CyberSec Research

Interpretation of time-resolved spectroscopies such as transient absorption (TA) or two-dimensional (2D) spectroscopy often relies on the perturbative description of light-matter interaction. In many cases the third order of nonlinear response is the leading and desired term. When pulse amplitudes are high, higher orders of light-matter interaction can both distort lineshapes and dynamics and provide valuable information. Here, we present a general procedure to separately measure the nonlinear response orders in both TA and 2D spectroscopies, using linear combinations of intensity-dependent spectra. We analyze the residual contamination and random errors and show how to choose optimal intensities to minimize the total error in the extracted orders. For an experimental demonstration, we separate the nonlinear orders in the 2D electronic spectroscopy of squaraine polymers up to 11$^{th}$ order.

We present a protocol for computing core-ionisation energies for molecules, which is essential for reproducing X-Ray photoelectron spectroscopy experiments. The electronic structure of both the ground state and the core-ionised states are computed using Multiwavelets and Density-Functional Theory, where the core ionisation energies are computed by virtue of the $\Delta$SCF method. To avoid the collapse of the core-hole state or its delocalisation, we make use of the Maximum Overlap Method, which provides a constraint on the orbital occupation. Combining Multiwavelets with the Maximum Overlap Method allows for the first time an all-electron calculation of core-ionisation energies with Multiwavelets, avoiding known issues connected to the use of Atomic Orbitals (slow convergence with respect to the basis set limit, numerical instabilities of core-hole states for large systems). We show that our results are consistent with previous Multiwavelet calculations which made use of pseudopotentials, and are generally more precise than corresponding Atomic Orbital calculations. We analyse the results in terms of precision compared to both Atomic Orbital calculations and Multiwavelets+pseudopotentials calculations. Moreover, we demonstrate how the protocol can be applied to target molecules of relatively large size. Both closed-shell and open-shell methods have been implemented.

Efficient chemical kinetic model inference and application for combustion problems is challenging due to large ODE systems and wideley separated time scales. Machine learning techniques have been proposed to streamline these models, though strong nonlinearity and numerical stiffness combined with noisy data sources makes their application challenging. The recently developed Kolmogorov-Arnold Networks (KANs) and KAN ordinary differential equations (KAN-ODEs) have been demonstrated as powerful tools for scientific applications thanks to their rapid neural scaling, improved interpretability, and smooth activation functions. Here, we develop ChemKANs by augmenting the KAN-ODE framework with physical knowledge of the flow of information through the relevant kinetic and thermodynamic laws, as well as an elemental conservation loss term. This novel framework encodes strong inductive bias that enables streamlined training and higher accuracy predictions, while facilitating parameter sparsity through full sharing of information across all inputs and outputs. In a model inference investigation, we find that ChemKANs exhibit no overfitting or model degradation when tasked with extracting predictive models from data that is both sparse and noisy, a task that a standard DeepONet struggles to accomplish. Next, we find that a remarkably parameter-lean ChemKAN (only 344 parameters) can accurately represent hydrogen combustion chemistry, providing a 2x acceleration over the detailed chemistry in a solver that is generalizable to larger-scale turbulent flow simulations. These demonstrations indicate potential for ChemKANs in combustion physics and chemical kinetics, and demonstrate the scalability of generic KAN-ODEs in significantly larger and more numerically challenging problems than previously studied.

Data-driven methods have shown potential in electric-vehicle battery management tasks such as capacity estimation, but their deployment is bottlenecked by poor performance in data-limited scenarios. Sharing battery data among algorithm developers can enable accurate and generalizable data-driven models. However, an effective battery management framework that simultaneously ensures data privacy and fault tolerance is still lacking. This paper proposes a swarm battery management system that unites a decentralized swarm learning (SL) framework and credibility weight-based model merging mechanism to enhance battery capacity estimation in data-limited scenarios while ensuring data privacy and security. The effectiveness of the SL framework is validated on a dataset comprising 66 commercial LiNiCoAlO2 cells cycled under various operating conditions. Specifically, the capacity estimation performance is validated in four cases, including data-balanced, volume-biased, feature-biased, and quality-biased scenarios. Our results show that SL can enhance the estimation accuracy in all data-limited cases and achieve a similar level of accuracy with central learning where large amounts of data are available.

