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IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis

Molecular conical intersections with odd electron number are realizations of the topological Yang monopole

The two-level conical intersection of a molecule with an odd electron number in the absence of a magnetic field obeys time-reversal symmetry T2 = −1 (referred to as the T2 = −1 conical intersection) and has a five-dimensional branching space due to the Kramer degeneracy. Similar to how the conical intersection of a molecule in a magnetic field (T2 = 0) behaves as the Dirac monopole, the T2 = −1 conical intersection behaves as the Yang monopole, a mathematical generalization of the Dirac monopole with SU(2) gauge field and SO(5) symmetry. This implies that we can study the topological properties of T2 = −1 conical intersections in chemistry based on what is known about the Yang monopole in high energy physics. In this work, we present a few mathematical tools to study this connection. First, we show that geometric algebra and quaternion numbers together provide a natural way to utilize the T2 = −1 time reversal symmetry and the SO(5) symmetry in scaled coordinates, making it simple to derive eigenfunctions, Berry connection, and Berry curvature of T2 = −1 conical intersection. In particular, this approach provides a simple proof that when viewed from the upper or lower states, the T2 = −1 conical intersection behaves as the self-dual or self-antidual Yang monopole with second Chern number C2=+122 or −122, respectively. In addition, we propose a visualization method for the SU(2) Berry connection of T2 = −1 conical intersection. This is achieved by showing that the non-zero part of the Berry connection induces a Hopf-fibration on the S3 longitude space, which is further visualized through stereographic projection.

Energy-dependent selective bond cleavage induced by low-energy electrons in DNA films deposited from buffered solutions: Insights from XPS

In this study, we investigated chemical modifications caused by low-energy electrons (LEEs) in DNA films deposited from Tris-EDTA (TE) buffered solutions, using x-ray photoelectron spectroscopy (XPS). DNA samples were exposed to 9.2, 4.2, and 0.2 eV electrons for up to 8 h. XPS revealed the energy- and site-specific selective cleavage of chemical bonds as observed in C 1s, N 1s, O 1s, and P 2p spectra. At 9.2 and 4.2 eV, LEE irradiation significantly induced the cleavage of C–N bonds in N-glycosidic linkages and C–O bonds in the sugar-phosphate backbone. The selective cleavage of C–N and C–O bonds may lead to the generation of apurinic/apyrimidinic sites and produce damage to the sugar–phosphate backbone and sugar moiety. In contrast, other structures, including the phosphate groups (P=O) within the DNA backbone, remained relatively stable. Non-significant spectral or compositional changes were observed at 0.2 eV. The TE components remain chemically stable during irradiation; however, experimental results suggest that it may help increase the yield of selective DNA damage. Our findings contribute to a deeper mechanistic understanding of LEE-induced biomolecular damage and support the development of LEE-based cancer radiotherapy.

Thermodynamics of hard-sphere plus square-well trimer fluids. A small mystery

The aim of this work is to show that the thermodynamics of some simple fluids, composed of molecules formed by only three atoms—trimers—presents a challenge when confronted with molecular simulation results. Thermodynamic perturbation theory (TPT), coupled to statistical associating fluid theory (SAFT), has successfully accounted for the properties of quite complicated and realistic fluids, both pure and mixed. It is then surprising that simple fluids, formed by trimers with square-well (SW) and hard-sphere (HS) atoms, reveal severe limitations of such theories. TPT1 and SAFT show how to calculate the free energy of the trimer fluid involving HS and SW atoms. The reference system in TPT-SAFT is a fluid made of isolated atoms. Its free energy is dealt with by the well-known high-temperature expansion (HTE), treated here to the fourth order. The trimerization contribution according to TPT—called the chain term—accounts for the change in free energy as the atoms bind and depends on the cavity function, which is determined here by a novel and consistent procedure. We studied the SW–SW–SW and SW–HS–SW trimers. The main properties analyzed are vapor–liquid (VL) coexistence and critical points. The theory presented here accounts very well for the VL coexistence of the SW–SW–SW trimers but fails for the SW–HS–SW trimer, for which a strong disagreement appears. We analyze the cause of this flaw in the theory and introduce a successful semi-empirical correction to it. The possible cause is pointed out and is currently being investigated.

Revealing the microhydration mechanisms of <i>α</i> -hydroxy carboxylic acids: Identification of large-amplitude torsional and librational motion

The molecular recognition mechanisms associated with the self-aggregation and microhydration processes of two different bifunctional α-hydroxy carboxylic acids, prototypical glycolic acid [CH2OHCOOH] and the doubly methylated variant 2-hydroxyisobutyric acid [(CH3)2C(OH)COOH], have been investigated by low-temperature mid- and far-infrared cluster spectroscopy of doped low polarizability neon matrices at 4 K, complemented by systematic theoretical conformational sampling refined with high-level electronic structure theory at the DLPNO-CCSD(T)/aug-cc-pV5Z level. Several mid-infrared perturbed vibrational fundamentals together with specific far-infrared vibrational fundamentals associated with large-amplitude, anharmonic hindered intramolecular torsional and intermolecular librational (hindered overall rotational) motion of the α-hydroxy carboxylic acid monohydrates are unambiguously assigned based on a combination of mixing ratio dependencies, thermal annealing, and the use of isotopically enriched H218O and D2O samples. These direct experimental spectroscopic probes of the formed intermolecular hydrogen bond motifs validate high-level quantum chemical predictions that the global intermolecular potential energy minima configurations involve six-membered cyclic cooperatively hydrogen-bonded C=O⋯H–Owater⋯H–Oacid “insertion” sequences in the monohydrate species of both α-hydroxy carboxylic acid analogues.

Spin localization in intermolecular complexes: A challenge for semi-local approximants for the embedding potential

Regardless of how the electron correlation is treated, all methods based on frozen-density embedding theory rely on approximations to the non-additive kinetic potential bi-functional ṽtnad[ρA,ρB](r)≈vtnad[ρA,ρB](r). Open shell systems, in which the spin is localized on a specific molecular fragment, are particularly prone to incorrect redistribution of charge depending on the used ṽtnad[ρA,ρB]. In this work, we present a systematic analysis of spin densities obtained with several semi-local approximations to vtnad[ρA,ρB], with the aim of delimiting their respective domains of applicability. We show that spin distributions obtained using decomposable semi-local ṽtnad[ρA,ρB] fall into two distinct categories: they are either qualitatively incorrect or reasonably accurate and consistent with trends previously observed for other properties computed using the same approximants. In neither case do gradient-dependent corrections, although crucial for improving the corresponding energy bi-functional (Tsnad[ρA,ρB]), resolve the deficiencies observed for spin densities. We propose a simple criterion based on orbital energies that allows one to identify a priori the situations in which a given approximant is likely to fail. Finally, we show that a recently developed non-decomposable approximant ṽtnad(NDCS)[ρA,ρB] extends the range of applicability of FDET-based methods to embedded radicals that are inaccessible to semi-local approximants. Moreover, ṽtnad(NDCS)[ρA,ρB] yields improved spin densities even in cases where decomposable semi-local approximants already perform reasonably well.

On the importance of numerical integration details for homogeneous flow simulation

The Sllod equations of motion enable modeling of homogeneous flow at the atomic scale and are commonly used to predict fluid properties such as viscosity. However, few publicly available codes support such simulations, and those that do often include subtle problems in the numerical integration scheme or related aspects, which result in a failure to conserve the energy of the extended system. Here, we demonstrate a reversible and energy-conserving integration scheme for the Sllod equations of motion with error on the order of δt3, in line with typical operator splitting integrators used in standard molecular dynamics simulations. We discuss various implementation details and implement the scheme in LAMMPS, where we find that our changes enable more accurate simulation of transient responses, mixed flows, and steady states, especially at high rates of flow. Importantly, we show that a lack of energy conservation can manifest as a systematic error in the direct ensemble average of the pressure tensor, leading to an error in the calculated viscosity which becomes significant at high flow rates.

Regional chemical potential analysis for material surfaces

We propose a local regional chemical potential (RCP) analysis method based on an energy window scheme to quantitatively estimate the selectivity of atomic and molecular adsorption on surfaces, as well as the strength of chemical bonding forces between a probe tip and a surface in atomic force microscopy (AFM) measurements. In particular, focusing on the local picture of covalent bonding, we use a simple H2 molecular model to demonstrate a clear relationship between chemical bonding forces and the local RCP. Moreover, density functional theory calculations on molecular systems and diamond C(001) surfaces reveal that the local RCP at the surfaces successfully visualizes electron-donating regions such as dangling bonds and double bonds. These results suggest that the local RCP can serve as an effective measure to analyze high-resolution non-contact or near-contact AFM images enhanced by chemical bonding forces. Analogous to the Tersoff–Hamann approach widely used for simulating scanning tunneling microscopy images, the RCP analysis method provides a practical and computationally efficient framework for interpreting AFM images.

sbml4md: A computational platform for system–bath modeling via molecular dynamics powered by machine learning

