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Femtosecond photoexcitation can abruptly redistribute electrons and trigger a series of transient nonequilibrium processes, among which ultrafast interatomic forces play a pivotal role in determining the structural and functional characteristics of solids. While ultrafast interatomic forces and their associated lattice dynamics have been extensively examined in semiconductors, experimental investigations of these nonequilibrium dynamics in metals remain lacking. To address this scientific gap, herein the direct observation of femtosecond‐scale variations in photoinduced ultrafast interatomic forces within wrinkled giant magnetostrictive FeGa thin films is presented. At the onset of demagnetization, a transient signal emerges, lasting ≈400 fs, with its orientation is influenced by the external magnetic field. Theoretical analysis indicates that this signal arises from the swift release of internal stress prompted by the suppression of magnetostriction during ultrafast demagnetization. Owing to magnetization‐induced stress anisotropy, this transient alteration in the interatomic potential introduces additional birefringence to the probe light. Consequently, this signal is attributed to a transient distortion of interatomic forces induced by the abrupt electron redistribution, establishing a nonequilibrium force state before any observable lattice expansion. These findings provide direct evidence for the existence of sub‐picosecond interatomic forces and suggest a novel approach to control metal lattice dynamics through ultrafast magnetostriction. This study reports ultrafast changes in the interatomic forces generated by femtosecond laser excitation in magnetostrictive FeGa thin films. This ultrafast process manifests as an optical birefringence signal lasting ≈400 fs. Using a pump‐probe system, the femtosecond evolution of this nonequilibrium interatomic interaction is visualized, providing new insights into ultrafast demagnetization and related atomic dynamics.

To realize the high-precision three-dimensional (3D) measurement of micro-precision devices, a 3D resonant trigger probe based on quartz tuning fork for micro/nano coordinate measuring machine (CMM) is proposed. The probe is composed of a quartz tuning fork, a tapered optical fiber stylus and a microsphere. It vibrates in resonance state and makes contact with the measured surface in the Z direction in tapping mode, while in the X and Y directions, it operates in friction mode. The 3D nano-positioning of the probe is achieved by the changes in resonance parameter caused by the interatomic force between the microsphere and the surface of the measured sample. In this study, the diameter of the probe microsphere can be as low as 80 μm, and the length of the probe stylus is approximately 5 mm. The trigger resolution of the probe in the X, Y and Z directions are 0.44, 0.41 and 0.34 nm, respectively. The probing forces in the X, Y and Z directions are 2.25, 1.81 and 4.24 μN, respectively. Experimental results verify that the proposed probe has the advantages of small size, sub-nano resolution and very low probing force. This probe can be used as the trigger probe of micro/nano CMM, which can be triggered by interatomic force.

Interatomic forces significantly influence the flow behavior and dispersion stability of the suspensions in new thermal applications. Using a molecular dynamics technique, the current work focuses on the interatomic forces are estimated. The present analysis accounts for significant contributing factors such as drag force, Brownian force, thermophoresis force, and DLVO potential force of attraction or repulsion by using several nanoparticle dispersions, including SiO 2 , CNT, and GO in distilled water as the base fluid. Among all the particles considered for the study, Carbon Nanotube (CNT) showed the highest time of stability in the base fluid. That means CNT will take more time to settle down than GO (graphene oxide) and SiO 2 due to its lowest density. The drag forces are high for GO (graphene oxide) mixed fluid due to its inherent properties. Another important finding is, below 0.05% volume fraction, the drag forces are insignificant for all the considered samples of analysis. The Brownian force is analyzed for various time intervals, which reports that for the initial periods of collision (Δt = 0.01) the molecular agitation is greater. However, increasing the Δt suppresses these forces. From the results, it is clear that the magnitude of Brownian force is dominant with CNT nanofluid, whereas it is the least intensity for SiO 2 nano fluid. The magnitude of thermophoresis forces is less than that of drag forces, and it is observed to reach its maximum intensity at T/x = 100, indicating that temperature gradient has a dominant influence on nanoparticle movements. DLVO force of nano particle mixed water is also estimated with respect to volume fraction. Increasing the volume fraction increases the intensity of these forces. These forces are maximum for GO nano fluid.

