Browse Articles

Spontaneous Formation of Single-Crystalline Spherulites in a Chiral 2D Hybrid Perovskite

How to address solar geoengineering’s transparency problem

The divide expand consolidate scheme for unrestricted second order Møller–Plesset perturbation theory ground state energies

The linear scaling divide–expand–consolidate (DEC) framework is expanded to include unrestricted Hartree–Fock references. By partitioning the orbital space and employing local molecular orbitals, the full molecular calculation can be performed as independent calculations on individual fragments, making the method well-suited for massively parallel implementations. This approach also incorporates error control through the fragment optimization threshold (FOT), which maintains precision and consistency throughout the calculations. A benchmark was conducted for correlation energies of open-shell systems and the relative energies of both open- and closed-shell molecules at the MP2 level of theory. The full calculation result is achieved as the FOT approaches zero. For correlation energies, an FOT of 10−3 is sufficient to recover over 98% of the full result in all cases. However, for relative energies and the electronic energy component of oxidation potentials, a tighter FOT of 10−4 is required to keep the DEC error within 10% for both open- and closed-shell molecules. This is likely due to a lack of systematic error cancellation for the molecules with vastly different chemical natures. Therefore, for accurate relative energies, the FOT should be an order of magnitude lower, and additional caution is needed, particularly for large systems. The DEC method extension to unrestricted references maintains favorable features of linear scaling and can be implemented in a massively parallel algorithm to calculate correlation energies for large open-shell systems.

Highly accurate real-space electron densities with neural networks

Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning Ansätze to obtain highly accurate wave functions free of basis set errors and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties.

Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation

Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Inspired by the robust information maintenance observed in higher cortical areas such as the prefrontal cortex, despite substantial inherent noise, we investigated the effects of random noise on RNNs across different cognitive functions, including working memory. Our findings reveal that random noise not only speeds up training but also enhances the stability and performance of RNNs on working memory tasks. Importantly, this robust working memory performance induced by random noise during training is attributed to an increase in synaptic decay time constants of inhibitory units, resulting in slower decay of stimulus-specific activity critical for memory maintenance. Our study reveals the critical role of noise in shaping neural dynamics and cognitive functions, suggesting that inherent variability may be a fundamental feature driving the specialization of inhibitory neurons to support stable information processing in higher cortical regions.

<scp>pyVPT2</scp>: Interoperable software for anharmonic vibrational frequency calculations

We present pyVPT2, a program to perform second-order vibrational perturbation theory (VPT2) computations to obtain anharmonic vibrational frequencies. This program is written in Python and can utilize any of the several quantum chemistry programs that have been interfaced to the QCEngine project of the Molecular Sciences Software Institute (MolSSI). The requisite single point energy, gradient, or Hessian computations can be automatically performed in a distributed-parallel fashion by optionally using the MolSSI’s QCFractal software. With pyVPT2, VPT2 anharmonic frequencies can now be computed using quantum chemistry programs that lack their own VPT2 capabilities.

Mpemba effect in the relaxation of an active Brownian particle in a trap without metastable states

We explore the role of activity in the occurrence of the Mpemba effect within a system of an active colloid diffusing in a potential landscape devoid of metastable minimum. The Mpemba effect is characterized by a phenomenon where a hotter system reaches equilibrium quicker than a colder one when both are rapidly cooled to the same low temperature. While a minimal asymmetry in the potential landscape is crucial for observing this effect in passive colloidal systems, the introduction of activity can either amplify or reduce the threshold of this minimal asymmetry, resulting in the activity-induced and suppressed Mpemba effect. We attribute these variations in the Mpemba effect to the effective translational shift in the phase boundaries, which occurs as activity is changed.

Kohn–Sham inversion for open-shell systems

Methods based on density-functional theory usually treat open-shell atoms and molecules within the spin-unrestricted Kohn–Sham (KS) formalism, which breaks symmetries in real and spin space. Symmetry breaking is possible because the KS Hamiltonian operator does not need to exhibit the full symmetry of the physical Hamiltonian operator, but only the symmetry of the spin density, which is generally lower. Symmetry breaking leads to spin contamination and prevents a proper classification of the KS wave function with respect to the symmetries of the physical electron system. Formally well-justified variants of the KS formalism that restore symmetries in real space, in spin space, or in both have been introduced long ago, but have rarely been used in practice. Here, we introduce numerically stable KS inversion methods to construct reference KS potentials from reference spin-densities for all four possibilities to treat open shell systems, non-symmetrized, spin-symmetrized, space-symmetrized, and fully-symmetrized. The reference spin-densities are obtained by full configuration interaction and high-level coupled cluster methods for the considered atoms and diatomic molecules. The decomposition of the total energy in contributions such as the non-interacting kinetic, the exchange, and the correlation energy is different in the four KS formalisms. Reference values for these differences are provided for the considered atoms and molecules. All KS inversions, except the fully symmetrized one, lead in some cases to solutions violating the Aufbau principle. In the purely spin-symmetrized KS formalism, this represents a violation of the KS v-representability condition, i.e., no proper KS wave functions exist in those cases.

