Papers

The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accurate transition temperature prediction, $T_{1/2}$. The difficulty lies in the intricate connection between the spin-crossover energy, which is a molecular spectroscopic property, and $T_{1/2}$, a condensed phase property. Here we show how to obtain spin-crossover energies systematically by reverse engineering of experimental $T_{1/2}$ data. The protocol is based upon fitting the range separation parameter, $\omega$, in the hybrid LC-$\omega$PBE density functional to reproduce the experimental $T_{1/2}$ values for a series of metal complexes. We provide insights into the sources of variations of at least $\pm 15$ kJ mol$^{-1}$ found from common exchange and correlation functionals by comparing their performance against our reference data. By analysis of the sensitivity of transition temperatures to $\pm 1$ % shifts in the range separation parameter, we determined a typical uncertainty of $\pm 50$ K for them, and a $\pm 2$ kJ mol$^{-1}$ uncertainty in the extracted spin-crossover energies due to $\pm 1$ % variations of $T_{1/2}$. Lastly, we present results from the high-level, all-electron coupled cluster method for eight of the smaller molecules in the reference data set, and discuss the influence of the truncation of the excitation series upon the spin state energies.

Large-scale coarse-grained simulations of anisotropic particles require compact interaction models that retain orientation-dependent energetics. We present a symmetry-constrained Fourier–Morse framework in which the radial interaction is described by a Morse potential and its orientational dependence by Fourier expansions. The representation converges systematically with harmonic resolution, allows known orientational symmetries to be imposed directly, and supports further reduction through harmonic truncation and coefficient pruning. Its explicit Fourier structure also provides a natural basis for constructing or modifying model interactions with prescribed orientational symmetries. The parameterization requires only a sampled interaction landscape and is therefore independent of the method used to generate the reference data. We demonstrate the approach for four interaction classes of chiral $\alpha$-polyalanine helices, representing more than \num{300000} reference energy values with tens to a few hundred coefficients while reproducing equilibrium interaction features with meV- and mÅ-level errors. As a proof of concept, molecular-dynamics simulations using the reduced analytical potentials produce stable low-temperature configurations exhibiting local ordering motifs qualitatively consistent with those identified previously by Monte Carlo simulated annealing.

Machine-learning interatomic potentials (MLIPs) enable nanosecond-scale atomistic simulations of inorganic semiconductor nanocrystals, but low errors on held-out configurations do not necessarily guarantee stable molecular dynamics. We benchmark five graph-neural-network MLIPs, SchNet, PaiNN, NequIP, Allegro and MACE, for dynamical stability in a chloride-passivated cadmium selenide nanocluster containing 149 atoms. The models were trained under harmonized conditions on 1,000 configurations and evaluated against 2,000 held-out configurations generated using density functional theory, considering force accuracy, computational efficiency, uncertainty and structural stability during 1 ns simulations at 300 K. Allegro produced the lowest validation force mean absolute errors, ranging from 12 to 14 meV per angstrom, whereas SchNet produced the largest, ranging from 84 to 110 meV per angstrom. This ranking did not predict dynamical robustness: NequIP remained stable for 1 ns without additional training data, whereas MACE became stable only after an ensemble-based active-learning procedure added 34 uncertainty-selected configurations. PaiNN and Allegro remained unstable after the addition of 100 and 95 configurations, respectively, and SchNet also failed to achieve stable dynamics within the tested augmentation budget. Under the benchmark hardware conditions, the machine-learning potentials required 6-66 ms per molecular-dynamics step, compared with approximately 20 s for density functional theory. These results show that held-out prediction errors alone are insufficient for selecting interatomic potentials for finite, surface-dominated nanostructures. Reliable deployment requires long-timescale dynamical testing combined with uncertainty-guided refinement, while the effectiveness and data efficiency of active learning remain strongly architecture dependent.