We introduce a novel approach to model heat transport in solids, based on the Green-Kubo theory of linear response. It naturally bridges the Boltzmann kinetic approach in crystals and the Allen-Feldman model in glasses, leveraging interatomic force constants and normal-mode linewidths computed at mechanical equilibrium. At variance with molecular dynamics, our approach naturally and easily accounts for quantum mechanical effects in energy transport. Our methodology is carefully validated against results for crystalline and amorphous silicon from equilibrium molecular dynamics and, in the former case, from the Boltzmann transport equation. Heat transport is a key process in designing functional materials, yet its calculations remain challenging due to large variance in material structures. Isaeva et al. introduce a unified approach counting the quantum effects, which is capable of modeling heat transport ranging from crystals to glasses.

Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression 1 . Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago 2 , several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism 1 , 3 – 5 , an anomalous melting line 6 and the possible transition to a superconducting state 7 . Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to ‘learn’ potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour. Simulations using machine-learning-based interatomic potentials in dense hydrogen overcome system size and timescale limitations, providing evidence of a supercritical behaviour of high-pressure liquid hydrogen and reconciling theoretical and experimental discrepancies.

Atomic lattice clocks have spurred numerous ideas for tests of fundamental physics, detection of general relativistic effects and studies of interacting many-body systems. On the other hand, molecular structure and dynamics offer rich energy scales that are at the heart of new protocols in precision measurement and quantum information science. Here, we demonstrate a fundamentally distinct type of lattice clock that is based on vibrations in diatomic molecules, and present coherent Rabi oscillations between weakly and deeply bound molecules that persist for tens of milliseconds. This control is made possible by a state-insensitive magic lattice trap that weakly couples to molecular vibronic resonances and enhances the coherence time of light-induced clock state superpositions by several orders of magnitude. The achieved quality factor Q = 8 × 1011 results from 30 Hz narrow resonances for a 25 THz clock transition in Sr2 molecules. Our technique of extended coherent manipulation is applicable to long-term storage of quantum information in qubits based on ultracold polar molecules, while the vibrational clock enables precise probes of interatomic forces, tests of Newtonian gravitation at ultrashort range and model-independent searches for electron-to-proton mass ratio variations.

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

We develop a fundamental theory of the long-range electrostatic interactions in two-dimensional crystals by performing a rigorous study of the nonanalyticities of the Coulomb kernel. We find that the dielectric functions are best represented by2×2matrices, with nonuniform macroscopic potentials that are two-component hyperbolic functions of the out-of-plane coordinatez. We demonstrate our arguments by deriving the long-range interatomic forces in the adiabatic regime, where we identify a formerly overlooked dipolar coupling involving the out-of-plane components of the dynamical charges. The resulting formula is exact up to an arbitrary multipolar order, which we illustrate in practice via the explicit inclusion of dynamical quadrupoles. By performing numerical tests on monolayer BN,SnS2, andBaTiO3membranes, we show that our method allows for a drastic improvement in the description of the long-range electrostatic interactions, with comparable benefits to the quality of the interpolated phonon band structure.

The stereochemical activity of lone-pair electrons critically influences lattice anharmonicity and thermal transport in crystals. However, traditional chemical substitution methods lack continuity and reversibility. We propose a strain-engineered bond angle distortion strategy in layered BiCuSeO to continuously modulate lone-pair electrons. Theoretically, tensile strain reduces the O-Bi-O bond angle, expands lone-pair electron spatial distribution, and decreases Bi-O bond charge overlap, intensifying Bi atom anharmonic vibrations. Furthermore, tensile strain induces reverse O atom vibrations and strong lattice dynamic disorder, lowering the phonon band gap and enhancing anharmonic phonon-phonon interactions and Umklapp scattering. Importantly, strain modulates lone-pair electron distribution and interaction strength without uniformly weakening long-range interatomic forces. As a result, 4% tensile strain reduces lattice thermal conductivity of BiCuSeO to 0.53 W/mK (54% decrease) at 300 K. This work establishes a multiscale framework linking strain, lone-pair electron behavior, and phonon dynamics, enabling robust and continuous control of thermal transport properties. This study shows that tensile strain continuously tunes lone-pair expression in BiCuSeO, increases lattice disorder and phonon scattering, and lowers thermal conductivity, offering an effective approach to control heat transport in materials.

Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector , a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector , which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry. Rare-earth and actinide complexes are critical for a wealth of clean-energy applications but Three dimensional (3D) structural generation and prediction for these organometallic systems remains challenging. Here, the authors propose a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes.