Papers

Singlet fission, the conversion of one singlet exciton into two triplet excitons, has the potential to revolutionize solar energy production by boosting the efficiency of solar cells beyond the Shockley-Queisser limit. However, the practical realization of singlet fission remains an outstanding challenge, and its underlying mechanisms have long been debated. Here we show that singlet fission arises from a distinctive type of collective excitonic entanglement in which the excitons condense into a single particle-hole mode, the same principle that leads to the Bose-Einstein condensation of excitons. Quantitatively, this condensation appears as a large eigenvalue of the particle-hole reduced density matrix (RDM), representing multiple excitons occupying a collective mode. We utilize the particle-hole RDM to capture the intrinsic particle-hole correlations in singlet fission materials including acene crystals and covalently linked pentacene dimers, revealing how structural changes control singlet fission mechanisms and modulate the contributions of charge transfer states. The same characteristic enhancement of excitonic population in a single mode is common to the onset of exciton condensation and the mechanism of photosynthetic energy transfer. Together, our results establish a unifying framework for understanding the origins of singlet fission in terms of collective excitonic entanglement.

The dependence of ion pairing on osmotic stress may strongly affect the performance of ionic materials and membranes whose interior is often osmotically stresses, yet quantitative understanding of this dependence and, specifically, the effects of water and ion activities is limited. Motivated by this gap, we analyze the enhancement of ion pairing with osmotic pressure for concentrated aqueous KCl, NaCl, and LiCl solutions using molecular dynamics simulations. Based on rigorous thermodynamic relations, we separate the contributions of ion non-ideality to the pairing constant, varying with osmotic pressure, from other effects including water release and type of ion-pair. Our analysis reveals that ion non-ideality indirectly generates a stronger effect on pairing than the direct one of water release. However, its effect is moderated and may even be reversed for more hydrated pairs by a similarly large and opposite effect of ion-pair non-ideality assigned to varying dielectric properties of the solution and water restructuring upon pairing. The interplay between these contributions, including large and pair type-specific hydration effects on the cost of pairing, explains the observed opposing trends: pairing decreases with osmotic pressure for more hydrated solvent-separated pair types while increasing for contact pairs. The trend becomes more pronounced for more hydrated smaller cations, but was fairly independent of the water model used. The results further suggest that dielectric effects enhanced in ionic materials-and, as a result, larger variations of ion-pair non-ideality, compared with aqueous solutions, should have a more significant impact on pairing than water release.

We consider a new methodology for modelling the reaction-diffusion systems based on systems of ordinary differential equations. In contrary to the specialised numerical methods like straight line method, this new methodology is positioned as a pure alternative at the model level for partial differential equations. In its description, the new method is largely similar to the finite volume method, but unlike the latter, it uses statistical simplifications and principles of geometric probability to describe diffusion. The main objectives of this approach are to simplify the qualitative analysis of reaction-diffusion systems and to improve the efficiency of numerical model implementation. The first objective is successfully addressed, as it becomes possible to use the apparatus of classical dynamical systems theory for a qualitative analysis of model dynamics based on the systems of ordinary differential equations. The second objective is only partially addressed, as the gain in efficiency while maintaining acceptable accuracy for numerical implementation will be significant only for certain simple initial distribution of molecules and for specific diffusion coefficients. Furthermore, to formulate criteria for practical applicability, we separately evaluate the modelling error using this new methodology.

Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate atomistic simulation. Classical density functional theory offers a reusable variational description, but its central excess free-energy functional is generally unknown, and learned approximations have largely remained restricted to planar or lower-dimensional settings. Here we show that this functional can be learned directly from fully three-dimensional equilibrium density fields while preserving spatial symmetry and variational consistency, without free-energy or chemical-potential labels. A single learned functional transfers across temperatures, system sizes and statistical ensembles, and recovers structure factors, the equation of state, liquid–vapor coexistence and interfacial broadening, none of which are used as training targets. Applied to complex three-dimensional geometries, it predicts the non-monotonic force associated with formation and rupture of a solvent-depleted bridge between colloids and adsorption in an interconnected gyroid pore. These results demonstrate that equilibrium density data can be converted into a transferable thermodynamic generator connecting microscopic liquid structure to response, phase behavior and collective phenomena.

Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/molecular mechanics (MLIP/MM) schemes correct ligand strain, but under mechanical embedding still describe ligand–environment electrostatics with static point charges. Electrostatic embedding schemes coupling machine-learned charges to the MM environment have been proposed and validated against QM/MM for simple systems, but not tested in a production alchemical workflow. We take the electrostatic embedding scheme of Semelak et al.\ and evaluate it on protein–ligand RBFE. We trained a TensorNet2 model, AceFF-2-RESP-1, on $10^{6}$ conformations from the AceFF dataset, jointly predicting energies, forces and Restrained Electrostatic Potential (RESP) charges. We chose RESP over MBIS for commensurability with the AMBER-family force field it couples to. The predicted charges enter the short-range direct-space part of the particle mesh Ewald sum, with Thole damping to prevent polarization catastrophes during alchemical transformations. We tested the scheme across five targets from the Wang et al.\ benchmark set, fixed in advance by a prior study, with three replicates per edge and matched protocols. Electrostatic embedding improved every accuracy and correlation metric for TYK2 ($\Delta\Delta G$ RMSE $0.86 \rightarrow 0.45$~kcal/mol against GAFF2), but performed comparably to the classical and mechanical-embedding baselines for CDK2, thrombin, p38 and JNK1. Standard single-molecule energy and charge benchmarks were not good predictors of this target-dependent outcome. TYK2 combined good $\Delta\Delta G$ accuracy with the lowest force error on the Schrödinger benchmark, but this pattern did not hold for the other targets.

Energy and charge transfer in condensed matter systems are often described in terms of quasiparticles. The order of accuracy of the results in terms of the electron-to-nucleus mass ratio $\epsilon$, an adiabatic small parameter, is usually not known. To quantify energy transfer with controlled accuracy in $\epsilon$, we use adiabatic perturbation theory to derive a formula for the rate of change of the nuclear kinetic energy. Applying a sequence of near-identity unitary transformations decouples the electronic and nuclear degrees of freedom to higher and higher order and produces an effective Hamiltonian. In the special case that the nuclei are treated classically, the energy transfer formula reduces to the usual formula for the rate of work done on a classical particle but the mass of the particle is enhanced by the factor $M_0^{-1}(M_0+\epsilon M_1)$, where $M_1$ is a nonadiabatic correction to the bare nuclear mass tensor $M_0$. Additional quantum geometric corrections appear at higher orders. Adiabatic perturbation theory is further used to incorporate, order-by-order in $\epsilon$, nonadiabatic transitions into linear response calculations. The nonadiabatic frequency-dependent conductivity and Drude weight of an electron-ion system are calculated.

Time-resolved infrared spectroscopy probes protein dynamics over timescales spanning more than ten orders of magnitude, yet the molecular motions underlying the observed kinetic signatures have remained elusive. Here we combine transient infrared spectroscopy with nonequilibrium molecular dynamics simulations to establish a direct connection between experimental relaxation times and local structural motions. Studying single-domain allosteric proteins, we find that inter-residue contact distances provide the structural representation that most faithfully reproduces the experimental dynamics. Correlation analysis identifies localized networks of coordinated contacts that mediate communication between secondary-structure elements. The characteristic timescales of these contact networks quantitatively match the experimentally observed relaxation processes, enabling each kinetic step to be assigned to a specific molecular motion. Applied to allosteric signal propagation in PDZ3 and photoinduced ligand unbinding in PDZ2, this framework provides an atomistic picture of hierarchical protein relaxation and establishes a general framework for connecting transient infrared spectroscopy with the molecular mechanisms of protein dynamics.

