Papers

Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar energy conversion, photocatalysis and photo switching. Accurately describing these processes requires a representation that respects electronic symmetry and consistently relates energies to interstate couplings. Here we introduce PEACE, which combines a parity-equivariant latent Hamiltonian with a learned electronic connection. Controlled ablations reveal the complementary roles of symmetry-allowed state mixing and electronic-frame variation in reproducing crossing structures and relaxation dynamics. PEACE closely reproduces excited-state population dynamics from first-principles simulations, while its extension to spin-orbit coupling enables simulations of intersystem crossing. These results demonstrate that a more complete incorporation of the underlying physics into learned electronic representations leads to more accurate predictions of nonadiabatic dynamics.

Quantum electrodynamics (QED) currently features prominently at the precision frontier in describing the structure of atoms and molecules. Dominant QED effects are governed by the electronic structure in the vicinity of the nucleus and are therefore generally regarded as atomic in origin, with molecular contributions largely arising from additive atomic contributions. By studying the nuclear magnetic resonance shielding tensors of thallium in Tl$X$, we reveal genuinely molecular QED effects that strongly depend on the bonding partner ($X$ = F, Cl, Br, I, At) and are absent in the Tl$^+$ ion.

Predicting the dynamic polarizability tensor of organic molecules is essential for simulating light-matter interactions in optoelectronic devices, yet conventional quantum-chemical methods such as time-dependent density functional theory (TD-DFT) are computationally expensive and limit large-scale screening. We present an equivariant graph neural network architecture that predicts frequency-dependent, complex-valued dynamic polarizability tensors directly from readily available molecular information, such as 3D geometry and UV-vis spectra. The model learns to approximate the shape and magnitude of the dynamic polarizability tensor, capturing both dispersive and absorptive features that define the molecular optical response. We evaluate the model on molecules from both the QM9 dataset and the Harvard organic photovoltaic dataset using various metrics, including the earth mover's distance, which is suitable for comparing spectral quantities. For both datasets, the model successfully reproduces the main features of the dynamic polarizability tensor and generalizes across diverse molecules. To assess the applicability, we further validated the model using a downstream workflow to calculate the optical properties of organic photovoltaic devices. We used the model-predicted polarizabilities to perform device-level optical simulations, yielding charge carrier generation rates in agreement with TD-DFT-based results ($R^2 = 0.94$). Our approach offers a promising complement, or even an alternative, to conventional methods such as TD-DFT, enabling faster and more efficient screening of photoactive organic molecules for optoelectronic applications.

Poly(1,3-dioxolane) (pDXL) is a chemically recyclable polyether that can be synthesized by living cationic ring-opening polymerization with precise control of chain length into the ultra-high-molecular-weight (UHMW) regime, where it acquires enhanced mechanical properties. Like most polymers, however, it has amorphous condensed-phase structure, and diffraction measurements alone cannot resolve the atomistic microstructure that underlies them. In this work, we predict the ensemble of the pDXL microstructures by applying a recently developed AI-accelerated ab initio bottom-up polymer structure prediction (AI$^{2}$-BPSP) framework that simulates the living polymerization of pDXL under experimental synthetic conditions with machine-learning force fields trained within van der Waals-corrected hybrid density functional theory. Partitioning the predicted X-ray and neutron diffraction signal between the growing chain and the surrounding monomer resolves its dominant feature into two counteracting modes: a monomer contribution near $q \approx 1.5$ Å$^{-1}$ that decays as monomer is consumed, and a polymer contribution near $q \approx 1.6$ Å$^{-1}$ that is absent in oligomers and grows with chain length. This work paves the way to direct accuracy validation of these statistically significant, first-principles, and chain-length resolved diffraction predictions against experiments.