Burning neat hydrogen in aircraft turbines eliminates carbon emissions, yet rapid reaction rates trigger nozzle flashback hazards and high nitrogen oxide emissions across flight throttles. This study investigates aerothermal holding mechanisms, flashback safety margins, and emission pathways in a dual-swirl combustor across equivalence ratios from 0.55 to 1.00. Three-dimensional simulations combine curvature-corrected shear-stress transport turbulence closure, dual-rate finite-rate and eddy-dissipation chemical kinetics, and discrete ordinates radiation, validated against experimental laser benchmarks using ASME grid standards. Advancing engine throttle triggers a topological flame transition from a faceplate-attached M-flame at lean idle to a lifted V-flame above equivalence ratio 0.895, while wall flashback safety indices consistently exceed 3.42. Nitric oxide emissions transition from water-chaperoned nitrous oxide intermediate reactions at lean idle (28.01 ppm, EINOx = 1.85 g/kg), scaling to 319.21 ppm (EINOx = 35.40 g/kg) at takeoff under thermal Zeldovich dominance, governed by a power-law exponent of 4.92. These findings deliver validated operability limits and establish an accessible workstation-based screening methodology for practical zero-carbon aero-engine combustor development.

Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost. A central challenge in using these tools for high-throughput property calculations is translating high-level scientific intent into adaptive simulation campaigns without compromising workflow rigour. We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations. Typed execution graphs provide structured and provenance-aware execution for standardized workflows, with large language models (LLMs) restricted to planning and routing while deterministic Python components perform scientific computation and validation. Complementing this structured execution, a coding agent constructs customized workflows for tasks requiring greater procedural flexibility while invoking existing Potente functions for supported calculations. We demonstrate El Agente Potente across computational materials discovery, molecular energy-landscape exploration, adsorption, and catalytic reaction workflows, together with systematic benchmarks of reproducibility and LLM token cost. These results establish typed execution graphs and code-based workflow construction as complementary mechanisms for agentic scientific computing, combining controlled, auditable execution with the flexibility required for customized atomistic simulations

Continuous unitary transformations repeatedly evaluate the same operator algebra as the coefficients of a Hamiltonian and its observables evolve. We compile these algebraic relationships once into a numerical operation graph that can be saved and reused for new coefficient sets, additional operators, derivatives, and forward and reverse calculations on CPUs and GPUs. Reuse is demonstrated by applying the same compiled graph to hydrogen and deuterium parameterizations of a reduced molecular model without regenerating the operator equations. The main computational challenge is construction: generated many-body algebra can contain hundreds of millions of contributions before numerical propagation begins. We address this by determining the required storage in advance and writing contributions directly into the final disk-backed representation. In a deliberately large diagnostic-enabled stress test containing more than 350 million generated contributions, peak host memory remains only about 7% above the final stored size. Cross-checks show that CPU and GPU implementations, forward and reverse operations, and repeated loading of the compiled graph reproduce the tested operations to numerical precision. This compile-once representation separates expensive operator-algebra generation from the numerical transformations that reuse it.

A chemical transition state is a phase-space bottleneck: trajectories approach along stable directions and leave along unstable directions. Lagrangian betweenness (LB), introduced in finite-time transport theory for fluid flows, combines backward and forward deformation in a way suggestive of this gather-and-disperse geometry. We ask what LB measures when the transition-state geometry is known. For a linear rank-one saddle, LB is spatially constant even though the stable and unstable manifolds are present. Relative nonlinear corrections remain small on shrinking initial neighborhoods, including observation times on the logarithmic local escape scale. At fixed spatial resolution, however, increasing observation time can concentrate the spatial variation of LB near a stable or unstable manifold. A separable quartic Hamiltonian makes these two limits explicit. A nonseparable Hamiltonian shows local flattening near a hyperbolic periodic orbit. We then use HCN/CNH isomerization, where dividing surfaces and gap times are established by phase-space transition-state theory. Before any exit, larger LB generally accompanies longer eventual gap times. The incoming reactive action provides a dynamical interpretation: initial conditions closer to the normally hyperbolic invariant manifold have longer local passages and greater accumulated stretching during passage. Among trajectories still inside at 0.5 ps that exit before 5 ps, early LB does not usefully rank exit times. LB therefore characterizes transition-state-organized transport in this example; a large value is neither an intrinsic manifold marker nor a general measure of molecular residence time.

Single-ion magnets (SIMs) show promise for high-density storage and quantum computing, but predicting spin-phonon coupling (SPC) and magnetic relaxation remains challenging due to the need for numerous non-equilibrium multiconfigurational calculations. Recent advances in quantum embedding methods offer a potential route to address this issue. In this work, density matrix embedding theory (DMET) combined with complete active space self-consistent field (CASSCF) is benchmarked for the static magnetic properties and spin-phonon coupling (SPC) parameters of Dy$^{3+}$-based SIMs. The method is further combined with spatial truncation to calculate SPC parameters for these SIMs. It is found that truncating the space near the first coordination sphere reduces the computational cost dramatically while keeping the errors in the effective energy barrier and relaxation time-scale negligible. This study provides a practical calculation framework for accurate and efficient spin dynamics prediction, laying the foundation for the rational design of high-performance single-molecule magnets.