Metal surfaces undergo structural, compositional, and morphological changes in response to their chemical environment. Tuning the surfaces' function and stability for a given application correspondingly necessitates an understanding of how this surface evolution couples to external conditions. Here, we demonstrate the feasibility of nested sampling simulations to obtain this coupling at first-principles predictive quality. By exploring the full configuration space, nested sampling estimates the partition function and gives direct access to desired thermodynamic ensemble averages at any temperature without prior knowledge. Computational feasibility is achieved through machine-learned interatomic potentials, an efficient GPU implementation of the sampling algorithm and bespoke sampling moves. Applied to the early oxidation of Cu(100), the approach successfully predicts the experimentally observed, complex $(2\sqrt{2}\times\sqrt{2})$R45$^\circ$-O missing-row reconstruction. The full access to the partition function enables a detailed characterization of the temperature-dependent surface evolution, mapping the emergence of defect states and the order-disorder transition of the reconstructed surface.

Accurate solvation free energies from molecular dynamics simulations require efficient sampling of coupled slow variables, including solvent coordinates, solute conformational modes, and the alchemical coordinate $\lambda$. Here, we develop a $\lambda$-dynamics framework that combines mass scaling, on-the-fly probability enhanced sampling (OPES), and driven adiabatic free energy dynamics (d-AFED) to address these sampling challenges within a unified protocol. For rigid organic solutes, Hamiltonian replica exchange with mass scaling is first used to quantify the effect of octanol solvent relaxation. Reducing all octanol atomic masses by a factor of ten accelerates convergence by more than fivefold while preserving equilibrium solvation free energies. These calculations then provide reference benchmarks for $\lambda$-OPES, a dual-bias $\lambda$-dynamics strategy that combines the "standard" and "explore" variants of OPES to promote transitions along the alchemical coordinate. This approach reaches convergence on timescales comparable to replica exchange, but without predefined $\lambda$ windows or multiple parallel simulations. For flexible $N$-acetyl amino-acid amide solutes, $\lambda$-OPES is coupled with d-AFED on selected backbone and side-chain dihedrals to enable simultaneous alchemical and conformational enhanced sampling. This combined strategy improves agreement with experimental octanol-water partition coefficients and reduces the mean absolute error from 0.75 log units with $\lambda$-OPES alone to 0.30 log units with $\lambda$-OPES-d-AFED. Overall, this work establishes an integrated enhanced sampling protocol for solvation free energy calculations across rigid organic solutes and flexible peptide-like solutes, and provides a foundation for the application of alchemical free energy methods to larger and more conformationally complex systems.

Classical PET glycolysis struggles with industrial scale-up due to a research paradigm defect: uncoupled reaction optimization that ignores system-level engineering entropy. By establishing a governing reference frame, Scientific Reasoning Units (SRUs) and human-AI collaborative discovery in this article, we present a Three-Step Synergistic Reinforcement Paradigm addressing the triple failure modes of traditional systems-mass-transfer collapse, viscosity deadlock, and side-reaction accumulation. By integrating twin-screw controlled shearing, solid-state micro-homogenization, and deep depolymerization (2:1 EG/Oligomer ratio), we achieve an ultrafast kinetic regime. BHET yields surge to 80% to 95% within 10 minutes, eliminating multi-hour thermal bottlenecks and delivering an economically viable framework for closed-loop polyester recycling.

Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge. Despite decades of computational efforts, no existing system achieved reliable reasoning over unseen spectra. Here, we propose MACROS, a multi-agent system automating structure elucidation by emulating expert iterative hypothesis-testing. Trained on 100M simulated and 1.6M experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques. It achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental chemical principles rather than memorizing database patterns. MACROS augments chemists via collaboration to deliver sixfold faster, 40% more accurate elucidation. MACROS establishes a scalable foundation for fully automated structure elucidation, and catalyzes accelerated molecular discovery toward autonomous laboratories.