We present full molecular dynamics simulations that explicitly incorporate the simultaneous interactions between a trapped ion and a bath of neutral atoms. In contrast to conventional molecular dynamics treatments, this framework enables a systematic assessment of how the atomic-gas density influences both the ion's cooling dynamics and its steady-state mean kinetic energy. Our results show that the gas density measurably modifies the ion's average kinetic energy, albeit only weakly, in qualitative disagreement with predictions obtained from standard molecular dynamics simulations. In addition, the calculations indicate that short-range features of the atom-ion interaction potential become increasingly consequential as the atomic density increases. Simulations including many atoms yield higher mean kinetic energies than standard molecular dynamics, a trend we attribute to the transient formation of molecular-ion complexes. Finally, we observe that for a fixed ion, lighter atomic baths lead to a smaller final ion average kinetic energy than heavier atomic baths.

Excited electronic states are difficult to accurately and tractably model, particularly those exhibiting multiexcitonic character. Scalable single-reference excited state methods typically fail to describe such states as they suffer from the defects of the mean-field approximation and/or neglect higher-rank excitation operators. To address this, we derive the [T] perturbative correction to the quantum self-consistent equation-of-motion unitary coupled cluster singles and doubles (q-sc-EOM-UCCSD) method. The method captures triple excitation effects through a classical step of second-order in many-body perturbation theory following a q-sc-EOM-UCCSD calculation on a quantum computer. The perturbative correction developed has a $\mathcal{O}(N^7)$ classical computational time scaling. We incorporate [T] into a hybrid compute strategy wherein eigenvectors of the effective Hamiltonian from the hybrid quantum-classical q-sc-EOM-UCCSD algorithm are relayed to a classical computer which performs the postprocessing required for the [T] correction. The benefits of [T] are quantified in a benchmark covering a variety of electronically excited states of the isoelectronic CH$^+$ and BH molecules. We find that the addition of the [T] correction to both trotterized and full operator variants of q-sc-EOM-CCSD can offer dramatic improvements over baseline q-sc-EOM-UCCSD. Our assessment clearly demonstrates the importance of considering higher-rank excitation operators in q-sc-EOM-UCCSD, quantifies the overall success of the [T] correction, and discusses some of its limitations.

We investigate intra-chain exciton energy transfer (EET) in a conjugated polymer using a reduced three-level donor-acceptor model that separates vertical excitations from vibrationally relaxed states. Population dynamics are obtained with the hierarchical equations of motion (HEOM), enabling a non-Markovian treatment of system-bath (S-B) interactions. Case studies on donor-acceptor (D-A) pairs with small and large energy gaps reveal competition among coherent hopping, vibrational relaxation, and Förster resonance energy transfer (FRET). Varying the hopping and the S-B relaxation rate highlights roles of exciton transfer, thermal relaxation, and dipole-mediated coupling. The results show that energy gap, hopping, and relaxation jointly control intrachain EET.

In this work, we present an amplitude extrapolation ansatz for geometry optimization that uses calculations from previous steps to construct an initial guess for the quasi-Newton solver at each new geometry, significantly accelerating convergence of the coupled-cluster and Lambda solver alike. The ansatz incorporates a necessary amplitude transformation to mitigate discontinuities associated with orbital energy crossings at the Hartree-Fock level of theory, while remaining computational and memory efficient. The method is tested for a diverse set of medium-sized molecules to demonstrate a systematic and significant reduction in the number of coupled-cluster iterations compared to the conventional method.

Nuclear spin relaxation has been widely used to probe molecular reorientation in solids, yet its interpretation has always required assuming both the relaxation mechanism and the form of the underlying correlation function. In rotator-phase solids more unknowns enter the picture: in ammonium iodide, the proton T$_1$ passes through a maximum and decreases with temperature, a behavior attributed to spin-rotation coupling. Here we model both the dipolar and the spin-rotation contributions to $^1$H relaxation in solid NH$_4$I from first principles, by combining molecular dynamics with machine-learned force fields trained on density functionals and ab initio spin-rotation coupling tensors. The computed rates reproduce the measured values above the order-disorder transition, together with their temperature dependence and frequency independence, and show that fluctuations of the NH$_4^+$ angular momentum dominate relaxation throughout the rotator phase. The orientational and angular-velocity correlation functions obtained directly from the simulations further share an oscillatory component at lattice-phonon frequencies, revealing a coupling between lattice vibrations and cation reorientation that contributes measurably to relaxation. Separating this component yields a reorientational barrier in agreement with neutron scattering. The approach makes nuclear spin relaxation a quantitative benchmark for simulated dynamics in molecular solids and provides direct access to angular-momentum correlations in the solid state.

Petri nets have been used to describe chemical processes such as reactions.They map well to chemistry: Places are the bonds between atoms and the free valence of each atom, a token is a unit of bond order, a transition forms or breaks a bond, the conserved quantities are the valence budgets of the atoms, and the enabling rule is the valence rule. These semantics are not guaranteed by learned models of reactions or neural networks that are built on Petri nets that use the net as a scaffold for message passing. Here, we ask what architecture remains a Petri net for every value of its weights. We find the answer in the theory, where all semantics of a net share the firing form $m^\prime=m+C\sigma$, locality, as enabling reads only the inputs of a transition, and the enabling rule, and we prove that conservation forces the firing form and that non-negativity forces the enabling rule on local rate laws. This leaves free the rate law, which is the propensity of each transition to fire. We introduce Neural Petri Flow, which learns this rate law, or a readout for classification, and hard-wires the rest as parameter-free layers. On what we denote a valence net, atom mapping, reaction classification, and forward prediction become three tasks on one firing vector. Without training, the minimum firing vector maps 88.8% of the curated Golden set against 85.6% for RXNMapper, and 88.7 against 77.9% of the enzymatic reactions of EnzymeMap. On USPTO-480K, NPF trained on these firing vectors predicts 87.7% of the products and 67.4% when trained on a 1% subset of the training reactions. EC numbers of ECREACT are predicted at the third level for 90.2% of reactions, 5.6 points ahead of the best published method. With electrons as tokens, the same token game predicts 90.5% of the elementary steps of FlowER first, ahead of the published baseline, and every top-1 prediction is a valid molecule without a filter.

Machine learning can accelerate molecular discovery by designing molecules and planning experiments. However, many scientific challenges demand molecules with very rare properties, and in this sparse setting, existing algorithms offer little gain over random guessing. We propose a method to efficiently search large regions of molecular space using algorithmically controlled stochastic synthesis. Rather than design, make and test individual molecules, we design and make complex mixtures, test them as a pool, then deconvolute the molecule-activity map. We optimize synthesis to encode maximal information. Theoretically, this approach can reduce the number of experiments required to find the optimal molecule among $d$ candidates from $\mathcal{O}(d)$ to $\mathcal{O}(\log d)$ or $\mathcal{O}(1)$. In simulation, on estimated protein fitness landscapes, it finds active molecules with an order of magnitude fewer experiments than existing Bayesian optimization methods.

Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose total configuration energies into simple effective interactions whose parameters are traditionally assigned based on atom or bond types, enabling efficient simulations but also limiting their ability to adapt across configurations. Recent machine learning approaches have improved the accuracy and transferability of bonded parameters in these FFs by inferring them as functions of local atomic environments, but still rely on empirical nonbonded parameters for practical simulations. In this work, we introduce a unified approach, grappa-fullFF, which learns both bonded and nonbonded parameters consistently and simultaneously from ab initio reference data. By incorporating physically inspired regularization via supervision of the electrostatic potential and an architecture that facilitates charge equilibration, our model recovers accurate electric response properties, achieves state-of-the-art accuracy on geometry optimization benchmarks, and reproduces the conformational sampling of both classical and existing machine-learned FFs, without relying on externally assigned nonbonded parameters.