Accurate quantum number assignment for calculated molecular energy levels is a critical bottleneck in generating line broadening parameters for comprehensive line lists for radiative transfer applications. We present an automated pipeline for assigning Air Force Geophysics Laboratory (AFGL) quantum numbers to CO2 calculated rovibrational states lying below 15,000cm$^{-1}$ across all 12 stable isotopologues. A GraphSAGE graph neural network is trained transductively on empirical (MARVEL) energy levels, exploiting inter-isotopologue perturbation chains and intra-isotopologue rotational ladder edges to propagate assignment information to unlabelled calculated states. Physical uniqueness is enforced locally by a Hungarian algorithm solver operating within groups of states sharing the same polyad, rotational quantum number, and parity. A five-generation bootstrap loop iteratively promotes high-confidence predictions into the training set, expanding coverage without additional labelling effort. The pipeline assigns 224,650 previously unlabelled states over 12 isotopologues, accounting for 10.7$\%$ of all available states (including MARVEL-derived levels), with coverage now increased to 97.4$\%$ below 5000cm$^{-1}$. The pipeline includes a novel decision tree method for converting AFGL to Herzberg notation in asymmetric isotopologues, while the architecture and Hungarian uniqueness enforcement are applicable beyond CO2, any molecular system with a conserved polyad-like quantum number and a large body of unlabelled computed states is a natural target for this approach, suggesting a pathway toward automated quantum number annotation for the next generation of large-scale computed line lists.

First-principles, electronic-structure simulations provide direct access to ground-state, global system properties. In the context of computational electrochemistry these are, e.g., bias-dependent energies, free energies, work functions, and capacitances. However, a comparably systematic local description of how these quantities respond to biasing via interfacial charging is still missing. Here, we introduce the screened electrochemical Born effective charge (SEBEC), a mixed total-energy derivative that measures the force response of an atomic degree of freedom to interfacial charging under electrochemical boundary conditions. SEBECs play the role for electrochemistry that Born effective charges play in the modern theory of polarization: they provide an unambiguous decomposition of global response and generate the same formal structures for structural relaxation, capacitance, and electrochemical Stark tuning that are familiar from solid-state response theory. The broader implication is a local response framework that brings electrochemical interfaces closer to the conceptual rigor long established for insulating solids.

Multireference electronic-structure calculations remain difficult to automate because critical workflow decisions, including active-space selection, state averaging, convergence recovery, and state identification, traditionally rely on expert judgment. Here, we investigate whether an autonomous large language model (LLM) agent can perform these tasks without human intervention. The agent selects active spaces using literature-grounded analogies or explicitly documented chemical reasoning, generates and submits ORCA calculations, analyzes outputs, and records all decisions in an auditable reasoning log. Benchmarking against 558 vertical transition energies (VTEs) from QUESTDB shows that an unguided baseline agent achieves 24.9% coverage with a mean absolute error (MAE) of 0.373 eV. Introducing a structured decision ladder increases coverage to 44.1% while reducing the MAE to 0.339 eV. The largest gains are observed for double and Rydberg excitations, demonstrating that expert-informed procedural guidance substantially improves active-space construction and state identification. When provided with complete workflow information, the agent successfully reproduces published QUEST calculations with an MAE of only 23 meV, resolving 75% of target configurations within seven attempts. These results demonstrate that contemporary LLM agents can autonomously execute and reproduce complex multireference quantum-chemical workflows, while highlighting the importance of structured reasoning frameworks for achieving reliable high-throughput and high-fidelity electronic-structure calculations.