Electronic excitation induced by nuclear motion is a key energy dissipation channel in chemical dynamics at metal surfaces. Here, nonadiabatic effects can be treated via molecular dynamics with electronic friction, where they act as frictional drag and fluctuation force contributions. Commonly, the Markov approximation is imposed, so memory effects are ignored. A theoretical formalism is presented to evaluate tensorial and configuration-dependent electronic friction memory kernels from first principles. We evaluate friction kernels for Newns–Anderson Hamiltonian models as well as within Kohn–Sham density functional theory and analyse their mathematical properties and configuration dependence. For hyperthermal atomic and diatomic scattering, memory effects arising from frequency and configuration dependence of electronic friction affect energy exchange between adsorbate and metal electrons. Memory effects lead to an increase of vibrational and a reduction of translational energy loss in the case of nitric oxide scattering on Au(111), leading to an increase of directional anisotropy of friction. Importantly, memory-dependent evaluation of electronic friction removes the need to define a single effective Markovian friction coefficient from the structured frequency-dependent electronic response.

The coupled dynamics of ions and water molecules in their first hydration shell impact a variety of processes including ion diffusion, selective ion transport in water-filled nanopores, and the kinetics of ion-pairing, ion adsorption, and metal-ligand binding reactions. In this work, we study these coupled dynamics for alkali metals (Li, Na, K, Rb, Cs), alkaline Earth metals (Mg, Ca, Sr, Ba), and chloride through the lens of their dependence on ion isotopic mass. Results are validated against previous measurements of the isotopic mass-dependence of ion diffusion coefficients in water and previous ab initio calculations of ion high-frequency dynamics in water. We find that the vibrational power spectra of ions in water consistently exhibit either two or three peaks, i.e., ions have several rattling frequencies within their solvations shells as previously reported for a subset of the species examined here. These frequencies have different sensititivies to isotopic mass that may serve as signatures of ion solvation processes (such as the tendency of ions to orient their first-shell water molecules) and that also may relate to Hofmeister-like effects including the relative affinity of different metals for ribonucleic acid (RNA).

Quantum-selected configuration interaction (QSCI) methods use a quantum computer to identify dominant electronic configurations in the molecular ground state, while a classical computer diagonalizes the Hamiltonian within the reduced subspace spanned by those configurations. Sample-based quantum diagonalization (SQD), a leading QSCI approach, uses iterative classical post-processing to correct noisy quantum measurement to ensure that the corresponding configurations remain physically sensible. In this work, we show that SQD performance can be strongly influenced by uncontrolled growth of the classical diagonalization subspace. When classical resources are not explicitly constrained, classical uniform random sampling can reproduce SQD benchmarks as noise increases the diversity of sampled configurations. We show any fair benchmarking protocol of SQD must explicitly control diagonalization size over unique samples. We then address the problem of efficiently discovering physically relevant, energy-lowering configurations by introducing a measurement protocol based on non-orthogonal configuration interaction (NOCI). By distributing measurements across orbital bases optimized with respect to the molecular Hamiltonian, we obtain improved sample efficiency relative to measurements performed solely in the Hartree–Fock basis. Importantly, these improvements persist even under fixed classical resource budgets, demonstrating that the resulting configurations are of higher quality rather than being more numerous. Under our proposed benchmarking procedure, we establish measurement-basis engineering as a promising route to improving quantum sampling methods for electronic structure.

Predicting the products of ionic-liquid impacts on extractor surfaces is important for electrospray-thruster lifetime analysis, yet available atomistic methods require a compromise between chemical fidelity and computational cost. Reactive force fields enable high-throughput sampling but do not explicitly resolve electronic charge redistribution and may miss relevant reaction pathways during impact, whereas mixed quantum–classical density-functional-theory molecular dynamics (DFT/MD) can capture charge redistribution and neutral-product formation at substantially higher computational cost. Pretrained atomistic foundation models have recently emerged as a potential route toward DFT-like chemical fidelity at considerably lower cost. Here, we benchmark two pretrained machine-learning interatomic potentials, MACE-MP-0 (medium) and MACE-POLAR-1, against DFT/MD and ReaxFF for geometry optimization of 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF$_4$) and for 10-100 eV impacts on a model Au extractor surface. The models reproduce several collision outcomes observed in DFT/MD, including ionic dissociation, high-energy covalent fragmentation, and, in particular, HF formation through neutralization-like chemistry that is not captured in the ReaxFF simulations. In the computational-performance benchmark, MACE-POLAR-1 and MACE-MP-0 (medium) completed each 2~ps trajectory in 5.12 and 2.54~min, respectively, corresponding to wall times approximately four orders of magnitude shorter than the DFT/MD reference under the reported benchmark conditions. These results support pretrained machine-learning potentials as a practical intermediate-cost approach for chemically resolved electrospray-impact simulations and motivate targeted fine-tuning with DFT data for broader applications in electrospray-thruster and electric-propulsion modeling.