Metal-organic frameworks (MOFs) are widely explored for gas separation, catalysis or energy storage, yet their integration with other electronically functional materials remains largely unexplored. Here, we integrate the layered Hofmann-type spin-crossover (SCO) MOF {FeII(pyS2Et)2[PtII(CN)4]} into electrical nanodevices to control the properties of two-dimensional (2D) quantum materials. Our molecular approach exploits the stimuli-responsive nature of SCO layered materials as switchable building blocks. We demonstrate selective modulation of the electronic transport in van der Waals heterostructures interfacing SCO with 2D quantum materials (few-layer graphene, magnetic CrSBr, superconducting NbSe2) through the strain induced via thermal and light-induced spin transitions. In graphene, the conductivity is selectively switched by light. In spin-valves based on CrSBr bilayers, the MOF triggers magnetic hysteresis (absent in pristine CrSBr) and enables tunable non-volatile zero-field memory. In NbSe2, light modulates the superconducting critical current and transition temperature. These results establish layered stimuli-responsive SCO MOFs as active molecular control elements for 2D quantum materials, providing a route to strain-mediated control of electronic, magnetic, and superconducting functionalities and extending MOF-based architectures beyond traditional porous-matter applications towards multifunctional electronics and spintronics.

Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subproblems that can, in principle, run concurrently. Their practical benefit on real quantum hardware, however, has remained unclear. We demonstrate parallel FMO calculations on three room-temperature diamond-based 'Quoll' quantum computers installed on-site at the Oak Ridge Leadership Computing Facility. Helium clusters (He$_{n}$, $n=2,4,8,10,14$) served as a testbed for total energy calculations using the Quantum Wave Function Sampler (QWFS), a selected configuration interaction (SCI) approach. We compare two execution modes: synchronous shot-parallel execution, in which quantum processors jointly sample QWFS circuits and combine their SPAM-corrected counts into a single probability distribution; and asynchronous fragment-parallel execution, in which processors independently evaluate FMO fragments using device-specific optimized circuits. Both modes increase quantum sampling throughput without systematic loss of accuracy. The three-QPU shot-parallel workflow reaches a parallel efficiency of $93.60\%$, while the fragment-parallel workflow achieves a measured efficiency of $75.01\%$, increasing to $95.17\%$ when excluding the non-useful idle time associated with non-interruptible submitted jobs. In both cases, the assembled FMO-QWFS energies remain chemically accurate compared to the classical ideal. These results provide a proof-of-concept demonstration that parallel quantum execution can improve the practical throughput of quantum chemistry workflows on prototype hardware, motivating the development of future HPC environments with many fault-tolerant quantum processors operating as a distributed resource for molecular and materials simulation.

Accurate prediction of continuous molecular properties supports chemical discovery and design. Many recent approaches combine multiple molecular representations within increasingly complex neural architectures, aiming to achieve higher accuracy. However, classical machine-learning models remain competitive when trained on chemically meaningful molecular representations. We therefore constructed a chemistry-guided classical branch from Morgan fingerprints, RDKit molecular descriptors, and conformer-derived geometric features selected to provide complementary views of molecular structure. We combined four tree learners trained on these representations with a CoMPT graph predictor and integrated their out-of-fold predictions using Ridge regression. Across ESOL, FreeSolv, and Lipophilicity datasets, the fused model achieved lower mean root-mean-square error (RMSE) than Chemprop and competitive graph and multimodal methods, including CoMPT, MoleSG, KFLM2, and MCMPP. After retraining on the separately curated AqSolDB dataset, the same approach, without redesign or retuning, reduced mean absolute error (MAE) relative to the classical ensemble and achieved lower mean MAE than Chemprop. These results show that accurate and transferable molecular property regression does not necessarily require a complex joint multimodal architecture: classical learning remains competitive when chemical insight guides the choice of molecular representations, and a graph model can add complementary information through simple prediction-level fusion.

