CyberSec Research

A disordered quasi-liquid layer of water is thought to cover the ice surface, but many issues, such as its onset temperature, its thickness, or its actual relation to bulk liquid water have been a matter of unsettled controversy for more than a century. In this perspective article, current computer simulations and experimental results are discussed under the light of a suitable theoretical framework. It is found that using a combination of wetting physics, the theory of intermolecular forces, statistical mechanics and out of equilibrium physics a large number of conflicting results can be reconciled and collected into a consistent description of the ice surface. This helps understand the crucial role of surface properties in a range of important applications, from the enigmatic structure of snow crystals to the slipperiness of ice.

Dimensional analysis is fundamental to the formulation and validation of physical laws, ensuring that equations are dimensionally homogeneous and scientifically meaningful. In this work, we use Lean 4 to formalize the mathematics of dimensional analysis. We define physical dimensions as mappings from base dimensions to exponents, prove that they form an Abelian group under multiplication, and implement derived dimensions and dimensional homogeneity theorems. Building on this foundation, we introduce a definition of physical variables that combines numeric values with dimensions, extend the framework to incorporate SI base units and fundamental constants, and implement the Buckingham Pi Theorem. Finally, we demonstrate the approach on an example: the Lennard-Jones potential, where our framework enforces dimensional consistency and enables formal proofs of physical properties such as zero-energy separation and the force law. This work establishes a reusable, formally verified framework for dimensional analysis in Lean, providing a foundation for future libraries in formalized science and a pathway toward scientific computing environments with built-in guarantees of dimensional correctness.

We introduce the Cyclic Variational Quantum Eigensolver (CVQE), a hardware-efficient framework for accurate ground-state quantum simulation on noisy intermediate-scale quantum (NISQ) devices. CVQE departs from conventional VQE by incorporating a measurement-driven feedback cycle: Slater determinants with significant sampling probability are iteratively added to the reference superposition, while a fixed entangler (e.g., single-layer UCCSD) is reused throughout. This adaptive reference growth systematically enlarges the variational space in most promising directions, avoiding manual ansatz or operator-pool design, costly searches, and preserving compile-once circuits. The strategy parallels multi-reference methods in quantum chemistry, while remaining fully automated on quantum hardware. Remarkably, CVQE exhibits a distinctive staircase-like descent pattern, where successive energy drops sharply signal efficient escape from barren plateaus. Benchmarks show that CVQE consistently maintains chemical precision across correlation regimes, outperforms fixed UCCSD by several orders of magnitude, and achieves favorable accuracy-cost trade-offs compared to the Selected Configuration Interaction. These results position CVQE as a scalable, interpretable, and resource-efficient paradigm for near-term quantum simulation.

Tip-enhanced Raman spectroscopy (TERS) is a powerful method for imaging vibrational motion and chemically characterizing surface-bound systems. Theoretical simulations of TERS images often consider systems in isolation, ignoring any substrate support, such as metallic surfaces. Here, we show that this omission leads to deviations from experimentally measured data through simulations with a new finite-field formulation of first-principles simulation of TERS spectra that can address extended, periodic systems. We show that TERS images of tetracyanoethylene on Ag(100) and defective MoS$_2$ monolayers calculated using isolated molecules or cluster models are qualitatively different from those calculated when accounting for the periodicity of the substrate. For Mg(II)-porphine on Ag(100), a system for which a direct experimental comparison is possible, these simulations prove to be crucial for explaining the spatial variation of TERS intensity patterns and allow us to uncover fundamental principles of TERS spectroscopy. We explain how and why surface interactions affect images of out-of-plane vibrational modes much more than those of in-plane modes, providing an important tool for the future interpretation of these images in more complex systems.

In cold, dense astrophysical environments dust grains are mixed with molecular ices. Chemistry in those dust/ice mixtures is determined by diffusion and reaction of molecules and radicals. However, investigations of diffusion of astrophysically relevant radicals and molecules across the surface and through the pores of cosmic dust grains and of surface reactions consequent to such diffusion is largely uncharted territory. This paper presents results of a study of a solid-state reaction of two molecular species, CO2 and NH3, separated by a layer of porous silicate grain aggregates, analogues of cosmic dust. The experiments demonstrate that the presence of the dust layer was necessary for a pure thermal CO2 + 2NH3 reaction to proceed, leading to the formation of ammonium carbamate (NH4+NH2COO-), an ionic solid containing a complex organic moiety of prebiotic interest recently detected in a protoplanetary disk. This result speaks for: (i) efficient diffusion of molecules on/within cosmic dust, (ii) an underestimated role for surface catalysis in the astrochemistry of cosmic dust, and (iii) potentially efficient dust-promoted chemistry in warm cosmic environments, such as protostellar envelopes and protoplanetary disks.