We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anharmonicity, intermolecular couplings, and bath correlation functions for each mode, sbml4md obviates empirical fitting and enables the modeling of environments with spatial and temporal heterogeneity. This work provides a set of parameters specifically tailored for the Hierarchical Equations of Motion (HEOM) framework, enabling numerically “exact” simulations of nonlinear vibrational spectra. Building upon our previous implementation for intramolecular vibrational modes [K. Park, J.-Y. Jo, and Y. Tanimura, J. Chem. Phys. 163, 214104 (2025)], the present code enhances optimization efficiency by explicitly accounting for intermolecular vibrational contributions. This extension enables sbml4md to broaden the applicability of HEOM-based dynamical modeling by seamlessly integrating classical MD approaches, thereby providing a flexible and scalable framework for simulating both linear and nonlinear spectra under realistic conditions with minimal empirical input. The accompanying ML code, written in Python, is provided as the supplementary material.

Physical mechanisms of nanoparticle–membrane interactions: A coarse-grained study

Nanoparticles are promising drug carriers for targeted therapies, diagnostic imaging, and advanced vaccines. However, their clinical translation is limited by complex biological barriers that reduce cellular uptake and efficacy. In particular, the interaction with the cellular membrane controls nanoparticle adhesion, wrapping, or full engulfment, which ultimately governs nanoparticle internalization efficiency. Flexible nanocarriers (e.g., liposomes, polymeric nanogels, and micelles) are particularly attractive because their deformability could help them enhance the probability of successful cellular entry. To understand the physical mechanisms associated with cellular uptake, we investigate the interaction of semi-flexible nanocarriers with a symmetric lipid bilayer using coarse-grained simulations. We represent a flexible nanoparticle using the previously introduced metaparticle model and the membrane using the Cooke–Deserno model. By systematically varying nanoparticle properties, i.e., adhesion strength and topology, we identify distinct interaction regimes ranging from surface adhesion and trapping to complete wrapping and endocytosis. These regimes correlate with nanoparticle shape, size, and surface properties, providing quantitative design principles for optimizing cellular uptake. Overall, this framework offers predictive insight into how the interplay between nanoparticle properties and membrane interaction governs cellular internalization, informing the rational design of next-generation soft nanocarriers and smart materials.

Pressure-induced p <i>K</i> a variations influence conformations of pH-sensitive polymers

Conformations of aqueous macromolecules depend on a delicate balance of hydrophobic, electrostatic, and hydrogen-bonding interactions, all of which are influenced by environmental factors such as pressure and pH. Understanding how these factors modulate structural stability is critical for both biological and material science applications. Here, we use constant-pH molecular dynamics simulations to investigate the pressure response of a short, pH-sensitive polymer in explicit solvent. Our results reveal that increasing pressure unfolds both neutral and charged polymers, but the degree of unfolding is markedly reduced when the polymer carries a charge, demonstrating the coupling of pressure and charge regulation. At pH = pKa, we observe a pressure-dependent transition from neutral-like behavior at low pressure to charged-like behavior at high pressure, a signature of pressure-induced pKa shifts. Additionally, pressure-induced unfolding is enhanced at this pH. Extending our study to a model polyampholyte with one acidic and one basic monomer, we find clear evidence of non-additive acid–base coupling that stabilizes collapsed states at low pressure, as well as pressure-induced salt-bridge denaturation at high pressure. This behavior reveals a competition between electrostatic stabilization and pressure-driven hydration effects. These findings shed some light on the pressure-modulated macromolecular behavior of charged and neutral polymers and provide insights relevant to both synthetic polymers and pressure-adapted biological systems.

Deriving effective electrode–ion interactions from free-energy profiles at electrochemical interfaces

Understanding ion adsorption at electrified metal–electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, we systematically investigate the free energy profiles of Na+, Cl−, and F− ions at the Au(111)–water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that—without due care—standard mixing rules can lead to qualitatively incorrect descriptions of ion–metal interactions. We present a systematic methodology for tuning the cross term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

Many roads to the seam: How conformational flexibility drives nonadiabatic relaxation in a prototypical tetrapyrrolic chromophore