We present a four-component multireference second-order perturbation theory (4C-MRPT2) within the small tensor product distributed active space (STP-DAS) framework. The formulation is compatible with the Dirac-Coulomb (DC), Dirac-Coulomb-Gaunt (DCG), and Dirac-Coulomb-Breit (DCB) Hamiltonians, and inherits the memory-efficient and massively parallel STP-DAS algorithm, enabling perturbative treatments over very large external spaces. Benchmark calculations on noble-gas and group-13 atoms demonstrate that 4C-MRPT2 efficiently recovers all-electron dynamic correlation while providing new insight into relativistic correlation effects. The calculations show that the Breit contribution to the correlation energy increases rapidly with atomic number and reaches approximately 4% of the total correlation energy for Xe. The method also shows that second-order correlation energies are only weakly dependent on the choice of multiconfigurational reference orbitals, and that frozen-core and frozen-virtual approximations substantially reduce computational cost with minimal loss of accuracy.

We trace the history of phase space electronic structure theory (PSEST), high- lighting how this powerful approach emerged from fundamental questions in semi- classical surface hopping dynamics and evolved into an alternative to standard Born- Oppenheimer based electronic structure theory (with moving instead of frozen nuclei). Our goal herein is not to recapitulate the mathematical details of phase space electronic structure calculations, and few equations are presented so as to maximize readability. Instead, our goal is to provide intuition for those new to the field both (i) regarding the physics present when solving the Schrodinger equation in a non-inertial frame as well as (ii) regarding why PSEST is a necessary step forward towards understanding chemical problems involving spin (with limited practical alternatives). We further high- light some of the many open questions in this fast developing area, which will hopefully inspire new practitioners in this field. This intuitive perspective lacks many equations and is meant to complement (rather than replace) the more technical review given in Bian et al, Chem. Phys. Rev. 7, 011303 (2026)

We present a second-derivative-corrected extension of the Flexible Ansatz for N-body Perturbation Theory (FANPT) for solving nonlinear Flexible Ansatz for N-body Configuration Interaction (FANCI) wavefunction equations. The original quasilinear FANPT approximation neglects second- and higher-order derivatives of the determinant overlap with respect to wavefunction parameters. In this work, we retain the overlap Hessian and neglect only third- and higher-order parameter derivatives, thereby including the leading nonlinear response of the wavefunction ansatz while preserving the same response-matrix structure used in the original FANPT formulation. The resulting additional terms enter only through the constant vector of the response equations and are implemented for coupled-cluster wavefunctions in the FanPy/FANCI framework. The new approximation is tested on the Lithium Hydride molecule and the insertion of Beryllium into H$_2$ using seniority-restricted coupled-cluster wavefunctions in the STO-6G basis. The main advantage of the second-derivative correction is that it provides a better initial guess for solving the projected FANCI equations at the next point along the adiabatic connection. This improvement is diagnosed by comparing the FANPT-propagated parameters with the independently optimized FANCI parameters at the same value of $\lambda$. For nonlinear coupled-cluster ansatzes, especially when larger $\lambda$-steps are used, the corrected approximation reduces the same-$\lambda$ parameter deviations and suppresses large parameter-space excursions. These results show that the leading nonlinear overlap correction improves the reliability of FANPT as a continuation strategy for nonlinear wavefunction equations.

In the thermodynamic limit, the equilibrium state of a many-body system can be characterized by three pairs of conjugate thermodynamic variables: $E/T,V/P,N/\mu$. In this limit, the thermodynamic properties in all ensembles are equivalent up to the leading order of $E,V,N$. However, for systems of finite size, this ensemble equivalence is no longer exact, and the thermodynamic properties may differ substantially among ensembles. To quantify these finite-size effects rigorously, it is desirable to develop a universal ensemble theory applicable to systems of arbitrary size, providing exact expressions for entropy and, thereby, giving rise to the precise value of all equilibrium thermodynamic quantities. In this work, we propose a theory that determines the exact entropy expressions for classical non-interacting many-body systems of arbitrary size across all statistical ensembles, based on only two postulates: stationarity, requiring that the physical laws be invariant under time translation, and unbiasedness, requiring that the equilibrium mixed state maximize the entropy subject to the prescribed constraints. Moreover, we show that the entropy expressions obtained in different ensembles converge to the common asymptotic form $S \asymp \ln\!\left( \left( \frac{4\pi m e E}{3N} \right)^{3N/2} \cdot \left(\frac{V}{N}\right)^N \right)+N$, consistent with the predictions of the large deviation theory.

We identify the spin rotatory strength—a chirality-odd, finite-frequency spin–dipole cross response—as the equilibrium observable for chiral-induced spin selectivity. Time-reversal symmetry forces the static response to vanish, while nonzero SU(2) Wilson-loop flux is an independent necessary condition. Exact diagonalization of a Kane–Mele–Hubbard model ($N{=}4,6,8$) reveals that gapped molecules robustly suppress the response, whereas near-degenerate systems show amplification at small $N$ whose persistence at larger sizes remains open.

We introduce a new singular-value-decomposition-based scheme for constructing small virtual spaces out of large basis sets for transcorrelated (TC) calculations, termed SVD-TC. This work builds on the recent finding that the residual basis error in the TC reference energy converges more slowly than that of the correlation energy. Within the new workflow, the post Hartree-Fock TC calculation is performed in a compressed virtual orbital subspace, obtained by projecting the canonical virtual orbitals from a large basis set onto a smaller basis set through singular value decomposition (SVD). This allows us to achieve the high accuracy allowed by the large basis, whilst the bottleneck steps - TC integral calculation and post-HF correlation method such as CCSD(T) - incur the cost of only a small virtual space calculation. The method therefore is highly efficient, whilst avoiding the composite nature of the reference correction method. Using the new scheme, we widen the scope of benchmark-quality TC results into more complex molecules than previously considered: using the G2-1 set of 55 molecules with first- and second-row atoms, we apply SVD-xTC-CCSD(T) to compute atomization energies. We compare our results against the near-exact semistochastic heat-bath configuration interaction (SHCI) reference values and experiment. We find that SVD-xTC-CCSD(T) delivers chemical accuracy already with triple-$\zeta$ basis sets. Finally, we use the quadruple-$\zeta$ results to analyze the accuracy of pseudopotentials within the TC method, and show that pseudopotential TC workflow provides faster basis-set convergence than all-electron TC. We also present timings for computing the atomization energies on G2-1 set, demonstrating the efficiency of our TC workflows.

We report absolute frequency measurements of the CO fundamental band, together with high-precision Ar-induced collisional line-shape parameters using comb-based Fourier-transform spectroscopy. High-quality spectra were obtained with a stable mid-IR optical frequency comb source and careful suppression of technical noise. By applying analysis methods that fully exploit the advantages of optical frequency comb spectroscopy, precise frequency calibration and robust multi-line fitting were achieved, resulting line-center fitting errors below 0.00001 cm$^{-1}$ for most transitions. The measured transition frequencies agree with HITRAN within 0.0001 cm$^{-1}$, supporting the reliability of the semi-empirical database. Multi-line fits using a speed-dependent Voigt profile yield collisional parameters for approximately 40 P- and R-branch lines, providing a reliable experimental benchmark for CO-Ar line-shape modeling.