Screened hybrid functionals such as HSE06 improve the description of band gaps, charge localization, and redox energetics relative to semilocal approximations, but their explicit Hartree-Fock exchange term is computationally costly for large, periodic systems, especially in plane-wave basis set calculations. Here we present RS-CIDER, a machine-learned non-local exchange functional that approximates the short-range Hartree-Fock exchange term in HSE06 by explicitly fitting both ground-state energies and single-particle energy levels. RS-CIDER combines scale-invariant semilocal and non-local density descriptors and can be evaluated self-consistently without explicitly applying the short-range Hartree-Fock exchange operator. RS-CIDER shows close agreement with HSE06 for molecular reaction energies and solid-state band gaps. Further tests across distinct materials show agreement between RS-CIDER and HSE06 for local magnetism, Cu-O phase competition, polaron localization, and neutral-defect energetics. For an Fe olivine, chemistry-specific fine-tuning recovers the HSE06 Li intercalation voltage. A timing benchmark shows that RS-CIDER reduces the measured per-SCF-step wall time by more than an order of magnitude relative to HSE06. Together, these molecular and solid-state results establish RS-CIDER as an efficient self-consistent machine-learned surrogate for HSE06.

We show that, for the unique case of a system of electrons and nuclei in one dimension, the $\hGamma$ operator in phase space electronic structure theory has a zero non-Abelian curl. Using this fact, we are able to show that a phase space electronic structure framework in Wigner space has a one-to-one mapping to the exact fully quantum vibronic Hamiltonian. In other words, one loses no information by working within the framework of phase space electronic structure theory rather than Born-Huang theory; the only difference is that, whereas Born-Huang theory represents the total vibronic Hamiltonian as a quadratic order expansion in $\hbar$, the Hamiltonian becomes an infinite order expansion in $\hbar$ within a phase space electronic structure framework (a so-called "Generalized Born-Huang" approach). That being said, numerical results at first or second order demonstrate that, for a limited number of electronic states, phase space electronic structure theory strongly outperforms Born-Huang theory – a result that justifies a great deal more research into this novel electronic structure approach.

Signatures of a material object's broken inversion symmetry within its intrinsic excitations and associated optical fields have largely evaded characterization from a perspective that evenly accounts for the inseparability between matter and field dynamics. Here, we formulate a complex-valued pseudoscalar chirality metric that is derived from the coupled electromagnetic and material governing equations, and demonstrate its resolution of the excitational chirality exhibited by the eigenmodes and eigenfields of a family of structurally chiral objects, including those with chirally connected enantiomeric states.

We present a unified frozen natural orbital (FNO) framework for periodic Gaussian-orbital post-Hartree-Fock calculations with explicit k-point sampling. The approach includes conventional ground-state FNOs together with state-specific (SS-FNO) and state-averaged (SA-FNO) variants constructed from perturbative one-particle density matrices. We implement these approximations for periodic Moller-Plesset perturbation theory, algebraic diagrammatic construction, and equation-of-motion coupled cluster methods in the PySCF software package and benchmark them for correlation energies, equations of state, fundamental band gaps, and quasiparticle band structures of semiconductors and insulators. FNO truncation reproduces canonical results with small errors while substantially reducing computational cost. SS-FNO enables accurate large-basis band-gap calculations, including quadruple-zeta results, whereas SA-FNO efficiently compresses the virtual space for multi-state band-structure calculations. Combined with basis-set and thermodynamic-limit extrapolations, the framework provides a practical route to high-accuracy correlated calculations in periodic systems.

We present the first application of transcorrelated (TC) coupled-cluster (CC) theory to noncovalent interactions within the xTC approximation. The method is assessed for the A24 dataset of hydrogen-bonded, mixed and pure dispersion bound dimers. The xTC interaction energies are computed at the CCSD, DCSD, and CCSD(T) levels in aug-cc-pVDZ (AVDZ), aug-cc-pVTZ (AVTZ) basis sets, and are compared with both canonical and explicitly correlated F12 methods. Because non-covalent interaction energies rely on delicate error cancellation between dimers and monomers, we optimize the TC Jastrow factor for each dimer, and reuse the same parameters for the corresponding monomer calculations. This shared-parameter strategy reduces stochastic optimization noise which would otherwise dominate the interaction energies. The results show that the xTC-CCSD(T)/AVTZ method performs extremely well for the hydrogen-bonded systems, with a mean-absolute error of only 0.007 kcal/mol in the interaction energies, with respect to the benchmark values generated with CCSD(T)/CBS + $\Delta$CCSDT(Q) + core corrections. For pure dispersion bound systems the errors are slightly larger (0.055 kcal/mol), leading to an overall MAE of 0.030 kcal/mol for the entire dataset. Adding a $\Delta$MP2 correction to the xTC-CCSD(T)/AVDZ brings these results close to AVTZ quality and provides a practical route toward larger noncovalent systems. A decomposition of the xTC interaction energy into a mean-field and correlation contribution shows that part of the correlation contribution is systematically shifted by the TC method to the mean-field contribution. This physically appealing feature indicates that the TC method has great potential for the quantitative description of noncovalent interactions, and opens a new route for high-accuracy quantum chemistry to be applied to systems of biological and soft-matter interest.