High-harmonic spectroscopy (HHS) in liquids promises real-time access to ultrafast electronic dynamics in the native environment of chemical and biological processes. While electron recollision has been established as the dominant mechanism of high-harmonic generation (HHG) in liquids, resolving the underlying electron dynamics has remained elusive. Here we demonstrate attosecond-resolved measurements of recolliding electron wave packets, extending HHS from neat liquids to aqueous solutions. Using phase-controlled two-colour fields, we observe a linear scaling of the two-colour delay that maximizes even-harmonic emission with photon energy, yielding slopes of 208+/-55 as/eV in ethanol and 124+/-42 as/eV in water, the latter matching ab initio simulations (125+/-48 as/eV). In aqueous salt solutions, we uncover interference minima whose appearance depends on solute type and concentration, arising from destructive interference between solute and solvent emission. By measuring the relative phase of solvent and solute HHG, we retrieve a variation of electron transit time by 113+/-32 as/eV, consistent with our neat-liquid results. These findings establish HHS as a powerful attosecond-resolved probe of electron dynamics in disordered media, opening transformative opportunities for studying ultrafast processes such as energy transfer, charge migration, and proton dynamics in liquids and solutions.

Molecular dynamics computer simulations have been conducted on neat liquid methanol, using three different united atom (three site) interatomic potentials: TraPPE [J. Phys. Chem. B 105, 3093 (2001)], UAM-I [J. Mol. Liq. 323, 114576 (2021)] and OPLS/2016 [J. Chem. Phys. 145, 034508 (2016)]. The effects of pressure, between 1 bar and 6 kbar, have been evaluated on total scattering structure factors, partial radial distribution functions, as well as on collective characteristics such as ring size distributions and cluster size distributions. Agreement with experimental density is nearly quantitative for all the three force fields, and major trends observed for recent pressure dependent neutron diffraction data are reproduced qualitatively. The OPLS/2016 force field, in general, generates properties that are markedly different from results originating from the other potentials. Pressure effects are hardly noticeable on most partial radial distribution functions and on the distribution of the number of hydrogen bonded neighbours. On the other hand, collective structural properties, like cluster- and ring size distributions exhibit significant changes with increasing pressure: larger clusters become more numerous, whereas the number of cyclic clusters, i.e., rings, decrease. The self-diffusion coefficient decreases with increasing pressure, and the same is valid for the average lifetime of hydrogen bonds.

Quantum computing offers a promising platform to address the computational challenges inherent in quantum chemistry, and particularly in valence bond (VB) methods, which are chemically appealing but suffer from high computational cost due to the use of nonorthogonal orbitals. While various fermionic-to-spin mappings exist for orthonormal spin orbitals, such as the widely used Jordan-Wigner transformations, an analogous framework for nonorthogonal spin orbitals remains undeveloped. In this work, we propose an alternative Jordan-Wigner-type mapping tailored for the nonorthogonal case, with the goal of enabling efficient quantum simulations of VB-type wavefunctions. Our approach paves the way towards the development of chemically interpretable and computationally feasible valence bond algorithms on near-term quantum devices. An initial theoretical analysis and a preliminary application demonstrate the feasibility of this encoding and its potential for extending the applicability of VB methods to larger and more complex systems.

Crystals and other condensed phases are defined primarily by their inherent symmetries, which play a crucial role in dictating their structural properties. In crystallization studies, local order parameters (OPs) that describe bond orientational order are widely employed to investigate crystal formation. Despite their utility, these traditional metrics do not directly quantify symmetry, an important aspect for understanding the development of order during crystallization. To address this gap, we introduce a new set of OPs, called Point Group Order Parameters (PGOPs), designed to continuously quantify point group symmetry order. We demonstrate the strength and utility of PGOP in detecting order across different crystalline systems and compare its performance to commonly used bond-orientational order metrics. PGOP calculations for all non-infinite point groups are implemented in the open-source package SPATULA (Symmetry Pattern Analysis Toolkit for Understanding Local Arrangements), written in parallelized C++ with a Python interface. The code is publicly available on GitHub.