Large and structurally flexible chromophores pose challenges for in silico modeling of photodeactivation due to the many vibrational modes that can funnel the system toward energy degeneracy. In this work, we examine how the multiple degrees of freedom in biliverdin, a prototypical tetrapyrrolic chromophore, cooperate to drive access to the S1/S0 intersection seam in vacuo. We begin by mapping the ground-state potential energy surface to identify representative biliverdin conformers relevant to photoexcitation. We then use a CASSCF-based framework to map the excited-state landscape and characterize the intersection seam, identifying distinct conical-intersection types. Finally, we employ ab initio multiple spawning to resolve the dynamical pathways by which the system accesses these regions. DFT potential-energy and free-energy mappings indicate that, although several conformers are relevant, the “locked-helix” ZsZsZs conformer predominates in the ground state. The intersection seam comprises numerous geometrically distinct regions characterized by varying degrees and combinations of dihedral torsion, pyramidalization, and bond-length alternation. Yet only select regions lie within energetic reach, and moderate barriers separate them from the S1 minimum. Nonadiabatic dynamics combined with multivariate analyses show that, despite extensive mode coupling during deactivation that guides the system toward multiple regions of the seam, a single dihedral torsion, together with bond-length alternation, predominantly drives energy degeneracy. This work offers new insight into biliverdin’s intrinsic photochemical response and underscores a general feature of flexible chromophores: many modes may participate during photorelaxation, but only a limited subset ultimately dictates seam accessibility.

Sequence-encoded patterning of stickers modulates biomolecular condensate reconfiguration in IDP systems

Intrinsically disordered proteins (IDPs) exert pivotal roles in Phase Separation Coupled to Percolation (PSCP), a process that drives the formation of functional biomolecular condensates linked to diverse cellular physiological activities. In this study, we investigate how sequence-encoded mesoscopic patterning modulates PSCP in IDP systems by leveraging the classic stickers-and-spacers framework, combined with coarse-grained molecular dynamics simulations. Intriguingly, our analysis demonstrates that the distribution of stickers plays important roles: compactness of sticker arrangement on IDPs exerts a substantial influence on IDP clustering process, while the patterning heterogeneity of the arrangement additionally impacts the morphology of the resulting aggregations. Subsequent findings elucidate that sparse and homogeneous stickers facilitate the emergence of robust aggregation, whereas proximal sequential organization directly induces dispersed and small clusters. These discoveries are validated through the statistical quantification of void volume fraction ϕvoid (serving as a referential measure for condensate maturation) in conjunction with the quantification of the total stickers present on the cluster surfaces. Collectively, this work may shed new lights on the underlying mechanism for regulating IDP-mediated phase separation.

Simulating quadrupolar NMR dynamics in solid electrolyte Li <b>10</b> GeP <b>2</b> S <b>12</b>

Quadrupolar solid-state nuclear magnetic resonance (NMR) spectroscopy is an excellent tool to trace lithium (Li) ion diffusion in solid electrolytes due to its sensitivity to dynamics over timescales from nanoseconds to seconds. However, the structural and dynamical complexity of battery materials limits the unambiguous interpretation of experimental data. Fast ionic motion can partially average experimentally observable quantities, leaving the underlying distribution of electric field gradients (EFGs) experimentally inaccessible and, therefore, the measured data hard to interpret. In contrast, atomic simulation approaches, while providing the structure–observable relationship, are often constrained to idealized models. Established methods such as density functional theory remain computationally expensive for realistic time and length scales. Here, we show how experimental complexity in the fast-ion conductor Li10GeP2S12 (LGPS) can be approached via a machine-learning (ML) assisted workflow. ML acceleration enables microsecond-scale molecular dynamics (MD) simulations and efficient predictions of EFG tensors via a tensorial model. By time averaging the EFG tensors from the MD trajectory, we compute the temperature dependence of 7Li NMR quadrupolar observables subject to motional narrowing. Our prediction of the quadrupolar coupling of 24 kHz for tetragonal LGPS is in excellent agreement with the experimental value of 23 kHz. Furthermore, we emulate a spin-alignment echo (SAE) experiment in silico and apply the inverse Laplace transform to extract correlation times for ionic motion of Li in different LGPS crystal structures. Finally, we assess whether SAE can differentiate inter-grain vs intra-grain ion dynamics via the orientational dependence of the EFG tensor.

Simultaneous optimization of assembly time and yield in programmable self-assembly

Rational design strategies for self-assembly require a detailed understanding of both the equilibrium state and the assembly kinetics. While the former is starting to be well understood, the latter remains a major theoretical challenge, especially in programmable systems and the so-called semi-addressable regime, where binding is often nondeterministic and the formation of off-target structures negatively influences the assembly. Here, we show that it is possible to simultaneously sculpt the assembly outcome and the assembly kinetics through the underexplored design space of binding energies and particle concentrations. By formulating the assembly process as a complex reaction network, we calculate and optimize the tradeoff between assembly speed and quality and show that parameter optimization can speed up assembly by many orders of magnitude without lowering the yield of the target structure. Although the exact speedup varies from design to design, we find the largest speedups for nondeterministic systems where unoptimized assembly is the slowest, sometimes even making them assemble faster than optimized, fully addressable designs. Therefore, these results not only solve a key challenge in semi-addressable self-assembly but further emphasize the utility of semi-addressability, where designs have the potential to be faster as well as cheaper (fewer particle species) and better (higher yield). More broadly, our results highlight the importance of parameter optimization in programmable self-assembly and provide practical tools for simultaneous optimization of kinetics and yield in a wide range of systems.