Assembly-enhanced recognition: A biomimetic pathway to achieve ultrahigh affinities

On the one hand, nature utilizes hierarchical assemblies to create complex biological binding pockets, enabling ultrastrong recognition toward substrates in aqueous solutions. On the other hand, chemists have been fervently pursuing high-affinity recognition by constructing covalently well-preorganized stereoelectronic cavities. The potential of noncovalent assembly, however, for enhancing molecular recognition has long been underestimated. Inspired by (strept)avidin, an amphiphilic azocalix[4]arene derivative capable of assembly in aqueous solutions has been explored by us and demonstrated to exhibit ultrahigh binding affinity (up to 10 12 M −1 ), which is almost four orders of magnitude higher than those reported for nonassembled azocalix[4]arenes. An ultrastable azocalix[4]arene/photosensitizer complex has been applied in hypoxia-targeted photodynamic therapy for tumors. These findings highlight the immense potential of an assembly-enhanced recognition strategy in the development of the next generation of artificial receptors with appropriate functionalities and extraordinary recognition properties.

The orientational structure of a model patchy particle fluid: Simulations, integral equations, density functional theory, and machine learning

We investigate the orientational properties of a homogeneous and inhomogeneous tetrahedral four-patch fluid (Bol–Kern–Frenkel model). Using integral equations, either (i) HNC or (ii) a modified HNC scheme with a simulation input, the full orientational dependence of pair and direct correlation functions is determined. Density functionals for the inhomogeneous problem are constructed via two different methods. The first, molecular density functional theory, utilizes the full direct correlation function and an isotropic hard-sphere bridge functional. The second method, a machine learning approach, uses a decomposition of the functional into an isotropic reference part and a mean-field orientational part, where both parts are improved by machine learning techniques. A comparison with the simulation data at hard walls and around hard tracers shows a similar performance of the two functionals. Machine learning strategies are discussed to eliminate residual differences, with the goal of obtaining machine-learning enhanced functionals for the general anisotropic fluid.

Joseph G. Gall (1928–2024): A naturalist and scholar for the genomic era

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers’ persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein–protein, protein–water, and water–water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Spatially programmed alignment and actuation in printed liquid crystal elastomers

Liquid crystal elastomers (LCEs) exhibit reversible shape morphing behavior when cycled above their nematic-to-isotropic transition temperature. During extrusion-based 3D printing, LCE inks are subjected to coupled shear and extensional flows that can be harnessed to spatially control the alignment of their nematic director along prescribed print paths. Here, we combine experiment and modeling to elucidate the effects of ink composition, nozzle geometry, and printing parameters on director alignment. From rheological measurements, we quantify the dimensionless Weissenberg number ( Wi ) for the flow field each ink experiences as a function of printing conditions and demonstrate that Wi is a strong predictor of LCE alignment. We find that director alignment in LCE filaments printed through a tapered nozzle varies radially when Wi &lt; 1, while it is uniform when Wi ≫ 1. Based on COMSOL simulations and in operando X-ray measurements, we show that LCE inks printed through nozzles with an internal hyperbolic geometry exhibit a more uniform director alignment for a given Wi compared to those through tapered nozzles. Concomitantly, the stiffness along the print direction and actuation strain of printed LCEs increases substantially under such conditions. By varying Wi during printing through adjusting the flow rate “on the fly”, LCE architectures with uniform composition, yet locally encoded shape morphing transitions can be realized.

