Browse Articles
Discover research articles across all indexed journals
Noniterative triple excitations contributions in equation-of-motion coupled-cluster theory for singlet and triplet excitation energies of closed-shell systems
In this work, we report the implementation of the equation-of-motion coupled-cluster method with noniterative triple excitations contributions [EOM-CCSD(T)(a)*] for spin-singlet and spin-triplet excited states based on a closed-shell reference. We also propose the l-EOM-CCSD(T)(a)* method, where {R1, R2} is approximated by {L1, L2} in EOM-CCSD(T)(a)*. In the implementation, spin and spatial symmetries are exploited to reduce computational cost. Vertical and adiabatic excitation energies, numerical energy gradients, optimized structures, and harmonic frequencies for some low-lying states of a series of organic molecules have been calculated using different EOM-CC methods. Results show that EOM-CCSD(T)(a)* and l-EOM-CCSD(T)(a)* are able to provide a highly accurate description of excited states, and differences between their singlet excitation energies and results of CCSDR(3) are very small. l-EOM-CCSD(T)(a)* has a lower computational cost and is recommended for calculating excited-state properties of organic molecules. In addition, using the CCSD(T) energy as the ground-state energy in EOM-CCSD(T)(a)* can improve excited-state properties.
Carotid atherosclerotic plaque vulnerability assessment from angiography-derived radial wall strain validated by MRI
State specificity in association reaction of H3+ with H2
The three-body association reaction of H3+ with H2 assisted by helium and H2 was studied using a 22-pole radiofrequency ion trap instrument in the temperature range of 15–35 K. The role of the rotational excitation of H2 was investigated in measurements with normal and para-enriched H2. From the dependence of the measured three-body reaction rate coefficients on the character of the third body and on the H2 nuclear spin state, we found that rotationally excited (normal) H2 is less efficient in stabilizing the (H5+)* intermediate complex than both He and ground-state (para) H2. The lifetime of the (H5+)* complex was found to be ∼3 times higher in para-enriched H2 than in normal H2.
Inulin supplementation modulates gut microbiota derived metabolites related to brain function in children with obesity
Abstract The gut microbiota plays a key role in regulating energy balance via gut-brain axis (GBA). Dysbiosis can disrupt this communication, contributing to obesity. This study aimed to assess the effects of inulin supplementation on GBA-related amino acids and bioactive molecules in children with obesity. Children aged 7–15 were randomly assigned to 3 treatment groups for 6 months: inulin supplementation, isocaloric maltodextrin (placebo), or dietary fiber advice. Plasma amino acids and bioactive molecules were analyzed using LC-MS/MS at baseline and month 6. Relationships of changes in GBA-related compounds with changes in gut microbiota were evaluated. By month 6, principal component analysis trajectories showed clustering across all groups, involving 154 children, but indicated potential metabolic shifts, particularly in the inulin group. S-plots identified significant changes in GBA-related compounds, with only the inulin group showing marked increases in putrescine, spermine, and tyrosine from baseline (all P < 0.0001). Inulin supplementation significantly upregulated putrescine over time compared to the placebo group (P = 0.021), suggesting enhanced GBA communication. Changes in specific GBA-related compounds in the inulin group were significantly associated with gut microbiota changes. These findings indicate that inulin effectively modulates GBA-related bioactive molecules, potentially mediating its effect on childhood obesity management through putrescine, spermine, and tyrosine. Clinical Trial Registry number: NCT03968003. Registered 30/05/2019.
Chemical reactivity from linear response eigenfunctions and eigenvalues
The atom-condensed linear response function matrix can be diagonalized, and the associated eigenvectors thus obtained form a complete basis set on which any density deformation can be projected. In this paper, one proposes to interpret these vectors as atom condensed Electron Density Deformation Modes. Each eigenvector represents a mode describing how electron density can deformed in response to an external potential perturbation. It is hypothesized that these modes can reveal the preferred direction or pattern of electron flow, helping identifying the reactive region of a molecule. A relationship between electron density polarization energy and hardness variation is also derived. Practical applications on organic reactions are provided.
Effectiveness of supervised machine learning models for electrical fault detection in solar PV systems
Abstract Even though Photovoltaic (PV) systems have emerged as a viable substitute for non-renewable energy sources, their widespread integration into the electrical grid presents several issues today. On the other hand, various faults are a key concern affecting PV plants’ production and longevity. The current study uses Machine Learning (ML) algorithms such as Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM) and XGBoost to detect and classify PV errors corresponding to Short Circuits (SC), Open Circuits (OC), Ground Faults (GF), and Mismatch Faults (MF). Simulations were conducted in MATLAB/Simulink to analyse voltage, current, and power variations during fault conditions and study their impact. The proposed results show that the effectiveness of ML in electrical fault detection, with the following classification accuracies: SVM – 97.40%, DT– 97.20%, RF – 97.20%, NB – 97.60%, and XGBoost – 98.0%. The effectiveness of the classification is confirmed through confusion matrices and correlation heatmaps. This research highlights the need for integrating intelligent monitoring, real-time IoT-based detection, and prediction analytics to improve PV system reliability.