Circularly polarized light has long been used to distinguish the two mirror images of chiral molecules. Recently, a conceptual breakthrough has predicted that a new form of optical fields - synthetic chiral light - could usher in an 'electric-dipole revolution' in chiroptical interactions. We report the first experimental evidence for this prediction in the optical domain, using a three-dimensional laser electric field synthesized to trace a chiral pattern. The ionization and fragmentation of chiral molecules was found to depend on both the molecular handedness, and the handedness and geometry of the synthetic chiral field. Supported by two theoretical models, our results demonstrate that synthetic chiral light can induce strong chiroptical interactions, opening new horizons for asymmetric photochemistry and chiral analysis.

Chirality underlies molecular function in living matter, yet its optical detection remains challenging because conventional chiroptical spectroscopies rely on weak corrections to the dominant electric-dipole light-matter interaction, making desired optical signals weak and fragile. Topology offers a route to robustness, enabling observables whose defining properties survive disorder and imperfections. However, experimental realization of a practical topological observable for chiral spectroscopy has remained elusive. Here we realize chiral topological light and demonstrate such an observable. A tightly focused, phase-locked, counter-rotating two-colour field encodes chirality in the three-dimensional electric-field trajectory while its dominant topological charge resides in the longitudinal electric-field component where it remains hidden from direct far-field detection. An isotropic chiral medium acts as a topological transducer, converting the latent topology of the driving field into a propagating nonlinear response whose topological charge becomes directly observable in the far field while remaining strongly suppressed in achiral media. Using randomly oriented chiral single-crystal powders, we experimentally detect the enantio-sensitive response through the topological charge of the emitted field. Directly accessible in the far field and robust against experimental imperfections, this observable allows us to track and control chiral signal on attosecond timescales through the relative phase of the driving two-colour fields. Our results establish topology as a practical resource for ultrafast chiral optical spectroscopy.

Chirality, a systematic rotational bias in the motion of a particle, arises in a wide range of physical and biological systems, from charged colloids in magnetic fields to swimming bacteria near surfaces. While its effects in homogeneous environments are well understood, the interplay between chirality and structural disorder has remained largely unexplored. Here we investigate the chiral random walk on two-dimensional percolation clusters above the percolation threshold, combining numerical simulations with an analytically tractable annealed-disorder approximation. We find that the long-time diffusion coefficient depends non-monotonically on both the chirality parameter and the obstacle density: for every obstacle density above the percolation threshold, there exists an optimal chirality that enhances diffusion relative to the achiral walk. We show that the optimal chirality is set by an edge-adhering mechanism: maximum diffusion is achieved when the persistence length of the wall-adhering motion matches half the typical obstacle cluster perimeter. This yields a closed-form prediction for the optimal chirality in terms of geometric properties of the medium alone, which we verify across the full range of obstacle densities studied. The enhancement extends to first-passage statistics, where chirality shortens typical search times at strong disorder while lengthening them at weak disorder, with direct implications for biological navigation in disordered environments.

An estimation of optoelectronic properties is a crucial necessity and an ongoing challenge in modern functional material science. This involves accurate accessing of ground and excited states in systems with complex electronic structure that are often too computationally expensive for accurate many-body methods. Building on our previous work on ground states [J. Phys. Chem. Lett. 2025, 16, 3295-3301], we present an efficient and cost-effective approach for evaluating electronic excitations in functional materials, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques. We demonstrate its performance on {\pi}-electron-correlated systems, namely polycyclic aromatic compounds. The transferability and effectiveness of the derived machine learning model are demonstrated on a number of challenging examples with up to 34 {\pi}-electrons.

Designing polymers with targeted properties requires navigating vast chemical spaces from limited labeled data. Here we introduce PolyLatentFlow, a framework based on continuous-time flow matching in latent space for unconditional and conditional polymer generation, together with LlamaUni, a multimodal representation combining polymer sequence and 3D structural information. In unconditional generation, PolyLatentFlow with LlamaUni produced the largest yield of valid candidates novel relative to PolyInfo among the evaluated unconditional generators while maintaining high diversity. For $T_g$ conditioning, generated property distributions shifted systematically across a 200 {\deg}C target range. In multi-property tasks, molecular representations showed similar surrogate target fidelity but differed markedly in validity, training-set replay, and structural proximity to labeled polymers. PolyLatentFlow with LlamaUni consistently combined high validity with low replay and achieved the largest per-attempt yield of nonreplayed target hits for CO$_2$/N$_2$ conditioning. These results demonstrate latent space flow matching for polymer inverse design and identify molecular representation as a key determinant of target control and exploration beyond labeled chemistry.

An object releasing a chemical into a surrounding flow leaves behind a plume that encodes the rate of release, but for most solutes, that plume is invisible. Classical transport theory describes this configuration through a self-similar concentration boundary layer whose structure has never been resolved experimentally in forced flow at low Reynolds number: a century of measurements at these conditions has returned integrated transfer rates rather than the field itself. Here we show that both the field and the information it carries are experimentally accessible. Around hydrogel posts photopatterned inside a microfluidic channel, a released solute drives suspended colloidal particles away by diffusiophoresis, i.e., their drift along chemical gradients, carving a particle-free “shadow” downstream. Using a fluorescent solute, we resolve the concentration field around the post and recover the classical self-similar structure of convective mass transfer. For an invisible surfactant, the width of the shadow, benchmarked against a directly imaged solute on the same platform, then serves as a proxy for the dimensionless rate of mass transfer from the post. Finally, comparing two orientations of a triangular post tests a classical prediction by Brenner: the local concentration fields rearrange, yet no orientation dependence of the integrated transfer rate is resolved. For solute–tracer pairs whose diffusiophoretic response is known, colloidal shadows thus turn ordinary tracer particles into quantitative reporters of chemical exchange that cannot be observed directly.

Water dissociation at solid surfaces underpins processes ranging from corrosion and catalysis to electrochemistry and photovoltaics. Defects often serve as reactive sites for dissociation, yet how solvation influences water dissociation at such sites remains poorly understood. Here, we use state-of-the-art machine-learned interatomic potentials to explore water dissociation at defective graphene-water interfaces. We show that solvation qualitatively changes the reaction mechanism at a graphene single vacancy (SV), opening pathways that are absent for an isolated water molecule. Whereas the gas-phase process proceeds via a single concerted channel, the solvated SV splits water through two competing pathways: a basic route forming SV-H and OH-(aq), and an acidic route forming SV-OH and H3O+(aq). These lower-barrier pathways produce distinct chemisorbed intermediates that enhance graphene-water adsorption. Accordingly, even a simple carbon vacancy gives rise to unexpectedly rich interfacial chemistry, coupling surface chemistry to interfacial charge and wettability, with implications for carbon functionalization and nanofluidic transport.

Cryogenic buffer-gas cells are widely used to produce cold molecular beams, but the microscopic dynamics governing beam formation remain challenging to model. Here we present fully kinetic simulations of a cryogenic buffer-gas cell using the Direct Simulation Monte Carlo method implemented in the PICLas framework, treating the buffer gas and ablated molecules within a single unified model. We capture characteristic features of cryogenic buffer-gas sources, including plume cooling, directed transport toward the aperture, and the formation of a slow molecular beam, while also resolving energy transfer from the hot ablation plume to the helium buffer gas that is inaccessible to existing approaches relying on the background-gas approximation. Our results demonstrate that fully kinetic simulations can provide detailed insights into buffer-gas cell dynamics and open a route toward a systematic optimization of such sources.

Understanding protein crystallization pathways is essential for controlling crystallization in structural biology, materials science, and pharmaceutical applications. Classical nucleation theory does not fully capture crystallization processes for several proteins, including ferritin. Here, we combine acoustic levitation with small- and wide-angle X-ray scattering (SAXS and WAXS) to monitor ferritin crystallization in evaporating aqueous polyethylene glycol (PEG) solutions. Acoustic levitation rapidly drives the droplets through a broad range of protein and polymer concentrations, enabling time-resolved measurements of crystallization during evaporation. The scattering data show that ferritin crystals form during evaporation and subsequently lose their crystalline order upon further dehydration. Varying the PEG molecular weight switches between distinct crystallization pathways: one dominated by attractive protein-protein interactions and another dominated by repulsive interactions and excluded-volume effects. Furthermore, we find that lower molecular weight PEG (1000 g/mol) suppresses the dehydration-induced loss of crystalline order observed for higher molecular weight PEG (6000 g/mol), providing a simple strategy for improving protein crystal stability.

