Browse Articles

Symmetry dilemmas in quantum computing for chemistry: A comprehensive analysis

Symmetry adaptation, universality, and gate efficiency are central but often competing requirements in quantum algorithms for electronic structure and many-body physics. For example, fully symmetry-adapted universal operator pools typically generate long and deep quantum circuits; gate-efficient universal operator pools generally break symmetries; and gate-efficient, fully symmetry-adapted operator pools may not be universal. In this work, we analyze such symmetry dilemmas both theoretically and numerically. On the theory side, we prove that the popular, gate-efficient operator pool consisting of singlet spin-adapted singles and perfect-pairing doubles is not universal when spatial symmetry is enforced. To demonstrate the strengths and weaknesses of the three types of pools, we perform numerical simulations using an adaptive algorithm paired with operator pools that are (i) fully symmetry-adapted and universal, (ii) fully symmetry-adapted and non-universal, and (iii) breaking a single symmetry and universal. Our numerical simulations encompass three physically relevant scenarios in which the target state is (i) the global ground state, (ii) the ground state crossed by a state differing in multiple symmetry properties, and (iii) the ground state crossed by a state differing in a single symmetry property. Our results show when symmetry-breaking but universal pools can be used safely, when enforcing at least one distinguishing symmetry suffices, and when a particular symmetry must be rigorously preserved to avoid variational collapse. Together, the formal and numerical analyses provide a practical guide for designing and benchmarking symmetry-adapted operator pools that balance universality, resource requirements, and robust state targeting in quantum simulations for chemistry.

Low-frequency vibrational dynamics of pyrene derivatives investigated by time-resolved impulsive stimulated Raman spectroscopy

Intramolecular charge transfer (ICT) dynamics of trisodium 8-aminopyrene-1,3,6-trisulfonate (APTS) in polar protic solvents were investigated using time-resolved impulsive stimulated Raman spectroscopy (TR-ISRS). The hydrogen-bond donating ability of the solvents promotes the ICT process of APTS in the excited state. APTS exhibits two distinct excited-state Raman spectra of the locally excited and charge-transferred S1 state in TR-ISRS, where ultrafast ICT and subsequent vibrational relaxation dynamics with time constants of ∼160 fs and ∼5 ps, respectively, were observed in the formamide solution. Multidimensional structural changes of APTS, including the twist (θt) and bend (θb) of the amino group, are predicted to accompany ICT in the excited state by time-dependent density functional theory calculations. The two in-plane deformation modes of the pyrene backbone at 457 and 812 cm−1 are strongly coupled to the ICT coordinate of APTS in the S1 state. These low-frequency deformation modes of the pyrene backbone were commonly observed in the excited-state dynamics of pyrene derivatives with hydroxyl and methoxy groups, and thus, they can provide crucial information on the multidimensional structural dynamics of pyrene derivatives in numerous excited-state processes, including ICT and proton transfer.

A universal metric for classifying gas transport regimes in nanoconfined media

Gas transport in nanoconfined media is fundamental to applications such as gas separation, catalysis, and shale gas extraction. While transport mechanisms in idealized rigid pores or simple fluids are well understood, classifying gas transport in complex soft matter and highly viscous liquids remains challenging. Here, we introduce a quantitative, physically grounded framework for classifying gas transport regimes based on the intrinsic dependence of gas diffusivity on molecular mass. Using molecular dynamics simulations, we systematically examine how gas diffusion coefficients scale with molecular mass across a broad range of nanoconfined media. We define a diffusivity–mass scaling exponent (α) that serves as a mechanistic fingerprint of the transport regime: α values near zero correspond to random diffusion, whereas values approaching −0.5 indicate transport regimes dominated by rigid pore confinement, such as Knudsen, surface, or hopping diffusion. This metric enables the quantitative identification of gas transport mechanisms and captures critical regime transitions of gas nanoflow that have previously been difficult to classify. Further analysis reveals that the molecular mass dependence arises from variations in characteristic step length, governed by molecular momentum and gas–medium interactions. The proposed mass-scaling framework provides a unified and objective criterion for identifying gas transport mechanisms in nanoconfined systems, laying the foundation for a general theory of nanoscale gas transport and enabling more reliable prediction and design of gas-transport materials.

Memory effects in contact line friction

When a drop of liquid comes into contact with a solid surface, it relaxes toward an equilibrium configuration, either wetting the surface or remaining in a droplet-like shape with a finite contact angle. The force driving the process toward equilibrium is the corresponding out-of-balance Young’s force. However, the speed with which the liquid front advances depends strongly on an opposing friction force arising from dissipative processes due to the moving solid–liquid–gas contact line. In analogy to the treatment of hydrodynamic friction, we present an exact method, based on the Mori–Zwanzig formalism, to extract this friction from equilibrium fluctuations. We find that the contact line exhibits long-lasting memory with a characteristic power-law decay due to coupling to the systems hydrodynamic modes. For the systems studied in this work, the majority of the friction emerges due to this coupling. Within the linear response regime, we obtain the frequency-dependent dissipative and elastic response of the contact line to an external perturbation, including a frequency-dependent friction coefficient.

Cracking donuts and sorting lipids: Geometry controls archaeal membrane stability and lipid organization

Cells are defined by lipid membranes that differ in their structure across the tree of life. While the membranes of most bacteria and eukaryotes consist of single-headed bilayer lipids, the membranes of archaea are composed of mixtures of single-headed bilayer lipids and double-headed bolalipids. Archaeal bolalipids can adopt straight or u-shaped conformations, enabling them—together with bilayer lipids—to control whether membranes form bilayer or monolayer structures. Yet, the physical principles governing archaeal membranes remain largely unexplored, especially how membrane structure couples to externally imposed curvature during membrane remodeling. Here, we perform coarse-grained molecular dynamics simulations of toroidal vesicles to systematically probe the effects of all relevant combinations of mean and Gaussian curvatures on shape stability and lipid organization. We find that soft bilayer membranes can sustain all curvatures induced, whereas rigid bolalipid monolayer membranes either transition to different vesicle shapes or rupture. Bilayer-mimicking u-shaped bolalipids and bilayer lipids are spatially accumulated in regions of high mean membrane curvature independent of Gaussian curvature. Our work identifies curvature–composition coupling as a physical signature of archaeal membrane remodeling.

Node transfer for multi-fidelity and multimodal machine learning for predicting experimental bandgaps

Bandgap is a key property of materials. In recent years, machine learning has become a powerful tool to predict the experimental bandgaps of compounds before synthesis, but there is still much room for improving the prediction accuracy. Here, we build a machine learning framework that consists of multi-fidelity and multimodal learning models to integrate heterogeneous data sources obtained from first-principle calculations and x-ray diffraction spectra. A new information-fusion strategy named node transfer is proposed. Compared to the widely used Δ-learning strategy, it naturally extends two-fidelity to multi-fidelity learning and facilitates heterogeneous multimodal integration. Node transfer consistently outperforms Δ-learning across two-fidelity, multi-fidelity, and multimodal benchmarks under fine-tuning. The best model involves XRD-based descriptors and encoded descriptors pre-trained based on four computational datasets using different functionals. It achieves a mean absolute error of 0.258 eV, a 26.3% reduction vs the single-fidelity baseline of 0.350 eV. In all prediction tasks, only the chemical composition of the crystal is required as input for the constructed machine learning models, which is free of structural information and, therefore, applicable to materials design before experiments or first-principle calculations.

Quantifying local point-group-symmetry order in complex particle systems

Crystals and other condensed phases are defined primarily by their inherent symmetries, which play a crucial role in dictating their structural properties. In crystallization studies, local order parameters (OPs) that describe bond orientational order are widely employed to investigate crystal formation. Despite their utility, these traditional metrics do not directly quantify symmetry, an important aspect for understanding the development of order during crystallization. To address this gap, we introduce a new set of OPs, called Point Group Order Parameters (PGOPs), designed to continuously quantify point group symmetry order. We demonstrate the strength and utility of PGOP in detecting order across different crystalline systems and compare its performance with that of commonly used bond-orientational order metrics. PGOP calculations for all finite point groups are implemented in the open-source package SPATULA (Symmetry Pattern Analysis Toolkit for Understanding Local Arrangements), written in parallelized C++ with a Python interface. The code is publicly available on GitHub at https://github.com/glotzerlab/spatula.

EquiHGNN: Scalable rotationally equivariant hypergraph neural networks

Molecular interactions often involve higher-order relationships that cannot be fully captured by traditional graph-based models limited to pairwise connections. Hypergraphs naturally extend graphs by enabling multi-way interactions, making them well-suited for modeling complex molecular systems. In this work, we introduce EquiHGNN, an equivariant hypergraph neural network framework that integrates symmetry-aware representations to improve molecular modeling. By enforcing the equivariance under relevant transformation groups, our approach preserves geometric and topological properties, leading to more robust and physically meaningful representations. We examine a range of equivariant architectures and demonstrate that integrating symmetry constraints leads to notable performance gains on large-scale molecular datasets. Experiments across small and large molecules indicate that while higher-order interactions provide marginal gains for small systems, they surpass 2D graphs on larger ones. Incorporating geometric features into these higher-order structures further enhances performance, underscoring the critical role of spatial information in molecular representation learning. Our source code is available at https://github.com/HySonLab/EquiHGNN/.