MD-BAX: A general-purpose Bayesian design framework for molecular dynamics simulations with input-dependent noise

Molecular dynamics (MD) simulations are a powerful tool for understanding complex molecular behavior, but exhaustively exploring the large space of input parameters can be computationally prohibitive, especially when the outcomes are noisy and/or expensive to evaluate. In this work, we introduce MD-Bayesian algorithm execution (BAX), a general-purpose, automated design framework that builds on BAX acquisition strategy to efficiently guide simulation campaigns toward learning meaningful features of the system. Unlike optimization-centric Bayesian optimization approaches, MD-BAX seeks to identify broader system properties (e.g., phase transition boundaries, level sets, and threshold crossings) by strategically selecting input/parameter settings based on uncertainty. To accurately represent the variability in simulation outcomes, MD-BAX incorporates a Gaussian process surrogate model with input-dependent noise, estimated directly from MD trajectory statistics at each simulation setting. This enables construction of reliable uncertainty estimates for guiding the next simulation. We demonstrate the approach on a case study involving coil-to-globule transitions in amphiphilic block copolymers, highlighting that explicitly including trajectory-derived noise improves uncertainty calibration and enables our framework to more efficiently map the relationship between polymer structure, solvent quality, and conformational behavior. MD-BAX represents a domain-informed specialization of the BAX framework for MD and is broadly applicable to molecular modeling problems where the goal is to infer key system behaviors from stochastic, trajectory-based simulation outputs rather than to locate a single optimal condition.

SHarmonic: A fast and accurate implementation of spherical harmonics for electronic-structure calculations

The authors present SHarmonic, a new implementation of the spherical harmonics targeted for electronic-structure calculations. Their approach is to use explicit formulas for the harmonics written in terms of normalized Cartesian coordinates. This approach results in a code that is as precise as other implementations while being at least one order of magnitude more computationally efficient. The library can run on graphics processing units as well, achieving an additional order of magnitude in execution speed. This new implementation is simple to use and is provided under an open-source license; it can be readily used by other codes to avoid the error-prone and cumbersome implementation of the spherical harmonics.

Diffusion crossover of protein molecules: Two-step coarse-graining and oscillating memory

A consistent treatment of anomalous diffusion requires a microcosmic framework that captures the underlying couplings between the relevant degrees of freedom. To this end, we employ a two-step coarse-graining procedure, in which protein molecules are modeled as generalized Brownian particles interacting harmonically with their neighbors, while the latter are coupled to a thermal bath. Under this construction, the power-law memory kernel in the generalized Langevin equation can be approximated as a sum of several response functions of damped oscillators, revealing, in particular, oscillatory behavior on sub-picosecond timescales. When applied to diffusion of proteins, the model predicts that the protein molecules exhibit ballistic diffusion up to ∼0.3 ps and subdiffusion up to 100 ps. Owing to the limited measurement window and the initial velocity preparation, we find that the time-averaged mean-square displacement along the reaction coordinate displays an upward-tail behavior. Finally, we extend the Markovianized dynamics to the super-diffusive regime by introducing velocity-dependent coupling.

The connection between network structure and terminal relaxation of unentangled vitrimer melts: A dynamic cross-linked Gaussian-strand model study

Vitrimers are flowable cross-linked polymer networks that have drawn significant attention as a platform for developing novel polymer materials. Here, we utilize a dynamic cross-linked Gaussian-strand model to investigate network structure–viscoelasticity relationships for unentangled vitrimer melts. The terminal relaxation of model vitrimers depends on the number of dynamic linkages and the strand-length distribution. Because the short-term dynamics are coarse-grained, the stress relaxation modulus curves of the model vitrimers exhibit well-defined plateaus. This is consistent with experimental observations, as Rouse dynamics usually occurs far faster than the relaxation of networks in most cases. For uniform model vitrimers, the distribution of relaxation times moves toward longer times as the dynamic-linkage number increases, extending the spread and changing the shape of relaxation curves. The links between the longest relaxation time and zero-shear viscosity with the dynamic-linkage number are further examined. The ratio between the maximum loss modulus and plateau modulus is also affected by the dynamic-linkage number and strand-length distribution. The intrinsic exchange reaction kinetics and segmental mobility determine the topological reshuffle of model vitrimers. The temperature dependence of their terminal relaxation is also discussed in order to gain a better grasp of their characteristic viscoelasticity.