Linearized Coupled Cluster Doubles (LinCCD) often provides near-singular energies in small-gap systems that exhibit static correlation. This has been attributed to the lack of quadratic $T_2^2$ terms that typically balance out small energy denominators in the CCD amplitude equations. Herein, I show that exchange contributions to ring and crossed-ring contractions (not small denominators per se) cause the divergent behavior of LinCC(S)D approaches. Rather than omitting exchange terms, I recommend a regular and size-consistent method that retains only linear ladder diagrams. As LinCCD and configuration interaction doubles (CID) equations are isomorphic, this also implies that simplification (rather than quadratic extensions) of CID amplitude equations can lead to a size-consistent theory. Linearized ladder CCD (LinLCCD) is robust in statically-correlated systems and can be made $O(n_{\text{occ}}^4n_{\text{vir}}^2)$ with a hole-hole approximation. The relationship between LinLCCD and random-phase approximation sets the stage for the development of next-generation double-hybrid density functionals that can describe static correlation.

Finding the ground state of spin glasses is a challenging problem with broad implications. Many hard optimization problems, including NP-complete problems, can be mapped, for instance, to the Ising spin glass model. We present a graph-based approach that allows for accurate state initialization of a frustrated triangular spin-lattice with up to 20 sites that stays away from barren plateaus. To optimize circuit efficiency and trainability, we employ a clustering strategy that organizes qubits into distinct groups based on the maximum cut technique, which divides the lattice into two subsets maximally disconnected. We provide evidence that this Max-Cut-based lattice division offers a robust framework for optimizing circuit design and effectively modeling frustrated systems at polynomial cost. All simulations are performed within the variational quantum eigensolver (VQE) formalism, the current paradigm for noisy intermediate-scale quantum (NISQ), but can be extended beyond. Our results underscore the potential of hybrid quantum-classical methods in addressing complex optimization problems.

In the present work, a generative deep learning framework combining a Co-optimized Variational Autoencoder (Co-VAE) architecture with quantitative structure-property relationship (QSPR) techniques is developed to enable accelerated inverse design of fuels. The Co-VAE integrates a property prediction component coupled with the VAE latent space, enhancing molecular reconstruction and accurate estimation of Research Octane Number (RON) (chosen as the fuel property of interest). A subset of the GDB-13 database, enriched with a curated RON database, is used for model training. Hyperparameter tuning is further utilized to optimize the balance among reconstruction fidelity, chemical validity, and RON prediction. An independent regression model is then used to refine RON prediction, while a differential evolution algorithm is employed to efficiently navigate the VAE latent space and identify promising fuel molecule candidates with high RON. This methodology addresses the limitations of traditional fuel screening approaches by capturing complex structure-property relationships within a comprehensive latent representation. The generative model provides a flexible tool for systematically exploring vast chemical spaces, paving the way for discovering fuels with superior anti-knock properties. The demonstrated approach can be readily extended to incorporate additional fuel properties and synthesizability criteria to enhance applicability and reliability for de novo design of new fuels.

Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy calculations require vast computational resources, limiting these explorations severely in practice. Machine learning potentials (MLPs) offer a solution to increase computational efficiency, while retaining the accuracy of reliable first-principles data used for their training. Unfortunately, MLPs will be limited in their generalization ability within chemical (reaction) space, if the underlying training data is not representative for a given application. Within the framework of automated reaction network exploration, where new reactants or reagents composed of any elements from the periodic table can be introduced, this lack of generalizability will be the rule rather than the exception. Here, we therefore study the benefits and drawbacks of two MLP concepts in this context. Whereas universal MLPs are designed to cover most of the relevant chemical space in their training, lifelong MLPs push their adaptability by efficient continual learning of additional data. While the accuracy of the universal MLPs turns out to be not yet sufficient for reaction search trials without any fine-tuning, lifelong MLPs can reach chemical accuracy. We propose an improved learning algorithm for lifelong adaptive data selection yielding efficient integration of new data while previous expertise is preserved.

Studying the anharmonicity in the infrared (IR) spectra of polycyclic aromatic hydrocarbons (PAHs) at elevated temperatures is important to understand vibrational features and chemical properties of interstellar dust, especially in the James Webb Space Telescope (JWST) era. We take pyrene as an example PAH and investigate how different degrees of superhydrogenation affects the applicability of the harmonic approximation and the role of temperature in IR spectra of PAHs. This is achieved by comparing theoretical IR spectra generated by classical molecular dynamics (MD) simulations and experimental IR spectra obtained via gas-phase action spectroscopy which utilizes the Infrared Multiple Photon Dissociation (IRMPD). All simulations are accelerated by a machine learning interatomic potential, in order to reach first principle accuracies while keeping low computational costs. We have found that the harmonic approximation with empirical scaling factors is able to reproduce experimental band profile of pristine and partially superhydrogenated pyrene cations. However, a MD-based anharmonic treatment is mandatory in the case of fully superhydrogenated pyrene cation for matching theory and experiment. In addition, band shifts and broadenings as the temperature increases are investigated in detail. Those findings may aid in the interpretation of JWST observations on the variations in band positions and widths of interstellar dust.

In recent years, rapid progress has been made in solid-state lithium batteries. Among various technologies, coating the surface of electrodes or electrolytes has proven to be an effective method to enhance interfacial stability and improve battery cycling performance. Recent experimental studies showed that gas-solid reactions offer a convenient approach to form modified coating layers on the solid electrolyte. Here, we performed computational simulations to investigate this surface reaction process. Specifically, we simulated the gas-solid reactions of Li$_6$PS$_5$Cl(LPSC) solid-state electrolytes in pure CO$_2$ and in mixed CO$_2$/O$_2$ atmospheres using ab-initio molecular dynamics (AIMD) and machine-learning force fields (MLFF)-accelerated molecular dynamics (MD) approaches. In the former case, LPSC surfaces primarily form Li$_2$CO$_2$S because it is difficult to dissociate another oxygen atom from the second CO$_2$ molecule. While in CO$_2$/O$_2$ mixed atmosphere, O$_2$ molecules preferentially adsorb onto LPSC, which supplies oxygen sites for subsequent CO$_2$ adsorption to form carbonate -CO$_3$ units. This reaction pathway ultimately generates an interfacial product dominated by Li$_2$CO$_3$. These coatings exhibit distinct electronic and ionic conductivity characteristics, allowing the possibility to control coating compositions and configurations by adjusting the gas-solid reactions. Key criteria for applying this strategy are extracted from the current research.

New routes for transforming nitrogen into ammonia at ambient conditions would be a milestone toward an energy efficient and economically attractive production route in comparison to the traditional Haber-Bosch process. Recently, the synthesis of ammonia from water and nitrogen at room temperature and atmospheric pressure has been reported to be catalyzed by Fe3O4 at the air-water interface. By integrating ambient pressure X-ray photoelectron spectroscopy and ab initio molecular dynamics and free energy calculations, we investigate the underlying reaction mechanisms governing ammonia and hydrazine formation at the water/Fe3O4/nanoparticle interface, laying the fundamental groundwork for future advancements in environmentally benign ammonia synthesis.

Oil-water emulsions resist aggregation due to the presence of negative charges at their surface that leads to mutual repulsion between droplets, but the molecular origin of oil charge is currently under debate. While much evidence has suggested that hydroxide ions must accumulate at the interface, an alternative perspective attributes the negative charge on the oil droplet to not an ionic species, but charge transfer of electron density from water to oil molecules. While the charge transfer mechanism is consistent with the correct sign of oil charge based on electrophoresis experiments, it is just as important to provide good estimates of the magnitude of the negative charge to explain emulsion stability. Here we show using energy decomposition analysis that the amount of net flow of charge from water to oil is negligibly small due to nearly equal forward and backward charge transfer through weak oil-water interactions, such that oil droplets would be unstable and coalesce, contrary to experiment. The lack of charge transfer also explains why vibrational sum frequency scattering reports a blue shift in the oil C-H frequency when forming emulsions with water, which arises from Pauli repulsion due to localized confinement at the interface.

In this article, we attempt to make a conceptual bridge between the research in biology, pre-biotic chemistry, biomimetics, and the tools used in organic bioelectronics in terms of materials and devices. The goal is discussing how materials and devices of organic bioelectronics can be exploited and used at the interface with biology, but also how, and at what extent, they can be adapted to mimicking nature-inspired properties, herein including unconventional computing strategies. The idea is to provide new hints and solid hypotheses for designing niche experiments that could benefit from a proper interaction, even at a basic communicative level, between materials science and biotechnology. The finale long-term vision goal being the vision of collecting experimental data that may help to made a step forward toward the implementation of the transition from inanimate objects to animated beings. The mathematical model canonically considered in this work is the Navier-Stokes-Nernst-Planck (NPNS) Model which is often used to model a charged continuum system such as the organic electrochemical transistors.

We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods.

We investigate the impact of intercalating a xenon layer between a thin condensed CD4 film of two monolayers (ML) and a platinum surface on the dissociative electron attachment (DEA). The observed desorption results are compared with density functional theory (DFT) calculations, which reveal the binding energies of various anionic and neutral species as a function of the xenon film thickness on the Pt (111) substrate. The theoretical results suggest that 6 ML of xenon are sufficient to diminish the surface effect, enabling physisorbed anionic fragments to desorb from the CD4 film. In contrast, 20 ML (approximately 10 nm) are experimentally necessary to achieve saturation in the desorption of D-. In addition, the presence of xenon layers enables the coupling of resonance states with Xe excited states, thereby inhibiting the electrons from returning to the metal. Aside from reducing surface interactions, the xenon interlayer significantly enhances DEA to CD4.

Emerging machine learning interatomic potentials (MLIPs) offer a promising solution for large-scale accurate material simulations, but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce. Here, we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals, namely naphthalene, anthracene, tetracene and pentacene. Through careful error propagation, we show that these potentials are accurate and enable the study of anharmonic vibrational features, vibrational lifetimes, and vibrational coupling. In particular, we investigate large-scale host-guest systems based on these molecular crystals, showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and guest nuclear motion. Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.

Despite their high accuracy, standard coupled cluster models cannot be used for nonadiabatic molecular dynamics simulations because they yield unphysical complex excitation energies at conical intersections between same-symmetry excited states. On the other hand, similarity constrained coupled cluster theory has enabled the application of coupled cluster theory in such dynamics simulations. Here, we present a similarity constrained perturbative doubles (SCC2) model with same-symmetry excited-state conical intersections that exhibit correct topography, topology, and real excitation energies. This is achieved while retaining the favorable computational scaling of the standard CC2 model. We illustrate the model for conical intersections in hypofluorous acid and thymine, and compare its performance with other methods. The results demonstrate that conical intersections between excited states can be described correctly and efficiently at the SCC2 level. We therefore expect that the SCC2 model will enable coupled cluster nonadiabatic dynamics simulations for large molecular systems.

This work presents a perturbative calculation methodology for evaluating the energy shifts and broadening of vibrational energy levels, caused by interactions between bound and unbound dissociative electronic states. The method is validated against previously semiclassical analyzed cases, demonstrating remarkable consistency. We successfully applied this approach to the N$_2$ molecule, which exhibits a strong spin-orbit interaction between the bound C$''^5\Pi_u$ and the repulsive 1$^7\Sigma^+_u$ electronic states, around 36 cm$^{-1}$. This interaction constitutes an major pathway for N($^{2}$D) production, important in both excitation and quenching in plasma afterglows. As a result, the maximum absolute shift of 0.15 cm$^{-1}$ was found for the C$''^5\Pi_u$ ($v$ = 7) and maximum broadening of 0.45 cm$^{-1}$ was calculated for $v$ = 8, demonstrating significant perturbation of the C$''^5\Pi_u$ by the 1$^7\Sigma^+_u$ state. The results obtained were compared with direct calculations of the predissociation rates of the C$''^5\Pi_u$ bound state, showing very good agreement.