Hyperpolarization techniques enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI), enabling detection of low concentration metabolites and dynamic processes. Signal Amplification by Reversible Exchange (SABRE) transfers spin polarization from parahydrogen without permanent chemical modification of the substrate. Here, we demonstrate SABRE-mediated 13C hyperpolarization of unmodified amino acids using L-[1- 13C]-valine as the primary model system and provide preliminary evidence of applicability to glycine. Signal enhancements exceeding 60-fold were observed on a 1.1 T benchtop NMR spectrometer. The dependence of polarization on parahydrogen bubbling time, solvent composition, and substrate-to-catalyst ratio was investigated.

Time-resolved X-ray absorption spectroscopy (TR-XAS) offers an element- and site-specific probe of coupled electronic and nuclear dynamics, but its interpretation requires methods that can treat multiconfigurational excited states across nonequilibrium nuclear ensembles. Here, we report time-resolved simulations of the acetylacetone (AcAc) transient X-ray absorption spectra by combining surface-hopping dynamics from complete active space second-order perturbation theory with multireference algebraic diagrammatic construction (MR-ADC). The simulated 20-200 fs spectra show a good agreement with experimental measurements and reveal that the TR-XAS response is governed by continuously evolving distributions of molecular geometries on the singlet potential energy surfaces (S2 and S1). In particular, absorption at 279.5-281.5 eV is enhanced for transient, nearly symmetric proton-sharing configurations, whereas the 284-286 eV profile reflects geometry-dependent C1s excitations modulated by proton transfer, bond alternation, and ring opening. For the long-time T_1 spectrum (7-10 ps), the principal features near 281.4 and 283.8 eV are assigned to central C 1s excitations into low-lying triplet pi-orbitals. Together, our simulations provide a more complete mechanistic picture of AcAc photorelaxation by directly linking ultrafast carbon K-edge signals to proton transfer, skeletal reorganization, internal conversion, and triplet formation.

Charged colloids coated with a polymer brush can be designed to preferentially self-assemble into different crystal structures by varying easy-to-tune experimental conditions. For a given set of conditions, we have observed in experiments and simulations a distribution of thermodynamically (meta)stable self-assembled crystal structures. Properly quantifying the free energy landscape of these colloidal systems is essential for rationally choosing conditions to preferentially target particular crystal structures. For some of the structures we have formed, standard crystalline order parameters are not able to differentiate between crystals or between crystals and amorphous aggregates. We show that local environment similarity descriptors are able to distinguish the relevant metastable states, but are too expensive for use in biased MD simulations. Here, we adopt an approach from machine-learned interaction potentials showing that SE(3)-equivariant transformer networks can serve as an efficient-to-evaluate machine-learned proxy. As a result, we can compute the relative free energies of accessible colloidal structures as a function of different experimentally-relevant physical knobs that can steer our system between two observed crystal types. As an example application, we then show how changing surface potentials of positive and negative colloids while maintaining the same attractive energy can shift which crystal structure is favored.

Simulating rare events in photochemistry by brute-force surface hopping is computationally prohibitive: most propagated trajectories either remain nonreactive or follow dominant relaxation channels, while the reaction of interest may occur with very low probability. Path sampling methods have long addressed this problem for ground-state dynamics, but their extension to nonadiabatic processes has so far been limited to low-dimensional analytical models. Here, we demonstrate that our recently developed nonadiabatic forward-flux sampling (NAFFS) method can be applied to efficiently simulate rare-events in full-dimensional molecular systems. As a representative benchmark, we investigate the rare molecular photodissociation channel of gas-phase ammonia, NH$_3$ + h$\nu \rightarrow$ NH + H$_2$. NAFFS samples reactive trajectories with the correct statistical weight, reproduces the reaction rate constant obtained in previous brute-force dynamical studies, provides direct access to mechanistic information through an averaged committor analysis, and reduces the simulation time per transition trajectory by up to three orders of magnitude. These results establish NAFFS as an efficient and statistically rigorous framework for investigating rare nonadiabatic processes in realistic molecular systems.