<i>Ab initio</i> study on the dynamics and spectroscopy of collective rovibrational polaritons

Accurate rovibrational molecular models are employed to gain insight in high-resolution into the collective effects and intermolecular processes arising when molecules in the gas phase interact with a resonant infrared (IR) radiation mode. An efficient theoretical approach is detailed, and numerical results are presented for the HCl, H2O, and CH4 molecules confined in an IR cavity. It is shown that by employing a rotationally resolved model for the molecules, revealing the various cavity-mediated interactions between the field-free molecular eigenstates, it is possible to obtain a detailed understanding of the physical processes governing the energy level structure, absorption spectra, and dynamic behavior of the confined systems. Collective effects, arising due to the cavity-mediated interaction between molecules, are identified in energy level shifts, in intensity borrowing effects in the absorption spectra, and in the intermolecular energy transfer occurring during Hermitian or non-Hermitian time propagation.

Dual modes of DNA N ⁶ -methyladenine maintenance by distinct methyltransferase complexes

Stable inheritance of DNA N 6 -methyladenine (6mA) is crucial for its biological functions in eukaryotes. Here, we identify two distinct methyltransferase (MTase) complexes, both sharing the catalytic subunit AMT1, but featuring AMT6 and AMT7 as their unique components, respectively. While the two complexes are jointly responsible for 6mA maintenance methylation, they exhibit distinct enzymology, DNA/chromatin affinity, genomic distribution, and knockout phenotypes. AMT7 complex, featuring high MTase activity and processivity, is connected to transcription-associated epigenetic marks, including H2A.Z and H3K4me3, and is required for the bulk of maintenance methylation. In contrast, AMT6 complex, with reduced activity and processivity, is recruited by PCNA to initiate maintenance methylation immediately after DNA replication. These two complexes coordinate in maintenance methylation. By integrating signals from both replication and transcription, this mechanism ensures the faithful and efficient transmission of 6mA as an epigenetic mark in eukaryotes.

Inference of Onsager coefficient from microscopic simulations by machine learning

Dynamic density functional theory (DDFT) is a fruitful approach for modeling polymer dynamics, benefiting from its multiscale and hybrid nature. However, the Onsager coefficient, the only free parameter in DDFT, is primarily derived empirically, limiting the accuracy and broad application of DDFT. Herein, we propose a machine learning-based, bottom-up workflow to directly extract the Onsager coefficient from molecular simulations, circumventing partly heuristic assumptions in traditional approaches. In this workflow, the Onsager coefficient is derived from the proposed DDFT-informed ordinary differential equation network, trained to replicate density evolution observed in Brownian dynamics (BD) simulations. We validate our method by studying the lamellar transition in symmetric diblock copolymer melts, where the DDFT model with the extracted Onsager coefficient precisely reproduces both the density evolution and interface narrowing predicted by BD simulations, thereby demonstrating the reliability of the present scheme. Meanwhile, our studies reveal the strong relevance of the Onsager coefficient with dynamic processes and identify the explicit connection between dynamic correlations, characterized by the correlation strength and correlation length, and the system parameters, e.g., the Flory–Huggins interaction parameter. We found that far from the transition point, the correlation that transmits the thermodynamic force into a density current is localized and strong, while close to the transition point, it becomes long-ranged but weak. Our approach aims to develop a more generalized framework to bridge more refined particle-based simulations to more coarse-grained field-based calculations, and the insights gained by using our approach could be extended to other non-equilibrium systems in polymer sciences.

Tuning the electronic structure and SMSI by integrating trimetallic sites with defective ceria for the CO ₂ reduction reaction

Heterogeneous catalysts have emerged as a potential key for closing the carbon cycle by converting carbon dioxide (CO 2 ) into value-added chemicals. In this work, we report a highly active and stable ceria (CeO 2 )-based electronically tuned trimetallic catalyst for CO 2 to CO conversion. A unique distribution of electron density between the defective ceria support and the trimetallic nanoparticles (of Ni, Cu, Zn) was established by creating the strong metal support interaction (SMSI) between them. The catalyst showed CO productivity of 49,279 mmol g −1 h −1 at 650 °C. CO selectivity up to 99% and excellent stability (rate remained unchanged even after 100 h) stemmed from the synergistic interactions among Ni-Cu-Zn sites and their SMSI with the defective ceria support. High-energy-resolution fluorescence-detection X-ray absorption spectroscopy (HERFD-XAS) confirmed this SMSI, further corroborated by in situ electron energy loss spectroscopy (EELS) and density functional theory (DFT) simulations. The in situ studies (HERFD-XAS &amp; EELS) indicated the key role of oxygen vacancies of defective CeO 2 during catalysis. The in situ transmission electron microscopy (TEM) imaging under catalytic conditions visualized the movement and growth of active trimetallic sites, which completely stopped once SMSI was established. In situ FTIR (supported by DFT) provided a molecular-level understanding of the formation of various reaction intermediates and their conversion into products, which followed a complex coupling of direct dissociation and redox pathway assisted by hydrogen, simultaneously on different active sites. Thus, sophisticated manipulation of electronic properties of trimetallic sites and defect dynamics significantly enhanced catalytic performance during CO 2 to CO conversion.