On the connections between microcanonical and canonical ensemble dynamics in liquids
The connection between dynamics in the canonical and microcanonical ensembles is explored in the context of computing activation energies and non-Arrhenius effects. Considering the diffusion coefficient, D, as an example, we show that simple relationships exist between the energy-dependent diffusion coefficient, D(E), the corresponding constant temperature diffusion coefficient, D(T), its activation energy, and the temperature derivative of the activation energy, a key measure of non-Arrhenius behavior. These relationships are used to propose a new approach to calculating activation parameters within the framework of fluctuation theory for dynamics. This method enables more rapid convergence of activation energies and their temperature derivatives, as illustrated for water self-diffusion in both neat water and aqueous TMAO solutions. We also demonstrate that the convergence can be further accelerated by using a rejection algorithm to uniformly sample energies to characterize D(E).
Origin of Umm Al Heesh lake in the Rub’ Al Khali desert, Saudi Arabia
Exact closed-form expressions for unitary spin-adapted fermionic singlet double excitation operators
We derive exact closed-form expressions for the matrix exponential of the anti-Hermitian spin-adapted singlet double excitation fermionic operators. These expressions enable the efficient implementation of such operators within unitary product state frameworks targeting conventional hardware and allow for the implementation of ansätze that guarantee convergence to specific spin symmetries. Moreover, these exact closed-form expressions might also lay the groundwork for constructing spin-adapted circuits for quantum devices.
Influence of subretinal drusenoid deposit on retinal sensitivity in age-related macular degeneration
Electron affinities of the actinide atoms from relativistic coupled cluster
The electron affinities (EAs) of the actinide atoms Ac–Pu and Bk–Lr have been calculated using the CCSD(T) method with sequences of large correlation consistent basis sets extrapolated to the complete basis set limit. The Am and Cm systems were too multideterminantal to be amenable to the present treatment. Spin–orbit effects have been included variationally using relativistic 4-component CCSD(T) throughout, and both 6d and 7p electron attachments have been investigated. The small effects due to quantum electrodynamics have also been included in all cases at the Dirac–Hartree–Fock level of theory. In a few cases (Ac, Th, Pu, No, and Lr), it was possible to include contributions from correlation beyond the CCSD(T) level of theory up to CCSDT(Q). The latter was particularly important for the 6d electron affinities of both Ac and Th. For the early actinides Ac through Np, stable anions are predicted for both 6d and 7p electron attachments, but except for the Th atom, the largest EAs occur when the additional electron is attached to the 7p orbital. The Pu atom is predicted to only slightly bind an electron with an EA of just 0.63 kcal/mol, while Bk–No are predicted to not bind an electron within the accuracy of the present calculations. In the two cases where accurate experimental values exist, Th and U, the agreement with the present results are within 0.1 and 0.3 kcal/mol, respectively, although the CCSD(T) level of theory is not sufficient to unambiguously determine the ground state of U−.
Realtime monitoring of internal morphology of gel samples during drying process by X-ray computing tomography image using synchrotron radiation
Abstract This study was conducted to evaluate the change in the internal morphology of a wheat-based gel sample subjected to a drying process using X-ray computed tomography (CT) imaging with synchrotron radiation. To monitor the process in real time, the instrument was equipped with a developed drying system. The water contents of the sample decreased from around 0.42 to 0.1 g-water/g-dry solid and the falling rate drying was observed in the 0–180 min of drying period. The mechanical properties of the gel sample, represented by the Young’s modulus, increased exponentially during the drying period. Thus, when subjected to low-water-content conditions, the hardness of the gel progressed sufficiently. In the X-ray CT images, small voids were generated uniformly in the sample during the early drying process, and the total number of voids and their volume also increased. Finally, they formed a band-like structure at the center of the sample. The total number of voids decreased at a rate of increase with the confluence of voids, and eventually reached a plateau. These results suggest that voids may be connected and cracked during drying. It was found that the drying process of a gel sample in this study proceeds in two phases: a void growth phase up to 60–80 min and thereafter, a crack generation phase. Consequently, the internal morphology of the gel was visualized using synchrotron-based X-ray CT imaging in real time, and it was demonstrated that the method is very effective for monitoring a gel during the drying process.
Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions
In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential, for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We demonstrate that the explicit incorporation of dispersion interactions via D2 and D3 corrections significantly improves the accuracy of MLIPs when the cutoff radius is set to the commonly used value of 5–6 Å. We also show that for carbon tetrachloride and methane, a substantial improvement in accuracy can be achieved by extending the cutoff radius to 7.5 Å. However, for accurate modeling of toluene, the explicit incorporation of dispersion remains important. Furthermore, we find that MLIPs incorporating dispersion interactions via D2 reach a level of accuracy comparable to those incorporating D3, implying that D2 is suitable for accurate modeling of the systems in the study, while being less computationally expensive. We benchmarked the accuracy of the MLIPs on dimer binding curves compared to ab initio data and on predicting density and radial distribution functions compared to experiments.
Machine learning approaches overcome imbalanced clinical data for intraoral free flap monitoring
Redundant parameter dependencies in conventional and quantum linear response and equation of motion theory for unitary parameterized wave functions
Extracting molecular properties from a wave function can be performed through the linear response (LR) formalism or, equivalently, the equation of motion (EOM) formalism. For a simple model system, He in a 6-31G basis, it is shown here that calculated excitation energies depend on the specifically chosen orbitals, even when the ground-state is the FCI solution, if the LR is truncated to a singles expansion. This holds for naïve, projected, self-consistent, and state-transfer parameterizations of the LR operators. With a focus on the state-transfer parameterization, this problem is shown to also hold for more complicated systems and is also present when the LR is truncated to singles and doubles. This problem can be alleviated by performing a ground-state constrained trace optimization of the Hessian matrix before performing the LR calculation. It is finally shown that spectra can be further improved for small LR expansions by targeting only a few states in the constrained trace optimization using constrained state-averaged UCC.
Specialized pro-resolving mediators alleviate inflammation and cartilage breakdown in vitro and may play a pivotal role in platelet-rich plasma
Submatrix and GPU-accelerated implementation of density matrix tight-binding
Effective single-particle theories, such as Hartree–Fock, density functional theory, and tight-binding, are limited by the computational cost of the self-consistent field (SCF) procedure, which typically scales cubically with the system size. This makes large-scale applications impractical without specialized algorithms and hardware. Here, we present the submatrix and graphical processing unit (GPU)-accelerated software implementation of the PTB tight-binding potential, realized in the open-source ptb codebase [M. Mueller, A. Katbashev, and S. Ehlert (2025). “grimme-lab/ptb: v3.8.1,” Zenodo. https://zenodo.org/records/17015872]. We first benchmark a traditional diagonalization-based SCF solver against density-matrix-based purification approaches, systematically varying both system size and computer hardware. Our findings show that the usage of GPUs permits shifting the boundaries to much larger systems than previously thought feasible, achieving an overall 10–15-fold performance speedup. Second, we introduce the implementation of a decomposition-type submatrix method, specifically designed for efficient operation on mid- to large-sized systems, to address the computational overhead associated with full-system diagonalization. We demonstrate that, from a certain dimension (≈104 basis functions) on, our submatrix method reduces the overall computational cost while maintaining acceptable numerical accuracy. Our study demonstrates the significance of the interplay between modern hardware, algorithmic considerations, and novel tight-binding methods, paving the way for further development in this direction.
Estimation of woody vegetation biomass in Australia based on multi-source remote sensing data and stacking models
Complete scaling theory applied to light scattering measurements of nitrobenzene + alkane liquid–liquid phase separation behaviors
Complete scaling theory expresses universal critical scaling fields as analytical combinations of all relevant experimental fields, both independent and dependent. Its utility has been demonstrated in the analysis of asymmetric liquid–liquid coexistence curves. Here, applications of complete scaling theory are extended to two other physical observables in liquid–liquid systems: light scattering and wetting properties. Coexistence curves for the nitrobenzene + n-hexane, +n-decane, and +n-tetradecane systems were measured using a synthetic method approach. Critical compositions ZC for these systems were determined from 90° light scattering data using three different choices of experimental composition coordinate. For each system, the ZC values became independent of composition choice when the nonanalytic complete scaling coefficients D2, found from fitting the coexistence curves, were incorporated into the analyses of the light scattering data through a transformation of the experimental composition coordinate. In addition, divergent wetting layer formation at the liquid/vapor interface on the nitrobenzene-rich side of the coexistence curve was observed in all three systems. After making the complete scaling correction to the composition coordinate, the growth behaviors of the wetting layers were identical to each other and to those seen in other systems. Values of the critical mole fraction xC, the critical temperature TC, and the complete scaling field mixing coefficient a1 for the three systems are reported and compared with the literature.