Recent advancements have led to the development of bright and heavy metal-free blue-emitting quantum dot light-emitting diodes (QLEDs). However, consensus understanding of their distinct photophysical and electroluminescent dynamics remains elusive. This work correlates the chemical and electronic changes occurring in a QLED during operation using depth-resolved and operando techniques. The results indicate that oxygen vacancy forms in the ZnMgO layer during operation, with important implications on the charge injection and electrochemical dynamics. Taken together, the results suggest a causal relationship between oxygen vacancy formation and operational degradation of the blue-emitting ZnSeTe-based QLEDs.

The components of the radial correlation energy density are calculated and analyzed for the atoms from He to Ar. The components include the nucleus-electron potential correlation energy density, the kinetic correlation energy density and the electron-electron potential correlation energy density. The necessary correlated one and two-electron density matrices are obtained from the Extrapolated-Full-Configuration-Interaction (exFCI) wave function where the reference wave function is restricted Hartree-Fock (RHF) or restricted open-shell Hartree-Fock (ROHF) depending on whether the atom is closed or open-shell. The accuracy associated with integrating the HF and exFCI energy density components, and the correlation energy density components, is evaluated on the SG-1 and SG-2 atomic grids. The SG-1 grid provides atomic energies that are accurate to about 1 kJ mol$^{-1}$, with the exception of the kinetic energy. The SG-2 grid is required for the analysis of atomic kinetic energies and more subtle energetic effects. There is also a significant amount of integration error cancellation in the correlation energy densities. The radial correlation energy densities display notable shell structure, and there is a substantial difference between the $\alpha$ and $\beta$-electron correlation energy densities for the open-shell atoms.

The vibrational averaging module of the Dalton Project was extended to work also with the Amsterdam Density Functional (ADF) program, making it possible to calculate vibrational corrections to properties and at the same time include a treatment of relativistic effects for heavier atoms at the level of the Zeroth-Order Regular Approximation (ZORA). To illustrate the importance of the relativistic contributions, zero-point vibrational corrections were calculated for the electric field gradient tensor and the two NMR parameters, the isotropic shielding and the spin-spin coupling constants (SSCC), of selected mercury compounds. For all three properties, the vibrational corrected values performed closest to experimental values, and the magnitudes of the corrections depended on the level of relativity and the basis set in the calculation.

Understanding and predicting the glassy dynamics of small organic molecules is critical for applications ranging from pharmaceuticals to energy and food preservation. In this work, we present a theoretical framework that combines molecular dynamics simulations and Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to predict the structural relaxation behavior of small organic glass-formers. By using propanol, glucose, fructose, and trehalose as model systems, we estimate the glass transition temperature (Tg) from stepwise cooling simulations and volume-temperature analysis. These computed Tg values are then inserted into the ECNLE theory to calculate temperature-dependent relaxation times and diffusion coefficients. Numerical results agree well with experimental data in prior works. This approach provides a predictive and experimentally-independent route for characterizing glassy dynamics in molecular materials.

Turbulent mixers have been widely used in industrial settings for chemical production and increasingly for therapeutic nanoparticle formulation by antisolvent precipitation. The quality of the product is closely related to the fluid and mixing dynamics inside the mixers. Due to the rapid time scales and small sizes of many turbulent mixing geometries, computational fluid dynamics simulations have been the primary tool used to predict and understand fluid behavior within these mixers. In this study, we used the residual-based variational multiscale finite element method to perform high-fidelity turbulent simulations on two commonly used turbulent mixers: the multi-inlet vortex mixer (MIVM) and the confined impinging jets mixer (CIJM). We simulated two geometric variations, two-inlets and four-inlets, of the MIVM and two different inflow ratios of the CIJM. Through detailed turbulence results, we identify differences in turbulence onset, total energy, and mixing performance of the two MIVM configurations. With the CIJM results, we demonstrate the effect of the flow rate ratio on the impingement behavior, and as a result, on the mixing performance and turbulence. The cross-comparison between the two mixers shows key differences in turbulence and mixing behaviors, such as the turbulence onset, the energy decay, and the output mixing index. This study demonstrates the importance of a high-accuracy numerical scheme in simulating the turbulent mixers and understanding the similarities and differences among mixers. Furthermore, the results show potential for optimizing the operating conditions to achieve the best mixing performance.

Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $\alpha$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $\alpha$-PSO uses mechanistically clear optimisation strategies through simple, physically intuitive swarm dynamics directly connected to experimental observables, enabling practitioners to understand the components driving each optimisation decision. We establish a theoretical framework for reaction landscape analysis using local Lipschitz constants to quantify reaction space "roughness", distinguishing between smoothly varying landscapes with predictable surfaces and rough landscapes with many reactivity cliffs. This analysis guides adaptive $\alpha$-PSO parameter selection, optimising performance for different reaction topologies. Systematic evaluation of $\alpha$-PSO across pharmaceutically relevant reaction benchmarks demonstrates competitive performance with state-of-the-art Bayesian optimisation methods, while two prospective high-throughput experimentation (HTE) campaigns showed that $\alpha$-PSO identified optimal reaction conditions more rapidly than Bayesian optimisation. $\alpha$-PSO combines the predictive capability of advanced black-box ML methods with interpretable metaheuristic procedures, offering chemists an effective framework for parallel reaction optimisation that maintains methodological clarity while achieving highly performant experimental outcomes. Alongside our open-source $\alpha$-PSO implementation, we release $989$ new high-quality Pd-catalysed Buchwald-Hartwig and Suzuki reactions.

Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems.

We systematically investigate the calculation of excited states in quantum chemistry using auxiliary field quantum Monte Carlo (AFQMC). Symmetry allows targeting of the lowest triplet excited states in AFQMC based on restricted open-shell determinants, effectively as a ground-state calculation. For open-shell singlet states, excited state calculations can be stabilized with the appropriate trial states, but their quality can have a larger effect on the accuracy in AFQMC. We find that active space-based configuration interaction trial states are often not sufficient to obtain accurate results. We instead use truncated equation of motion coupled cluster with single and double excitations (EOM-CCSD) as trial states. We benchmark the performance of these calculations on a set of small and medium molecules and polyacenes, focusing on predominantly single excitations. We find that the AFQMC results, obtained at a per-sample cost scaling of $O(N^6)$, are systematically more accurate than those obtained using EOM-CCSD, reducing excitation energy errors by approximately half for open-shell singlets. In regimes where EOM-CC triples are impractical, these results position AFQMC as a scalable, higher-accuracy complement for low-lying excited states.

Polaritons - hybrid light-matter states formed from the strong coupling of a bright molecular transition with a confined photonic mode - may offer new opportunities for optical control of molecular behavior. Vibrational strong coupling (VSC) has been reported to impact ground-state chemical reactivity, but its influence on electronic excited-state dynamics remains unexplored. Here, we take a first step towards excited-state VSC by demonstrating optical modulation of the ReCl(CO)$_3$(bpy), (bpy = 2,2-bipyridine) complex under VSC using femtosecond ultraviolet (UV)-pump/infrared (IR)-probe spectroscopy. We establish ground-state VSC of ReCl(CO)$_3$(bpy) in a microfluidic Fabry-P\'erot cavity equipped with indium tin oxide (ITO)-coated mirrors. ITO is effectively dichroic as it is reflective in the IR and transmissive in the UV-visible and therefore minimizes optical interference. Excitation with UV pump light drives ReCl(CO)$_3$(bpy) into a manifold of electronic excited states which subsequently undergo non-radiative relaxation dynamics. We probe the transient response of the strongly-coupled system in the mid-IR, observing both Rabi contraction and cavity-filtered excited state absorption signatures. We reconstruct the intrinsic response of intracavity molecules from the transient cavity transmission spectra to enable quantitative comparison with extracavity control experiments. We report no changes in the excited-state dynamics of ReCl(CO)$_3$(bpy) under ground-state VSC. However, we do observe significant amplification of transient vibrational signals due to classical cavity-enhanced optical effects. This effort lays the groundwork to pursue direct excited-state VSC aimed at modulating photochemical reactivity.