Effective repulsive interaction between Janus polymer-grafted nanoparticles adhering to lipid vesicles

The adhesion of nanoparticles to lipid vesicles causes curvature deformations to the membrane to an extent determined by the competition between the adhesive interaction and the membrane’s elasticity. These deformations can extend over length scales larger than the size of a nanoparticle, leading to an effective membrane-curvature-mediated interaction between nanoparticles. Nanoparticles with uniform surfaces tend to aggregate into unidimensionally close-packed clusters at moderate adhesion strengths and endocytose at high adhesion strengths. Here, we show that the suppression of close-packed clustering and endocytosis can be achieved by the surface modification of the nanoparticles into Janus particles where a moiety of their surface is grafted with polymers under a good solvent condition. The osmotic pressure of the polymer brushes prevents membrane wrapping of the nanoparticles’ moieties that are grafted with polymers, thus suppressing their endocytosis. Furthermore, a repulsion between polymer brushes belonging to two nearby nanoparticles destabilizes the dimerization of the nanoparticles over a wide range of values of the polymers’ molecular weight and grafting density. This surface modification of nanoparticles should allow for reliable, non-close-packed, and tunable self-assemblies of nanoparticles.

CFTR dictates monocyte adhesion by facilitating integrin clustering but not activation

Monocytes are critical in controlling tissue infections and inflammation. Monocyte dysfunction contributes to the inflammatory pathogenesis of cystic fibrosis (CF) caused by CF transmembrane conductance regulator (CFTR) mutations, making CF a clinically relevant disease model for studying the contribution of monocytes to inflammation. Although CF monocytes exhibited adhesion defects, the precise mechanism is unclear. Herein, superresolution microscopy showed that an integrin clustering but not an integrin activation defect determines the adhesion defect in CFTR-deficient monocytes, challenging the existing paradigm emphasizing an integrin activation defect in CF patient monocytes. We further found that the clustering defect is accompanied by defects in CORO1A membrane recruitment, actin cortex formation, and CORO1A engagement with integrins. Complementing canonical studies of leukocyte adhesion focusing on integrin activation, we highlight the importance of integrin clustering in cell adhesion and report that integrin clustering and activation are distinctly regulated, warranting further investigation for selective targeting in therapeutic strategy design involving leukocyte-dependent inflammation.

Absolute line strength measurements of HO2 radical in the OO-stretching fundamental band between 1088 and 1124 cm−1 using time-resolved dual-comb spectroscopy

Absolute line strength measurements of hydroperoxyl (HO2) radical in the OO-stretching (ν3) fundamental band have been performed by means of mid-infrared time-resolved dual-comb spectroscopy. By employing two sets of dual-comb spectrometers, high-resolution time-resolved spectra of HO2 and HCl, formed in the photolysis reaction system of Cl2/CH3OH/O2, could be, respectively, measured near 1123 and 3059 cm−1. With kinetic simulations, spectral analysis of both HO2 and HCl, as well as the accurate line strength of the HCl R(9) transition at 3059.316 cm−1, an absolute line strength of the ν3 131,13 ← 121,12 F1,2 transitions in HO2 at 1122.983 cm−1 was first determined to be 1.80 × 10−20 cm molecule−1 with a small uncertainty down to 4% under the conditions with low initial concentrations of Cl radical (1.63–1.81 × 1013 molecule cm−3). Furthermore, broadband high-resolution spectra of the ν3 fundamental band of HO2 were recorded in the range of 1088–1124 cm−1 with an average spectral resolution of 0.002 cm−1. By contour fitting the measured broadband spectra with PGOPHER, the line strengths of hundreds of rovibrational transitions were obtained relative to the well-determined HO2 lines at 1122.983 cm−1, and those values were observed to be higher than those tabulated in the HITRAN database by a factor of ∼2.8. Moreover, the absolute band strength of the ν3 fundamental band in HO2 was derived to be 22.3 km mol−1 with an uncertainty of 5%. This work providing precise and detailed spectral data would be crucial in revisiting the theoretical modeling of HO2 geometry and updating the database of the HO2 radical.