Browse Articles

A rank ordering and analysis of nine resilience competencies demonstrates the special importance of thought management in maintaining resilience

Inverse design of drying-induced assembly of multicomponent colloidal-particle films using surrogate models

The properties of films assembled by drying colloidal-particle suspensions depend sensitively on both the particles and the processing conditions, making them challenging to engineer. In this work, we develop and test an inverse-design strategy based on surrogate modeling to identify conditions that yield a target film structure. We consider a two-component hard-sphere colloidal suspension whose designable parameters are the particle sizes, the initial composition of particles, and the drying rate. Film drying is simulated approximately using Brownian dynamics. Surrogate models based on Gaussian process regression (GPR) and Chebyshev polynomial interpolation are trained on a loss function, computed from the simulated film structures, that guides the design process. We find the surrogate models to be effective for both approximation and optimization using only a small number of samples of the loss function. The GPR models are typically slightly more accurate than polynomial interpolants trained using comparable amounts of data, but the polynomial interpolants are more computationally convenient. This work has important implications not only for designing colloidal materials but also more broadly as a strategy for engineering nonequilibrium assembly processes.

Development and preliminary validation of a predictive model for IgA nephropathy progression

Nitrile infrared intensity is more sensitive to local electric field change than its frequency: Application to probe protein hydration dynamics

The stretching vibration of a nitrile (C≡N) group gives rise to a sharp and strong infrared (IR) absorption band. Because the frequency (νCN) and molar extinction coefficient at νCN (εCN) of this vibrotational mode depend not only on the parent molecule but also on interactions that can affect the electronic distribution of the C≡N triple bond, it has found broad utility as an IR probe of the local environment of various chemical and biological systems. However, most previous studies only used the νCN to extract the information of interest. Herein, we show that the integrated molar extinction coefficient [∫ενdν] of the C≡N band has a stronger dependence on local electric field than its frequency and, hence, can be used to probe smaller environmental changes, as demonstrated in a proof-of-principle application. In addition, we find that for a series of C≡N IR bands that are measured with different aromatic nitriles or in different solvents, the νCN exhibits a linear correlation with the square root of the frequency normalized ∫ενdν, indicating that the underlying IR transition dipole moment is linearly dependent on the electronic interaction between the C≡N triple bond and the parent aromatic molecule.

Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies

A systematic methodology to develop bottom-up coarse-grained models for sequence-specific polypeptoids

Current developments in the precise synthesis of sequence-controlled polymers allow for new opportunities in designing materials with finely tunable properties. In particular, polypeptoids offer a robust platform for sequence-specific polymers that can be produced at gram scale and offer a range of sidechain chemistries that far exceed those of polypeptides and natural protein-based biopolymers. However, the vast chemical design space of polypeptoids demands high-throughput screening, which is not yet synthetically feasible. Moreover, the lack of large structural and property databases limits the development of AI-based predictive models. These challenges highlight the need for systematic, physics-based computational methods to understand and predict how sequence impacts the polypeptoid structure and material properties. Here, we create a multiscale simulation workflow to develop bottom-up coarse-grained (CG) peptoid models using the relative entropy approach, to create a library of peptoid monomers suitable for studying the CG models of a wide range of sequences in both long-chain and multi-chain simulations. Using a representative subset of peptoid chemistries, we validate the resulting CG models by comparison with all-atom simulations and experimental end-to-end distance measurements measured through double electron–electron resonance spectroscopy. This approach is encouraging for polymer platforms that lack large databases as it offers a bottom-up framework to navigate the vast sequence and chemistry space of sequence-defined polymers, enabling molecular-level insight and in silico screening of peptoid-based materials.

Development and validation of identification models for aortic dissection and non-ST-segment elevation acute coronary syndrome in the emergency department

Abstract Aortic dissection (AD) and non-ST-segment elevation acute coronary syndrome (NSTE-ACS) are critical illnesses whose prompt identification within the emergency department is challenging. This study aimed to establish rapid discrimination models to differentiate between these conditions. Patients of the training set and validation set were collected from January 2020 to June 2023. All patients used their final diagnosis. Discriminant models were constructed via univariate and multivariate logistic regression analyses. Based on the results of the two models, two web calculators were developed. A total of 1314 patients were included in the study, with 997 patients (399 AD patients and 598 NSTE-ACS patients) and 317 patients (132 AD patients and 185 NSTE-ACS patients) in the training and validation sets, respectively. The semi-model consisted of six clinical characteristics (age, heart rate, pulse pressure, temperature, hypertension, diabetes), with an area under the ROC curve (AUC) of 0.792 and 0.823 in the training and validation sets. The whole-model included five clinical characteristics (age, pulse pressure, hypertension, diabetes) and two point-of-care test data (high sensitivity troponin I, D-dimer). It had a higher predictive value compared to the semi-model, with AUCs of 0.973 and 0.980 in the training and validation sets, respectively. Given the optimal cutoff point, the semi-model demonstrated a sensitivity of 0.716 and a specificity of 0.734, whereas the whole-model displayed a sensitivity of 0.930 and a specificity of 0.946. Both identification models can be used as reliable tools for rapidly identifying AD and NSTE-ACS.

Terahertz and dielectric spectral calculations of aqueous CaCl2 solutions using polarizable models: Intra- and inter-species contributions involving permanent and induced dipoles and ion current

Intermolecular interactions, structure, and dynamics of aqueous CaCl2 solutions are investigated through calculations of the terahertz (THz) and dielectric spectra from molecular dynamics simulations using explicit polarizable models for both water and the ions. Calculations are performed for three different concentrations of CaCl2 in water at room temperature. For each system, the difference absorption spectrum shows several features in the THz region. We dissected the difference absorption spectrum further into ion–ion, water–water, and ion–water components. The ion–ion contribution is further dissected into cation–cation, anion–anion, and cation–anion self and cross correlation contributions. The ion–water terms are further separated into cation–water and anion–water correlations. These dissections provide detailed insights into ion–ion and ion–water correlations and dynamical behavior of the hydrated ions. We further separated the cation–water and anion–water contributions into permanent dipole–permanent dipole, permanent dipole–induced dipole, and induced dipole–induced dipole correlation components, which reveal the nature and magnitude of these interactions contributing to the ion–water THz spectrum. Our calculations of the anisotropy of induced dipole moments reveal that the presence of Ca2+ ions in the vicinity of Cl− ions increases the anisotropy of the induced dipole moments of the anions. The present study also reveals a significant presence of contact ion pairs in the CaCl2 solutions, especially at higher concentrations. We also explored the influence of heterogeneity of water hydrogen bonds around Ca2+ ions on THz spectral features of hydration shell water. We also calculated the low-frequency dielectric spectrum, including the static dielectric constant of each solution, which accounts for both dipole–dipole and current–dipole contributions.

Hollow polyaniline/pyrrole-chitosan supported Pt-Cu0.5Zn0.5Fe2O4 nanoparticles as an efficient electrocatalyst for enhancement of the methanol oxidation reaction

Nonvolatile switchable electromagnetically induced transparency in terahertz range

The terahertz frequency range has attracted significant interest for its potential in applications such as high-speed communication, sensing, and imaging. However, the dynamic control of terahertz waves remains a challenge. Here, we present a switchable metasurface incorporating the Ge–Sb–Te (GST) phase-change material, designed to achieve switchable electromagnetically induced transparency (EIT) in the terahertz frequency range. The metasurface features an array of gold stripes on a thin GST sublayer, with the quasi-BIC-related EIT mode arising from a hybrid symmetry-protected Friedrich–Wintgen supercavity mode. By carefully adjusting the geometric parameters of the gold stripes, the EIT bandwidth can be precisely tuned through the controlled asymmetry. The phase transition of GST induces a significant change in the metasurface electrical properties, enabling robust nonvolatile switching between transparent and opaque states of the device. Both theoretical analysis and experimental validation confirm the efficacy of this design for dynamic modulation in the terahertz regime, demonstrating its potential for advanced terahertz photonic applications.

Paramicrosphaeropsis eriobotryae as an emerging canker pathogen in pomegranate trees and the susceptibility of various cultivars

Measurement-efficient ADAPT-VQE with the SOAP parameter optimizer

Quantum computing on near-term noisy intermediate-scale quantum devices holds significant promise for simulating complex chemical systems. Among various variational quantum algorithms, the adaptive derivative-assembled pseudo-Trotter ansatz variational quantum eigensolver (ADAPT-VQE) is widely used for generating molecule-specific adaptive ansätze for different molecules, yet its measurement requirement is extensive, calling for suitable optimizers. In this study, we utilize the ADAPT-VQE algorithm enhanced by a powerful optimizer termed sequential optimization with an approximate parabola (SOAP) to calculate molecular energies. These computations are carried out through classical simulations using the TenCirChem software. Our results demonstrate the efficiency and robustness of the SOAP optimizer for ADAPT-VQE. Furthermore, we show that SOAP performs effectively across different ADAPT-VQE ansatz element pools. This work presents a strategy to mitigate the substantial measurement requirements associated with ADAPT-VQE.