Quantum coherence in neuromorphic computing

Quantum effects become significant when hardware computing units scale down to nanoscale dimensions. To maintain reliable performance as neuromorphic computing hardware scales down, researchers must understand how quantum coherence across multiple neurons impacts neural network function. In this study, we model neuromorphic computing with quantum coherence effects using a quantum spiking neural network model. We find that quantum coherence between neural activations can alter the network perception, compared to the incoherent network. Destructive interference between activation signals propagating through different synaptic channels drives this effect at the quantum scale. This quantum effect becomes more prominent with increasing network depth and can be mitigated by increasing the number of input neurons connected to each output neuron.

Machine-learned many-body potentials for charged colloids reveal gas–liquid spinodal instabilities only in the strong-coupling regime of primitive models

Past experimental observations of gas–liquid and gas–crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like-charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency, where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas–liquid and gas–solid phase separation observed in primitive-model simulations at low charge numbers. Extending the ML-based simulations to higher valencies, where primitive-model simulations become prohibitively slow, also reveals like-charge attractions and gas–liquid spinodal instabilities, however, only in the regime of strongly coupled electrostatic interactions and not in the weakly coupled Poisson–Boltzmann regime of the experimental observations of colloidal like-charge attractions.

Parsing contributions of physical phenomena to smFRET statistical inhomogeneity via multiparameter stochastic simulations

Single-molecule Förster resonance energy transfer (smFRET) affords access to nanometer-scale structural and kinetic information for individual biomolecular species. Conventional analyses presuppose a strict separation of the underlying dynamical processes into distinct timescales—an assumption that is frequently violated and seldom verifiable a posteriori. To address this limitation, we present an integrated Brownian dynamics/stochastic simulation framework that treats the three principal dynamic contributors to smFRET observables—(i) diffusion of the molecule’s center of mass, (ii) photophysical state-cycling, and (iii) intramolecular diffusion—in a fully time-resolved manner. Each contribution can be selectively activated, deactivated, and parametrically adjusted, thereby providing a controlled computational testbed for determining the extent to which distinct dynamical contributions alter smFRET data. By systematically varying these contributions, the individual and collective impact of specific physical processes on smFRET measurements can be delineated and, therefore, the biologically relevant information (iii) can be more precisely estimated.

Ice-induced structural reconfigurations in nanoconfined water–glycerol mixtures

We elucidated the mesoscopic organization emerging in water–glycerol mixtures confined within the nanoporous cylindrical pores of SBA-15 and MCM-41 silicas, with pore diameters of 8.1 and 3.5 nm, respectively. Neutron diffraction was used to track variation in the intensity of Bragg reflections originating from the crystalline pore arrangements after filling and as a function of composition and temperature. In addition, isotopic substitution was employed to systematically adjust the scattering length density contrast between the different components of the mixtures. The radial concentration profile within the pore was determined by fitting various form factor models to the experimental intensity data. At room temperature, our findings support a uniform compositional distribution within the pore. Upon cooling, we observe partial ice crystallization at Tf ≈ 200–230 K in solutions where the water content exceeds the maximally freeze-concentrated solution threshold (30% w/w), while vitrification of the entire solution is observed otherwise. The partial freezing triggers phase separation into distinct ice and liquid domains. Remarkably, the morphology adopted by these domains is shaped by the geometry of the confining cylindrical nanopore, resulting in a core–shell structure: a pure ice core surrounded by a glycerol-rich layer adjacent to the pore surface. In contrast to the unfreezable layer commonly found in pure confined water, typically around 0.6 nm thick, our findings demonstrate that the size of the interfacial glassy solution is governed by both the pore size and the overall mixture composition. In the systems studied, this interfacial thickness varies between 0.3 and 1.4 nm, with its composition aligning with that of the MFCS.

