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Rotationally inelastic scattering of cyanocyclopentadiene by helium atoms
In the interstellar medium (ISM), polycylic aromatic hydrocarbons (PAHs) are believed to be an important carbon reservoir, accounting for up to a quarter of all interstellar carbon in our galaxy. This makes the investigation of their potential formation precursors highly relevant in the context of ISM chemistry. This, in turn, includes knowing the abundance of the precursor species. One of the possible precursor molecules for PAHs is the recently detected cyanocyclopentadiene, c-C5H5CN. Given the physical conditions of the dense dark molecular cloud TMC-1 where the cyclic species was detected, it is crucial to consider that local thermodynamic equilibrium conditions may not be satisfied. In such case, an accurate estimation of the molecular abundance involves taking into account the competition between the radiative and collisional processes, which requires the knowledge of rotational excitation data for collisions with the most abundant interstellar species—He or H2. In this paper, the first potential energy surface (PES) for the interaction of the most stable isomer of cyanocyclopentadiene (1-cyano-1,3-cyclopentadiene) with He atoms is computed using the explicitly correlated coupled-cluster theory [CCSD(T)-F12]. The obtained PES demonstrates high anisotropy and is characterized by a global potential well of −101.8 cm−1. Scattering calculations of the rotational (de-)excitation of 1-cyano-cyclopentadiene induced by He atoms are performed with the quantum mechanical close-coupling method for total energies up to 125 cm−1. The resulting rotational state-to-state cross sections are used to compute the corresponding rate coefficients for temperatures up to 20 K and propensity rules are also discussed.
Efficient dye removal from aqueous solution using a hybrid GA@ZnO-AC nanocomposite
State-specific dissociation dynamics on a global potential energy surface of N(4S<i>u</i>) + C2(a3Π<i>u</i>): Machine learning-driven molecular simulations
The dissociation dynamics of N(4Su) + C2 (a3Πu) under hypersonic conditions is critical for modeling radiative heating in aerospace thermal protection systems, yet it remains unexplored due to computational limitations. This work focuses on the systematic study of N + C2 collision-induced dissociation (CID) processes using molecular dynamics simulations with 50 000 quasi-classical trajectories (QCT) per rovibrational state on a global 12A″ potential energy surface to obtain cross sections (CS) and state-specific thermal rate coefficients (1000–20000 K). The results demonstrate that vibrational excitation dominates dissociation dynamics, with vibrational quantum states significantly lowering energy barriers and facilitating bond dissociation at reduced collision energies. In addition, we integrate quasi-classical trajectory simulations with neural networks, utilizing simulation datasets per rovibrational state to train our genetic-algorithm-optimized neural network. This framework achieves 99% prediction accuracy (R2 = 0.99) across all validation sets for state-specific dissociation CS and rate coefficients. We develop an effective CS model through machine learning (ML) to generate rate coefficient datasets covering all possible rovibrational levels. This machine learning approach reduces computational costs by three orders of magnitude compared to direct QCT calculations. Finally, this study further examines the proposition by Truhlar et al. regarding the influence of multi-electronic-state potential energy surfaces on the dissociation rate coefficients, including the application of modified rate-coefficient formulas to the current system. This study bridges the gap in molecular dynamics (MD) simulations for this system. Furthermore, it demonstrates that the combined MD and machine learning approach can generate comprehensive datasets from limited discrete data points. This creates a high-quality database for modeling non-equilibrium hypersonic flows and highlights the benefits of machine learning in reactive MD simulations.
Horse model of spontaneous atrial fibrillation share proteomic changes with humans
Deep potential-driven molecular dynamics of CO ice analogs: Investigating desorption following vibrational excitation
We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited vibrational states, up to approximately v = 40. The MLP is based on the combination of high-dimensional neural network atomic potentials using the DeePMD-kit package, trained on prior ab initio molecular dynamics data, with selective treatment of the excited molecule, allowing us to capture complex energy redistribution dynamics in condensed-phase environments. In particular, the MLP is capable of accurately describing the desorption process of a single CO molecule within an aggregate of 50 CO molecules, in excellent agreement with both previous theoretical predictions and experimental measurements. The MLP provides a much finer description of the translational and rotational energy distributions, capturing their character with high fidelity and allowing a more detailed comparison with experimental results. Furthermore, the analysis of the rotational energy, resolved over specific translational energies, revealed new insights into the coupling between translational and rotational degrees of freedom during the photodesorption process. This novel approach opens new perspectives for extensive statistical studies on desorption energies and detailed investigations of surface molecule excitations and the exploration of larger-scale models incorporating periodic boundary conditions to simulate more realistic CO aggregates.
Machinability analysis in wire-EDM of cryogenically treated Ti6Al4V alloy and multi-objective optimization using MOAVOA and MOGA
How a fraudulent scientist faked his career and other cautionary tales: Books in brief
Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La–Si–P systems
While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 2007, Behler and Parrinello first proposed and demonstrated an artificial neural network machine learning (ANN-ML) scheme, opening a new paradigm for developing accurate and efficient interatomic potentials for reliable MD simulation studies of the thermodynamics and kinetics of materials. In this paper, we show that an accurate and transferable ANN-ML interatomic potential can be developed for MD simulations of the La–Si–P system. The crucial role of training data in the ML potential development is discussed. The developed ANN-ML potential accurately describes not only the energy vs volume curves for all the known elemental, binary, and ternary crystalline structures in the La–Si–P system but also the structures of La–Si–P liquids with various compositions. Using the developed ANN-ML potential, the melting temperatures of several crystalline phases in the La–Si–P system are predicted by the coexistence of solid–liquid phases from MD simulations. While the ANN-ML model systematically underestimates the melting temperatures of these phases, the overall trend agrees with experiment. The developed ANN-ML potential is also applied to study the nucleation and growth of LaP as a function of different relative concentrations of Si and P in the La–Si–P liquid, and the obtained results are consistent with experimental observations.
A cost effective real time rail track monitoring system leveraging multi sensor fusion and multi objective optimization
Net zero needs AI — five actions to realize its promise
Surface local strain and diatomic iron doping synergistically promote the plasmonic photoelectrochemical nitrogen reduction reaction
Utilizing single/dual atom catalysts as well as the crystallographic plane effect to promote the nitrogen reduction reaction (NRR) has attracted extensive attention. However, there are few works to combine the two effects with the surface plasmon resonance effect based on the first-principles periodic slab models. Our calculated results can be summarized as three points. First, we investigated the adsorption Gibbs free energy (ΔG) of *N2 and *NNH on Au(100), Au(110), and Au(111) facets with single or dual Fe atoms doped. The strongest end-on and side-on adsorption occur at single and dimer Fe atoms doped Au(100) surfaces, respectively. More importantly, the N2 side-on adsorption configuration significantly reduces the ΔG of the first hydrogenation step. Second, we investigated the reason why there is a lower adsorption ΔG for the N2 side-on adsorption on dimer-Fe-doped Au(100). It was discovered that the interatomic distance of the Fe dimer exhibits a strong correlation with adsorption strength. When the Fe dimer is doped on the Au(100) surface, the dimer will relax due to local strain, enabling a stronger N2 side-on adsorption. Finally, we compared the photon absorption capacity between side-on and end-on nitrogen adsorption configurations, revealing that the latter exhibits enhanced light absorption capability at lower photon energies and across broader wavelength ranges. This theoretical research highlights the superiority of the N2 side-on adsorption configuration in plasmonic photoelectrochemical NRR and shows the influence of surface local strain and dynamic relaxation for side-on adsorption, which could be referenced to design efficient side-on adsorption catalysts.
Multi-objective optimization design approach for prefabricated buildings to minimize cost, duration and carbon emissions using ant colony algorithm
Quantum effects in electric-field–driven dissociation of liquid hydrogen fluoride
The inclusion of Nuclear Quantum Effects (NQEs) in molecular dynamics simulations is increasingly recognized as essential for accurately modeling systems involving light nuclei, particularly hydrogen. Classical approaches, such as standard ab initio molecular dynamics (AIMD), are not suited to predict key quantum-mechanical phenomena such as zero-point motion and proton tunneling, which can critically influence the structural behavior. This is especially true in H-bonded systems, where the strength and directionality of interactions are highly sensitive to quantum delocalization. In this work, we investigate the impact of NQEs on liquid hydrogen fluoride (HF) at standard conditions and subjected to strong external electric fields by comparing classical nuclei AIMD and path-integral AIMD simulations. HF presents a rich H-bonding network and strong molecular dipole moments, leading to the manifestation of important NQEs even in the absence of the field. Furthermore, our results demonstrate that quantum effects significantly alter the response of bulk liquid HF to applied electric fields, leading to enhanced proton delocalizations favoring the protolysis reaction 2 HF ⇌ H2F+ + F−. Similarly to water, indeed, the inclusion of NQEs lowers by one-third the field threshold necessary for dissociating HF molecules [i.e., 0.15 V/Å (classical) vs 0.05 V/Å (quantum)] and increases proton mobility. These differences become particularly evident under moderate-to-strong field strengths, where quantum simulations predict molecular dissociation and Grotthuss diffusion processes that are either absent or underestimated in classical AIMD simulations, though the general mechanism for proton migration is unaltered by the inclusion of quantum effects.
Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet
Dual modulation of the anion-driven thermodynamic properties of aqueous choline halide-based deep eutectic solvents
Deep eutectic solvents (DESs) are considered tunable solvents because their specific properties can be achieved based on the choice of components and their relative concentrations in a mixture. In this work, we investigate the influence of the variation in halide ions (F−, Cl−, Br−, I−) of choline salts used on the thermodynamic and physicochemical properties of choline halide-based DESs. Our findings show that the density of choline halide-based DESs decreases nonlinearly with an increasing mole fraction of water, following a trend based on the size of the halides, with choline iodide showing the highest density. Temperature-dependent density data reveal that the thermal expansion coefficient decreases slightly with increasing water content, indicating a more stable volume at a higher mole fraction of water. The excess molar volume (VE) of the DES mixtures exhibits complex behavior depending on the choline halide used, with both negative and positive VE values observed across different water mole fractions. These variations are linked to the hydrogen bonding interactions between the DES components and water molecules. In addition, viscosities decrease with increasing water content, suggesting the disruption of hydrogen bonding networks and enhanced mobility of the ions, which contributes to the observed increase in conductivity. The excess molar Gibbs energies, enthalpies, and entropies of activation have also been determined.
Masked pretraining of U-Net for ultrasound image segmentation
Viscosity of mixed lipid bilayers: Comparison between atomistic and coarse grained models
Membrane fluidity, often characterized by surface viscosity, plays a critical role in regulating a wide range of biological processes, including protein diffusion and function, receptor binding, signal transduction, enzyme and drug interactions, and cellular events such as membrane fusion. While the viscosity of single-component lipid membranes has been extensively studied, significantly less is known about how compositional heterogeneity influences surface viscosity in mixed lipid bilayers. In this work, we employ both atomistic and coarse-grained molecular dynamics simulations to examine surface viscosity trends in binary lipid mixtures composed of lipids with distinct structural characteristics: (i) mismatch in acyl chain lengths, (ii) opposite spontaneous curvatures, and (iii) variations in head group structure. The simulation data are analyzed using the Redlich–Kister model to quantify deviations from ideal mixing behavior. Our findings reveal that the greatest non-ideality in surface viscosity arises when the constituent lipids exhibit opposite spontaneous curvatures. Notably, coarse-grained models fail to capture the correct viscosity trends in systems with more complex intermolecular interactions, indicating that interfacial friction is not accurately represented at reduced resolution.
A simple and effective approach for body part recognition on CT scans based on projection estimation
Aperiodic defects in periodic solids
To date, computational methods for modeling defects (vacancies, adsorbates, etc.) have relied on periodic supercells in which the defect is far enough from its repeated image that they can be assumed non-interacting. Yet, the relative proximity and periodic repetition of the defect’s images may lead to spurious, unphysical artifacts, especially if the defect is charged and/or open-shell, causing a very slow convergence to the thermodynamic limit (TDL). In this article, we introduce a “defectless” embedding formalism such that the embedding field is computed in a pristine, primitive-unit-cell calculation. Subsequently, a single (i.e., “aperiodic”) defect, which can also be charged, is introduced inside the embedded fragment. By eliminating the need for compensating background charges and periodicity of the defect, we circumvent all associated unphysicalities and numerical issues, achieving a very fast convergence to the TDL. Furthermore, using the toolbox of post-Hartree–Fock methods, this scheme can be straightforwardly applied to study strongly correlated defects, localized excited states, and other problems for which existing periodic protocols do not provide a satisfactory description.