Major advances in chemical rate theory changed what was computed and what a barrier meant. I organize this conceptual history around those changes, while retaining the controlled corrections and limiting relations between theories. The calculated quantities include an empirical rate constant, a collision count, a thermodynamic activation free energy, a statistical flux across a dividing surface, a stochastic escape rate, an energy-resolved microcanonical rate, a quantum flux correlation function, and an ensemble of reactive paths computed with electronic-structure or machine-learned potentials. I compare the assumptions with the limits stated by the pioneering authors. A rate constant is inferred from measurements under a stated rate law; its temperature dependence defines an apparent activation energy $E_a$. Activation free energy $\Delta G^{\ddagger}$, activation enthalpy $\Delta H^{\ddagger}$, activation entropy $\Delta S^{\ddagger}$, the prefactor, transmission coefficient $\kappa$, and saddle height require a kinetic model and, where relevant, a standard state, dividing surface, ensemble, and dynamical correction for their interpretation in a rate calculation. In hydrogen transfer by soybean lipoxygenase-1, similar single-isotope activation parameters accompany a large change in isotope-sensitive dynamics. Five rate benchmarks illustrate the errors different comparisons test and motivate seven reporting items. These distinctions guide comparisons of rates and interpretations of activation parameters.

We present a method to obtain the proven exact 2-component (X2C) method, normally applied to Gaussian-type fixed Hilbert bases, in an adaptative non-uniform grid basis of the Hilbert space, namely Multiwavelets. The X2C method manages to block-diagonalise the Dirac Hamiltonian restricted to the subspace of the basis, in an energy-independent manner. The method takes inspiration from the success of the atomic mean-field X2C method on the Gaussian bases to represent the coupling operator as a projector onto the small components of the eigenstates of the constituent atoms of the system.

We introduce a simple coupled cluster approach for multireference and open-shell systems. In the restricted active space coupled cluster framework, the cluster operator only includes excitations from doubly occupied to virtual orbitals, and this ensures the individual operators commute among each other. The ground and excited states are obtained by diagonalisation of the coupled cluster similarity transformed Hamiltonian in a restricted active space. We demonstrate the framework for the RAS-CCSD model, where single and double excitations are included in the cluster operator, and likewise for a complete active space. We consider systems with multireference character and open-shell systems in a spin-adapted manner. The approach captures significantly more dynamical correlation than without the coupled cluster similarity transformation.

Nonlinear spectroscopy is widely used to probe ultrafast dynamics in matter. Signals of higher nonlinear orders encode valuable many-body dynamics but only recently have become individually retrievable. However, their physical interpretation in terms of double-sided Feynman diagrams is cumbersome and non-intuitive. Here, we establish an alternative framework that connects them to the underlying excitation conditions and many-body interactions. This approach predicts two classes of dynamic nonlinearities, due either to direct many-body interactions or to saturable kinetic channels. The method provides a direct physical interpretation of the retrieved signals, explaining their sign, magnitude, and dynamics. It also enables model-free reconstruction of the dynamics of a sub-ensemble of particles starting with a given number of excitons, which we demonstrate to be especially insightful. We validate the framework using colloidal CdSe/CdS core/shell quantum dots, which enable us to model nonlinearities of both classes in isolation and also combined.

An old but recurring question in ground-state density functional theory, relevant to the construction of density functional approximations, is the existence of a second-order gradient expansion for the exchange energy that is exact for densities that vary slowly over space. Here we will argue that there is such an expansion, with a gradient coefficient 10/7 of Sham's, as suggested by theoretical/mathematical derivations and by numerical calculations on model slowly-varying metallic densities, although some standard derivations do not quite capture the correct gradient coefficient. We also discuss long-range anomalies in the gradient expansions for the exchange and correlation holes around an electron. It is plausible that, as suggested earlier and recently, standard derivations are still correct for the coefficient of the second-order gradient expansion for exchange and correlation together, in which case the correct gradient coefficient for correlation is $C_{\mathrm{c}}$ = $C_{\mathrm{xc}}$ - $C_{\mathrm{x}}$. We propose possible numerical evaluations of $C_{\mathrm{xc}}$ and $C_{\mathrm{x}}$ that are free from long-range anomalies, and discuss implications for density functional approximations. Axel Becke's 1988 generalized gradient approximation (the B88 GGA) for the exchange energy brought density functional theory into chemistry, with a gradient coefficient for exchange that is optimal at the GGA level for atoms and molecules but too large for solids. Meta-GGAs seem to work as well for molecules as for solids, but may not be perfected yet. The article concludes with a few memories that the first author has of discussions with Axel Becke.