Factors influencing high expectations in patients undergoing corneal transplantation

Origin of heteroatom substitution effects on hyperfine coupling in muoniated radicals: A path-integral molecular dynamics study

Muonium (Mu) represents a powerful probe of radical electronic structure owing to its hyperfine coupling constant (HFCC). In this study, we investigate the influence of heteroatom substitution on the HFCC of Mu through ab initio path integral molecular dynamics simulations on muoniated xanthene-9-thione (μ-XT) and thioxanthene-9-thione (μ-TXT). The simulations reproduce the experimental trend that μ-XT exhibits a larger HFCC than μ-TXT, highlighting the crucial influence of nuclear quantum effects on the local structure around Mu. The Mu–S bond exhibits nearly identical behavior in both molecules and, therefore, does not explain the observed difference in the HFCC. In contrast, the dihedral angle between the S–Mu bond and the molecular framework considerably affects the HFCC. In particular, when the S–Mu bond is oriented perpendicular to the molecular framework, the HFCC difference between μ-XT and μ-TXT is maximized. Natural bond orbital analyses reveal that oxygen substitution enhances electron delocalization across the π-system, thereby stabilizing the C1 p-orbital and intensifying the hyperconjugative interaction. Overall, the reduced HFCC in μ-TXT originates from diminished hyperconjugation caused by the weaker electronic delocalization associated with the longer C–S bond. These results provide fundamental insights into substituent effects on Mu-labeled radicals.

Unified spatial-temporal graph aggregation framework for predicting student performance

The Spin-MInt algorithm: An accurate and symplectic propagator for the spin-mapping representation of nonadiabatic dynamics

Mapping methods, including the Meyer–Miller–Stock–Thoss (MMST) mapping and spin-mapping, are commonly utilized to simulate nonadiabatic dynamics by propagating classical mapping variable trajectories. Recent work confirmed that the Momentum Integral (MInt) algorithm is the only known symplectic algorithm for the MMST Hamiltonian. To our knowledge, no symplectic algorithm has been published for the spin-mapping representation without obtaining Cartesian variables and utilizing the MInt algorithm. Here, we present the Spin-MInt algorithm, which directly propagates the spin-mapping variables. First, we consider a two-level system, which maps onto a spin-vector on a Bloch sphere. Despite the spin-variables being non-canonical, we rigorously prove that the Spin-MInt is symplectic using a canonical variable transformation. We determine that the Spin-MInt is a symmetrical, second-order, time-reversible, angle invariant, and geometric structure preserving algorithm. Computationally, for a one-dimensional spin-boson model, the Spin-MInt and MInt algorithms are symplectic, satisfy Liouville’s theorem, provide second-order energy conservation, and are more accurate than a previously published angle-based algorithm. We present accurate correlation functions for a multi-dimensional spin-boson model. We also extend this methodology to a general number of electronic states and present accurate population results for a three-state Morse potential. The Spin-MInt is faster than the MInt algorithm for all tested models, particularly so for large nuclear degrees of freedom. We believe this to be the first known symplectic algorithm for propagating the nonadiabatic spin-mapping Hamiltonian and one of the first rigorously symplectic algorithms in the case of non-trivial coupling between canonical and spin systems. These results should guide and improve future simulations.

Implications of Pr3+ ions on structural, opto-thermal features of alkali zinc boro-tellurite glass systems for optical and laser technology applications

Abstract A novel multicomponent series of Pr 3+ -ions doped borotellurite glasses with composition (45-y)B 2 O 3 +20TeO 2 +20ZnO+5Pb 3 O 4 +10Na 2 O+yPr 6 O 11 , where y = 0.0, 0.3, 0.6, 1.0, 1.5, and 2 mol%, were prepared. By the XRD technique, the non-crystalline state of the prepared glasses was validated. ATR-FTIR and Raman spectroscopy confirmed the presence of functional units, including BO 4 , BO 3 , TeO 4 , TeO 3 , and Pb–O and Zn–O links. The thermal analysis by DSC shows that glass transition and crystallization temperatures are found between 409 $${\rm ^\circ C}$$ and 461 $${\rm ^\circ C}$$ , and 511 $${\rm ^\circ C}$$ and 572 $${\rm ^\circ C}$$ . UV-visible absorption spectroscopy characterization reveals the transitions 3 H 4 → 3 P 2 , 3 P 1 , 3 P 0 , and 1 D 2 corresponding to wavelengths of 444 nm, 470 nm, 483 nm, and 590 nm. The emission spectra of Pr 3+ ions embedded in glass samples were recorded by a spectrofluorometer with an excitation wavelength of 444 nm. Among the observed transitions, the 3 P 0 → 3 H 4 (488 nm) and 3 P 1 → 3 H 5 (605 nm) transitions exhibited the most intense peaks. The CCT value of orange emission is found to be < 4000 K, implying warm CCT, whereas blue emission is > 4000 K, implying cool CCT. The optical and physical parameters were evaluated with appropriate formulae. The density and refractive index range from 3.710 gcm − 3 to 4.112 gcm − 3 and 2.356 to 2.368, respectively. The energy band gap varies from 3.154 eV to 3.110 eV. Metallization criterion and the electronic oxide polarizability vary in the range from 0.397 to 0.394 and 4.243 Å to 4.376 Å, respectively. By considering their structural, thermal, optical, and luminescence properties, the prepared glasses are promising materials for optical technologies, including LEDs and lasers.