Grid resilience enhancement of photovoltaic systems via Lyapunov-validated active–reactive power coordination and inverter oversizing

The Berry curvature in the framework of current density functional theory for molecules in external magnetic fields

In this work, we investigate the quantum geometry framework for molecules in external magnetic fields. For electronic ground states, the linear response formalism through which the quantum geometric tensor can be computed was described by Culpitt et al. [J. Chem. Phys. 156, 044121 (2022)], and this work expands their framework to current density functional theory. We show that for nuclear displacements, the Fubini–Study metric can be connected to the diagonal Born–Oppenheimer correction. Furthermore, we examine the effects of external magnetic fields on the molecular Berry curvature. For selected systems, we investigate how different density functional approximations compare to both full configuration interaction and Hartree–Fock theory. Finally, the convergence of the Berry curvature with respect to the numerical grid is estimated for different functionals, highlighting some known deficiencies of modern density functional approximations.

Molecular dynamics of water hexamer anions at cryogenic temperatures

We studied the dynamics of water hexamer anions [(H2O)6−] at cryoscopic temperatures using MP2 level ab initio molecular dynamics (AIMD) simulations. The vertical electron detachment energy (VDE) of these clusters varies in the 150–550 meV range in good agreement with experiments. The dominant characteristic pattern of the electron binding sites consists of a double hydrogen bond acceptor water molecule with two dangling hydrogen atoms in direct contact with the excess electron. In addition to surface localized excess electron clusters, we examine the dynamics of a hexamer model of the bulk hydrated electron. We analyze correlations between the binding strength of the excess electron and geometrical and spectroscopic properties, in particular, the radius of the excess electron and the bending frequencies of the electron binding water units. Our investigations were extended to the evaluation of nuclear quantum effects on the physical properties of the clusters by performing path integral molecular dynamics simulations on a neural network based potential energy surface and also using the generalized smoothed trajectory analysis method. Nuclear quantum effects at these low temperatures were found to be significant, as demonstrated by structural, energetic, and spectroscopic characteristics of the clusters. Most strikingly, the half-width of the quantum distributions of the VDE or the radius of the electron increases by a stunning factor of ∼5–10 relative to the classical ones. Molecular dynamics trajectories also reveal that, while all investigated isomers persist at 10 K in AIMD simulations, nuclear quantum effects promote isomerizations to more stable, lower lying minima.

Model driven adaptive design with concentration profiles

Effective kinetic models of heterogeneous catalytic processes are an indispensable tool for reactor design, optimization, and control. Under the assumption of using functional forms like power laws, model parameters are traditionally fitted to kinetic data measured along local line scans. A local line scan involves systematically varying one individual reaction parameter, such as a reactant concentration or temperature, at a time. This approach typically involves numerous separate kinetic measurements and is susceptible to the uncertainty of these line scans in determining the model’s parameters. Here, we explore the use of profile reactors in combination with a fully automated adaptive design approach for an efficient identification of effective kinetic models. Originally developed to provide operando information along the axis of tubular reactors, profile reactors provide a complex line scan that encapsulates kinetic information across all reaction conditions probed along the tube. The proposed Model-Driven Adaptive Design with Profiles algorithm harnesses this extensive dataset to strategically guide the selection of initial reaction conditions for subsequent profile reactor measurements. This approach ensures that each line scan provides maximally complementary information, thereby significantly enhancing the efficiency and accuracy of kinetic model identification.

Efficient calculation of crystal–solution coexistence lines for aqueous electrolytes

Electrolyte solutions have a widespread presence in biological, geological, and industrial systems. To advance our understanding of these solutions, we need to develop theoretical models that can efficiently predict their collective properties. In this work, we present a novel workflow for computing the phase diagrams of electrolyte systems described by classical force fields, using free-energy calculations from molecular dynamics simulations. We show that this approach is significantly more efficient than commonly employed direct coexistence (interfacial) simulation methods. In particular, we apply this “chemical potential route” to obtain the NaCl crystal–aqueous solution phase diagrams for both pure and hydrated crystals for two parameterizations of the Madrid scaled-charge force field. We show that the original model parameterization achieves state-of-the-art performance in predicting NaCl–water phase behavior at 1 bar within the temperature range of 250–350 K and predicts a stable hydrohalite (NaCl · 2H2O) crystal at temperatures below 250 K. Our approach enables potential future computational studies of hydrohalite nucleation.

