters. This accuracy heavily depends on the mean-field reference state, which determines the sampling ansatz and configuration-recovery target. We extend the qcsc-prefect workflow to support a broken-symmetry unrestricted Hartree-Fock (BS-UHF) starting point, derived from atom-localized antiferromagnetic initial guesses. For the $[Fe_4S_4(SCH_3)_4]^{2-}$ cubane (54 electrons, 36 orbitals, 72 data qubits; 88 total qubits), this lowers the mean-field reference by $\approx 534$ mHa relative to the Restricted Hartree-Fock (RHF) solution (-327.0809 vs. -326.5474 Ha). Combined with deep multi-step configuration recovery on ROQUO GPU nodes at subspace dimension $d = 3 \times 10^{10}$, we reach $E = -327.235076$ Ha. This is 4.3 mHa above the finite-$D = 4000$ DMRG value (-327.2393952 Ha) and $\sim 512-517$ mHa below a previous same-active-space RHF-reference SQD calculation. Extrapolating to the $d \to \infty$ limit via a Gaussian-in-log $d$ fitting ansatz gives an asymptotic estimate $E_\infty = -327.244468$ Ha $\pm 1.05$ mHa, just 0.33 mHa above our discarded-weight-extrapolated DMRG estimate ($E_{D\to\infty} = -327.244797$ Ha $\pm 0.46$ mHa), well within their combined $1\sigma$ uncertainty. We characterize the determinant-subspace composition driving convergence, show matching validation on $[Fe_2S_2(SCH_3)_4]^{2-}$, and report multi-HPC execution on Fugaku, Miyabi-G, and ROQUO. We conclude that reference-state preparation, rather than subspace size alone, is a key driver of SQD energy accuracy under strong electronic correlation.
Polaron hopping in disordered molecular semiconductors such as eumelanin is set by energetic disorder $\sigma$ and reorganization energy $\lambda$. We show that the reduced polaron absorption band encodes this transport problem. Its value at zero energy fixes the mean hop rate, which bounds the mobility in any dimension. For Gaussian disorder and classical vibrations its centroid is $E_\mathrm{op}=\lambda+(1-c)\sigma^2/k_BT$, its variance obeys the Hush relation, and the bound is the Marcus rate at $\lambda\to E_\mathrm{op}$; a 1D lower bound vanishes at $k_BT^*=(1-c)\sigma^2/\lambda$, read from optics.