Organic photovoltaic (OPV) materials are promising candidates for distributed solar energy in tropical regions, yet existing virtual screening tools report static power conversion efficiency (PCE) values at standard testing conditions (STC) that fail to capture the temperature-driven performance degradation experienced under real deployment conditions. Here we introduce a Climate-Native computational framework that forecasts the annual PCE profile of OPV donor molecules under geographically realistic operating conditions. The framework combines GFN2-xTB molecular dynamics with an equivariant graph neural network surrogate ($268$ Neyman-stratified CEP molecules; $120,600$ training geometries; $\sim 1050\times$ speedup over explicit quantum chemistry) and sequential deep learning models trained on annual time series anchored in NASA POWER climate data for Douala, Cameroon, and validated by zero-shot transfer to Yaoundé and Maroua. Applied to $\sim 30,000$ molecules from the Harvard Clean Energy Project (CEP) and validated against $350$ HOPV15 experimental device measurements, the framework demonstrates that sequential models trained on full molecular dynamics trajectories outperform time-averaged baselines ($35\%$-$48\%$ relative MAE improvement over static baselines), confirming that thermal conformational dynamics carry information beyond mean geometry. We further introduce a seasonal stability score that reranks OPV candidates by performance consistency under tropical conditions, identifying molecules whose deployment suitability differs substantially from their static PCE ranking.

We present a real-time linear-response approach to solving the dynamical Bethe-Salpeter equation (BSE). The polarization part of the screened interaction is obtained from time-dependent Hartree propagation of the one-particle density matrix within an orbital basis-set representation. The frequency convolution defining the dynamical kernel is evaluated as a product in time, avoiding numerical integration and storage of the full screened Coulomb operator. The resulting nonlinear eigenvalue problem is then solved directly, going beyond the static screening approximation with full-frequency dependence in the screened interaction. While the deterministic scaling remains $\mathcal{O}(N^6)$, the time propagation formulation is readily compatible with grid-based stochastic sampling methods, which will open the possibility for dynamical BSE calculations of very large systems.

We present the development of self-consistent one-body double-hybrid (OBDH) density functional theory (DFT). In this approach, the one-body second-order Møller-Plesset (OBMP2) perturbation potential is embedded directly in the generalized Kohn-Sham framework, allowing orbitals to be optimized in the presence of MP2-level dynamic correlation. Unlike existing orbital-optimized double hybrids, OBDH requires neither the optimized effective potential nor perturbative orbital relaxation corrections. The energy functional combines a semilocal exchange-correlation functional, exact exchange, and OBMP2 correlation, from which the effective Hamiltonian and self-consistent-field equations are systematically derived. To reduce computational cost, projector-based embedding with concentric localization truncation is applied to the OBMP2 component, termed sub-OBDH. OBDH and sub-OBDH are benchmarked on diatomic potential energy curves, self-interaction errors, dihedral torsions of organic molecules, and interaction energies in non-covalent charged systems. Across all systems, OBDH consistently outperforms standard DFT, demonstrating its potential for accurate and practical electronic structure calculations.

Nuclear gradients and Hessians are fundamental quantities in computational chemistry, essential for a wide range of applications including geometry optimization, vibrational spectroscopy, and molecular property calculations. In this work, we present their analytical implementation on quantum hardware. The methodology is formulated within an active-space framework combining orbital optimization and linear-response theory. On the quantum-computing side, the approach employs the tiled unitary product state (tUPS) ansatz to directly evaluate the tensor elements required for solving the response equations. Moreover, the expectation values are corrected using an adapted confusion-matrix error-mitigation scheme in combination with post-selection criteria. The resulting workflow is assessed on molecular hydrogen and on water through the calculation of potential energy surfaces, nuclear gradients, Hessians, and vibrational frequencies, enabling the evaluation of both its capabilities and current limitations. The results demonstrate good performance for the hydrogen molecule, whereas the water molecule provides a more demanding test of quantum-hardware resources and highlights the trade-offs associated with error-mitigation strategies. The quantified analysis of the results identify the main sources of errors, suggesting improvement directions for more accurate quantum computer applications.