From cationic to anionic Al12B and Al13 clusters. Aromatic characteristics for intermediate superatomic species

Al13- remains a prototypical superatomic cluster, featuring 40-cluster electrons (ce), fulfilling a closed shell electronic structure with spherical aromatic characteristics. Here, we evaluated intermediate counterparts, given by Al13+ (38-ce) and neutral Al13 (39-ce), which can be controlled by the use of different n- and p-type organic substrates, exhibiting a decreased spherical aromatic behavior. In addition, the boron-doped isoelectronic counterparts show similar characteristics. For both cationic and neutral clusters, a contrasting magnetic behavior is observed upon different orientations of the external field, in line with the decrease in spherical aromatic characteristics, resulting in a variation of the inherent magnetic anisotropy. Moreover, despite the decrease in spherical aromaticity, their characteristics remain, leading to these intermediate superatomic clusters being also depicted as stable building blocks toward the formation of molecular-based materials, offering prototypical examples of how superatomic clusters behave after interaction with substrates.

Dual-metal porphyrin–graphene hybrids as oxygen catalysts: Comparative DFT insights into O2 adsorption and activation

The efficient activation of molecular oxygen (O2) underpins electrochemical energy conversion; however, the design strategies for non-precious catalysts for the oxygen reduction reaction remain incomplete. Transition metal porphyrins supported on conductive substrates offer a versatile platform, but the mechanism by which different metal centers cooperate to control O2 activation is not well understood. In this work, we used density functional theory to explore heterometallic (Fe, Mn) porphyrin–graphene hybrids and reveal the decisive role of axial–core metal synergy. Across FeTPyP–Fe/Gr, FeTPyP–Mn/Gr, and MnTPyP–Fe/Gr, axial (bridging) sites consistently promote stronger O2 binding, greater charge transfer, and more pronounced weakening of the O–O bond than their core counterparts. Electronic-structure analysis showed that this effect arises from enhanced orbital overlap and π* occupation at the axial position, while Mn incorporation tunes the ligand field to further optimize O2 activation. The most effective configuration combines axial Fe binding with Mn-mediated electronic modulation, demonstrating that the complementary roles of distinct metals can be harnessed in a single catalytic architecture. These findings provide mechanistic insights into oxygen reduction and establish clear design principles for the engineering of earth-abundant porphyrin catalysts. More broadly, they highlight heterometallic coordination as a powerful strategy for tailoring molecular electrocatalysts for sustainable energy conversion.

Comparative study of conformational behavior of hydroxyl-terminated carbosilane dendrimers at water–toluene and water–air interfaces

In this study, we investigate how molecular density—governed by dendrimer generation and branching functionality—influences the conformational behavior and hydrogen bonding of OH-terminated carbosilane dendrimers in water, air, toluene, and at water–air and water–toluene interfaces by atomistic molecular dynamics simulations. We focus on the 4-3 series (G2–G4), featuring a tetrafunctional core and trifunctional branching, and compare it with the denser, more rigid 4-4G3 dendrimer of the third generation (tetrafunctional at both core and branching points). In hydrophobic environments, terminal OH groups form linear intramolecular aggregates; the 4-4G3 exhibits markedly reduced toluene uptake (10% vs 40% volume change for 4-3G4) and severely restricted intramolecular dynamics, with some OH groups remaining kinetically trapped near the core—a phenomenon requiring microsecond-scale simulations for proper characterization. In aqueous solution, 4-3 dendrimers expose OH groups at their periphery to form hydrogen bonds with water, whereas 4-4G3 retains a significant fraction of OH groups internally, forming intramolecular H-bonds instead. At interfaces, 4-3 dendrimers adopt flattened “umbrella” conformations to maximize interfacial H-bonding with water, swelling slightly into toluene to form biconvex shapes, while 4-4G3 remains nearly spherical due to steric constraints, forming over four times more intramolecular H-bonds and fewer with water than the more flexible 4-3G4. These findings establish molecular density as a key determinant of solvation, dynamics, and interfacial adaptability, providing a foundation for understanding structure–composition–property relationships in dendrimer monolayers under lateral confinement.