The use of the shifted inverse Hamiltonian in the Lanczos algorithm is examined. We present a perturbative inverse and demonstrate that the Krylov subspace formed from it is better suited to approximating the ground state wavefunction compared with the Hamiltonian itself. We further demonstrate that the perturbative inverse provides an adequately accurate approximation to the inverse Hamiltonian at second order. Naturally, the quality of the results depend on the quality of the initial guess, here we use the Hartree-Fock, CISD, MP-PT1, and EN-PT1 wavefunctions. Lastly, we examine the scaling of the method where we find that accurate results can be found for similar cost as PT5.

We propose the use of an a priori localization method based on separating the molecule into fragments to generate a wavefunction suitable for post Hartree-Fock methods. A quasi-Newton approach utilizing Thouless rotation is developed to optimize the strictly localized molecular orbitals(sLMO). We discuss the properties of the sLMO-HF wavefunction and compare it to the tail deleted a posteriori localization methods that are usually done in preparation of post Hartree-Fock methods. We then discuss the effects of choosing different fragmentation patterns and basis sets.

We expand the correlation-consistent effective core potentials (ccECPs) library by developing semi-local pseudopotentials and matching basis sets by heavy-elements from $5d$ (Hf, Os, Hg) and $6p$ (Tl, Po, At, Rn) blocks. In order to accurately capture scalar relativistic effects, spin-orbit coupling, and electron-electron correlation, we implement a tiered core-valence partitioning strategy across three distinct resolutions. This includes a small 60-core (Hf, Os, Hg) that explicitly correlates subvalence shells, a large 78-core definition for the main-group elements that rigorously accounts for core polarization and relaxation effects in sparse valence environments, and an intermediate 68-core partition for Hg and Tl. This 68-core architecture represents a unique development in the ccECP library, optimizing the balance between accuracy and computational efficiency in a manner unexplored for ligther elements. Optimized against relativistic all-electron CCSD(T) references, the ccECPs deliver outstanding atomic precision, achieving a global average atomic low-lying states deviation of just 0.045 eV. This accuracy translates directly to robust molecular transferability, systematically restricting dissociation energy discrepancies to under 0.03 eV, equilibrium bond lengths to within 0.005 Å. By enforcing a regularized, finite potential at the origin for enhanced numerical stability in stochastic quantum Monte Carlo methods, this library removes a critical methodological bottleneck for predictive many-body simulations of heavy-element systems and materials.

Coupled Cluster (CC) theory is one of the most popular correlated wavefunction methods, yet the steep computational cost scaling of CC truncated at higher rank limits its application. Modern Selected Configuration Interaction (SCI) methods successfully exploit the sparsity of ground-state wavefunctions in the determinant space, motivating an analogous approach with the exponential CC ansatz. In this work, we introduce Selected Coupled Cluster (SCC), an arbitrary-order sparse CC method that explicitly solves the CC residual equations within a compact amplitude space guided by an SCI wavefunction. SCC optimizes the CC amplitudes in the sparse amplitude space. Using a pilot Just-in-Time (JIT) compiled implementation, we benchmark SCC against SCI, SCI+PT2, standard rank-truncated dense CC (CCSD, CCSDT), and DMRG across various chemical systems. We demonstrate that SCC exhibits fast, monotonic convergence with the SCI variational threshold, outperforming SCI and surpassing SCI+PT2 in the tight-threshold regime. SCC also provides accurate energies for high-dimensional topologies, where DMRG typically converges slowly with bond dimension. SCC thus presents an efficient drop-in enhancement for modern SCI workflows.

We present a direct method of separating intra- (form factor) and inter-molecular (background) contri- butions in pulse EPR dipolar spectroscopy (PDS) experiments. The form factor/background separation is accomplished by rearranging data measured at two different biradical concentrations, without any fitting fundamentally required. This new data processing method allows to analyse PDS data from spin concen- trated samples for which background separation was previously unrealistic. In particular, the presented method would be of high importance for studies of spin labeled biomolecules, especially in respect to the liquid-liquid phase separation (LLPS). Furthermore, we propose a simple measurement and data processing protocol to separate the PDS contributions from each phase in LLPS samples. The protocol for the form factor/background separation for RIDME experiment is also discussed