Direct-write pattering, using chemical vapor deposition through a nozzle, enables localized film growth using high-purity precursors standard to the electronics industry. Yet, achieving sub 100 $\mu\mathrm{m}$ feature control has been limited by nozzle design and unquantified precursor surface diffusion. Here, we report a direct-write chemical vapor deposition platform that employs a 10 $\mu\mathrm{m}$ tip diameter glass micro-nozzle mounted on a scanning stage above a heated substrate in ultra-high vacuum. Using this approach, we demonstrate the continuous single-pass writing of 500 $\mathrm{nm}$ thick metallic aluminum lines with a 30 $\mu\mathrm{m}$ linewidth on titanium nitride-coated silicon. This capability enables maskless, localized growth under systematically varied deposition conditions on a single substrate. To analyze the resulting deposit profiles, we introduce a transport framework coupling Knudsen gas emission from the micro-nozzle with surface diffusion to extract the effective precursor diffusion length, $L_d$. $L_d$ captures the combined impact of precursor surface mobility, residence time, substrate temperature ($165^{\circ}\mathrm{C}$ to $205^{\circ}\mathrm{C}$), growth duration, and nozzle height, contracting from 17.4 $\mu\mathrm{m}$ down to 3.2 $\mu\mathrm{m}$ at elevated temperatures. These results establish $L_d$ as a quantitative parameter for evaluating laterally confined chemical vapor growth and predicting feature resolution limits. This approach provides a practical methodology to measure and quantify precursor surface transport in localized film synthesis.

Geometry optimization is a major cost in many quantum-chemical workflows: each optimization step requires one force evaluation, and at the density-functional level that evaluation dominates the wall time. Research in this area has produced a broad range of optimization methods, and we ask whether a language model can improve on the best of them through autoresearch. An agent rewrites the optimizer itself to minimize force-call counts, restrained by two admission gates that reject premature stopping and improvements that do not generalize to unseen molecules. Starting from Sella, the fastest open-source optimizer available, the search produces AutoSella, a family of two optimizers. Both of them deliver consistent force-call reductions relative to Sella across held-out molecular benchmarks and potentials not used during the search. Most notably, at the r2SCAN-3c DFT level, the best variant requires only 40.2-77.2% of Sella's force calls while achieving the same energy reduction, even though agent used no DFT gradients.

We present an exact self-consistent two-component reformulation of the Dirac equation in the Weyl representation. By exploiting the diagonal structure of the kinetic operator and the purely scalar mass coupling in the Weyl basis, the four-component Dirac problem is reduced to an exact two-component form without introducing inverse-potential operators that arise in conventional energy-decoupling approaches. The resulting second-order differential equations are cast into an integral formulation and solved iteratively via convolution with the Helmholtz Green's function. We implement this formalism within an adaptive multiwavelet basis framework, providing rigorous, user-defined error control. Proof-of-concept calculations for simple atomic systems demonstrate the numerical stability, accuracy, and efficiency of the proposed algorithm.

Compact, reliable, and cost-effective gas sensors have become a highly demanding subject for safety management in the medical sector, chemical manufacturing, food quality monitoring, agriculture, and industrial safety. Hazardous gas emissions need to be controlled and monitored with fast-responsive and highly sensitive sensing devices. A first-principles study employing density functional theory (DFT) was used to investigate the adsorption behavior of Cl2, CO, CO2, NO, NO2, and HCN gas molecules with our proposed alloys, which consisted of hexagonal boron nitride (hBN) and graphene (Gr). The alloy consisting of 22% carbon (BNGr-2) was found to be most competent for sensing Cl2, CO, CO2, and HCN with sufficient adsorption energy, charge transfer, and bandgap alteration. However, NO and NO2 gas molecules showed more engagement with 33% carbon-proportioned alloy (BNGr-3) in terms of adequate gas sensing properties. NOx gases exhibited the most chemiresistive sensitivity towards the adsorbents. Other gases also showed significant chemiresistive sensitivity and distinct selectivity ratios, which would facilitate these alloys as chemiresistive sensors. Besides, noticeable work function variation (~20%) of these systems manifested potential as work function based sensors. Cl2 and NO2 showed strong physical adsorption, while the rest of the gases were weakly to moderately physisorbed, resulting in very short recovery times (10-1 ~ 10-6 seconds). Additionally, the distinctive absorption spectra observed for the gas analyte systems highlighted the potential of the proposed alloys as optical gas sensors. Temperature variation revealed that all gas molecules can be freed from the adsorbent BNGr-2 at 425 K. These findings imply hBN-Gr alloys as promising gas sensors for pollution auditing.

The dynamics of chemical reaction systems with two chemical species can be described (under mass-action kinetics) by planar autonomous systems of ordinary differential equations (ODEs) with right-hand sides containing polynomials. While their behaviour close to hyperbolic critical points is well understood, their dynamics has not been fully characterized for strongly non-hyperbolic critical points, where the linearization close to the equilibrium is given by the zero matrix. In this paper, the behaviour of such chemical reaction systems is investigated. Considering second-order chemical reaction networks with a positive equilibrium point, we show that only nine geometric equivalence classes are possible out of the sixteen classes for general ODE systems. Examples of chemical reaction networks in each class are presented. All sixteen geometric equivalence classes can be realized by chemical systems if the non-hyperbolic equilibrium point is located at the origin.

The identification of plastic inorganic semiconductors becomes challenging when their mechanical responses depend on crystallographic orientation, sample size, and loading conditions. Using layered GeSe as a model system, we examine its deformation behavior through macroscopic compression, bending, conventional micropillar compression, and eccentric micropillar compression. Under macroscopic compression perpendicular to the layers, GeSe sustains approximately 23% strain without fracture, whereas bending along the c-axis armchair direction produces brittle cleavage fracture. Conventional micropillar compression results in brittle fragmentation, while eccentric loading introduces a shear component that activates pronounced interlayer sliding and accommodates deformation. These contrasting responses reflect the coupled effects of bonding topology, interlayer van der Waals interactions, defect density, stress constraints, and strain path on the competition between sliding and fracture. The results highlight the limitations of identifying plasticity from a single direction, scale, or test and support a systematic evaluation framework combining multiple directions, length scales, loading modes, and characterization techniques. More rigorous and unified criteria are needed to guide reliable materials selection for flexible electronics and devices integrated on curved surfaces.

The variational calculation of rovibrational spectra without model Hamiltonians remains challenging, with the size and composition of the vibrational basis constituting a major bottleneck. Preceding vibrational configuration interaction (VCI) calculations reduce this basis, but leave the question of which vibrational state functions to include. In close analogy to configuration-selective VCI theory, we propose a basis-pruning algorithm in which candidate vibrational states are represented by inexpensive approximate wavefunctions and selected according to their impact on rovibrational energies in a spectral window of interest. For formaldehyde, comparisons with widely used polyad schemes controlling the vibrational basis are provided. Calculations on thioketene benchmark the accuracy and efficiency of the pruning approach for different threshold values.