<i>Ab initio</i> derivation of the crystal field parameters for lanthanide ions: The f1 case

The crystal field theory as explained by Abragam and Bleaney in their landmark 1970 book on transition-ion electron paramagnetic resonance remains a cornerstone in the development of luminescence applications and molecular magnets based on the f-elements. The modern numerical derivation of the 27 Bkq Stevens crystal field parameters (CFPs), which describe the splitting of the energy levels of a central ion, is traditionally achieved through the effective Hamiltonian theory and multiconfiguration wave function theory calculations, insofar as the lowest J level fully captures the targeted low-energy physics. In this study, we present a novel theoretical approach for determining the CFPs. The procedure resembles the traditional extraction path but crucially accounts for the full |J, MJ⟩ space of an ion configuration with L = 3 and S = 1/2. By demonstrating the extraction procedure using the simplest case of a CeIII 4f1 ion with a crystal-field split J ∈ {5/2, 7/2} manifold, it is shown for the first time that a unique set of CFPs describes the splitting and mixing of both the J manifolds. In fact, this J/J′ mixing is analogous to the “spin mixing” in binuclear transition metal complexes. At the employed level of calculation, we demonstrate that there is no spin–orbit coupling influence on the CFP values. Moreover, for the 4f1 case, the present extraction yields crystal-field and spin–orbit parameters similar to those obtained from ab initio ligand field theory. This study represents the first step of a larger effort in reviewing the theory and extraction procedures of CFPs in f-element complexes.

Research on microgrid cluster optimization method based on sparrow search algorithm

Abstract With the widespread application of renewable energy in microgrids, collaborative optimization scheduling of microgrid clusters has become a key issue in improving energy utilization efficiency and operational economy. To solve this problem, this paper proposes a microgrid cluster optimization scheduling method based on the sparrow search algorithm. Firstly, construct a microgrid cluster model that includes wind turbines, photovoltaics, energy storage batteries, diesel generators, and hydrogen fuel cells. Secondly, the economic optimum is defined as the objective function, and combined with the constraints of equipment and system operation, the sparrow search algorithm is proposed to solve the optimization model of the microgrid cluster. Finally, this method is used to simulate an IEEE 9-node system with 4 microgrids. The simulation results showed that this method has significant advantages over traditional particle swarm optimization algorithms in reducing total costs and new energy utilization efficiency, verifying the feasibility of this method in microgrid cluster optimization scheduling.

Understanding twist-disorder of polytetrafluoroethylene (PTFE) chains using neural network potential molecular dynamics

Molecular dynamics (MD) calculations are used to characterize the temperature-dependent evolution of helicity and twist disorder in polytetrafluoroethylene (PTFE) chains. The MD calculations employ machine-learned neural network potentials (NNPs) trained on density functional theory data. In particular, NNPs were derived using three functionals: PBE-D3, r2SCAN, and r2SCAN-rVV10. As temperature increases, for PBE-D3 and r2SCAN-rVV10, we observe gradual unwinding from the equilibrium helix to less twisted, increasingly disordered conformations, with distinct discontinuous transitions near 250 K (PBE-D3) and 410 K (r2SCAN-rVV10). Notably, the PBE-D3 NNP captures these transitions and associated structural changes most accurately, whereas the r2SCAN-rVV10 NNP shows similar phase behavior with signs of overbinding. In contrast, the NNP trained on r2SCAN suppresses the unwinding transition and produces tighter helical structures with increasing temperature, underscoring the strong dependence on the choice of functional. The onset of chain rotational motion is accompanied by diffusion along the chain axis, supporting the Klug–Franklin hypothesis of screw-like disorder. Observed helical unwinding occurs after the emergence of conformational disorder and correlates with the onset of rotational and translational mobility, revealing a progression from structural to dynamical disorder upon heating. Computed PTFE melt densities highlight long-range van der Waals (vdW) interactions in the reference data used to train the NNP, motivating the incorporation of long-range vdW energies into the training scheme via an analytical Ewald summation for damped dispersion energies. Our MD results provide molecular-scale insights into the structural and dynamical behavior of PTFE, especially the thermally induced twist disorder, and demonstrate the utility of our approach for exploring other fluorocarbon macromolecules.