Cavity Quantum Electrodynamics (Cavity-QED) offers a powerful framework for controlling light-matter interactions, yet first-principles approaches commonly assume ideal, lossless cavity fields. Here, we introduce a minimal extension to the Pauli-Fierz Hamiltonian that accounts for cavity dissipation through the experimentally accessible cavity quality factor Q. The electromagnetic field is described as a damped harmonic oscillator within the very weak damping approximation. In this framework, cavity losses only enter the light-matter interaction and dipole self-energy terms, without introducing additional photonic degrees of freedom. A time-dependent unitary transformation yields a real-valued Hermitian Hamiltonian that can be incorporated into existing ab-initio QED methods without additional computational cost or changes in scaling. We combined the approach to the QED-CCSD method and investigated the effects of cavity dissipation on the optical spectrum of a p-nitroaniline molecule under strong light-matter coupling. We found that, for realistic quality factors, cavity dissipation can reduce the Rabi splitting by more than 20% compared to the ideal-cavity limit, even though the system remains in the strong-coupling regime. Convergence towards the ideal-cavity limit then requires substantially higher quality factors than those needed to establish strong coupling. These results demonstrate that cavity dissipation can significantly affect polaritonic energetics and highlight the importance of incorporating experimentally realistic cavity losses into quantitative ab-initio Cavity-QED simulations to accurately guide experimental protocols.

Modelling molecular absorption spectra from scalable ground-state representations is challenging because conventional approaches often require explicit excited states or time propagation. Here, we introduce a method to obtain Absorption Using Recursive Operator Response with vibronic Augmentation (Aurora), which combines neural quantum states (NQSs) with continued-fraction memory-kernel coupling theory (CF-MKCT). Aurora maps the zero-temperature Liouvillian response hierarchy onto repeated wavefunction-space applications of a shifted Hamiltonian to a dipole-prepared state, enabling direct spectral reconstruction from variational ground states. The implementation combines variational Monte Carlo, implicit-restarted-Lanczos-assisted NQS optimization, matrix-free Hamiltonian application, and adaptive recursion control. The resulting electronic spectra closely agree with full-configuration-interaction and conventional MKCT references for tractable molecules, with the restarted-Lanczos strategy improving difficult NQS cases. For electron-phonon Hamiltonians, Aurora combines Huang-Rhys mode selection with local singular-value-decomposition (SVD) phonon encoders. The H2 benchmark indicates that compression retains the dominant vibronic envelope and improves upon bare local-basis truncation, while paired calculations for N2, H2O, H2S, and NH3 yield similar principal features with and without encoding. We further obtain vibronic spectra for C6H6 and a 10-electron, 10-orbital C10H8 active space. Overall, Aurora provides a unified route from variational molecular ground states to electronic and vibronic spectra without constructing excited-state manifolds.

Dielectric screening of electric fields is modified by interfaces and confinement, and any accurate description of interfacial chemistry necessitates properly modeling the nonuniform dielectric tensor. However, models with short range interactions, such as local machine learned interatomic potentials (MLIPs), are increasingly used in simulations of molecular interfaces without quantitative understanding of their nonuniform dielectric response. To build this understanding, we use the framework provided by local molecular field (LMF) theory to quantify the roles of short and long range electrostatics in confined water. Short and long range models predict the same transverse dielectric profile but short range models predict qualitatively different longitudinal dielectric profiles than their long range counterparts. To understand these differences, we develop a Landau theory that shows that long range interactions stiffen polarization fluctuations by a factor of the bulk dielectric constant and reduce the interfacial polarization correlation length by an order of magnitude. LMF theory, which captures long range interactions with an averaged field, can correct the structure and dielectric response, but the standard fluctuation relation no longer holds. We derive a fluctuation relation and integral equation for LMF theory and demonstrate their accuracy for predicting longitudinal dielectric response. MLIPs that incorporate interfacial configurations in their training essentially learn a static mean field correction at the interface, such that the integral equation can be used to predict their nonuniform dielectric profile from polarization fluctuations. We also show that short range MLIPs can be combined with symmetry preserving mean field (SPMF) theory to provide a linear-scaling alternative for long range interactions and predicting nonuniform dielectric functions.