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Memory control of ice growth during non-equilibrium freezing of water
Freezing of supercooled water is a classic non-equilibrium problem, yet the influence of thermal history on crystallization remains unclear. Using molecular dynamics simulations with the TIP4P/Ice model, we investigate how the initial temperature Ti shapes freezing following rapid quenching to 250 K. By monitoring the evolution of hydrogen-bonded ring structures, we find a non-monotonic dependence of the freezing time tF on Ti, with the slowest crystallization occurring near 300 K. Remarkably, this means that initially hotter water can freeze faster than cooler water, a molecular-scale analogue of the Mpemba effect. A non-stationary generalized Langevin equation framework shows that two-time memory kernels retain information about the system’s thermal past, directly influencing crystallization dynamics. Structural analysis further reveals that five-membered rings act as kinetic traps, while correlations among ring types regulate the accessibility of ice-like motifs. These results uncover a molecular origin of memory-driven freezing and establish structural memory as a key driver of non-equilibrium phase transitions.
Residual stress behaviors in sputter-deposited BCC solid solution alloys
Metal alloy films are used in many technical applications such as magnetic storage, catalysis, and hard coatings. As with any coating, residual stress is critical to their adhesion and physical properties. While there have been numerous studies of residual stress evolution in elemental metal systems, alloy films are less understood. In this work, we present in situ measurements of the stress in Cr–W and V–Mo alloys of different compositions at different growth rates. The stress is quantitatively analyzed (along with previous results in V–W) using a model for stress in alloys that is based on similar mechanisms invoked for elemental systems. The modeling allows the measured change in stress with composition to be related to the underlying stress-generating mechanisms. Adding a lower-mobility element to a higher mobility, one suppresses surface mobility, leading to less compressive stress from non-energetic growth kinetics. However, it also decreases defect mobility, which enhances the stress due to energetic impacts. Adding heavier, higher-melting-point elements enhances compressive stress generated by energetic particle impacts. As a result, W-containing alloys become increasingly compressive with higher W content, whereas in V–Mo, the competing growth kinetic and energetic impact contributions are comparable, leading to a non-monotonic dependence of stress on composition for different growth rates.
Machine learning accelerated nonadiabatic molecular dynamics of defect-mediated recombination in alkali metal passivated Cu2ZnSnS4
Nonradiative electron–hole recombination mediated by defects is a critical loss mechanism in semiconductors and requires computationally demanding nonadiabatic (NA) molecular dynamics simulations. In this work, we develop a machine learning-accelerated framework to model long-time NA couplings in Cu2ZnSnS4 containing CuZn antisites and CuZn + ZnCu antisite pairs, with and without alkali metal passivation. By formulating NA coupling evolution as a time-series prediction problem, we benchmark 37 representative deep learning architectures, including recurrent neural networks, convolutional neural networks, transformers, and hybrid models. Among them, the extended long short-term memory model achieves the best overall performance, yielding an average test set R2 of 0.98 while maintaining high computational efficiency. This approach enables accurate reconstruction of long-time NA coupling trajectories at a prediction cost reduced by over five orders of magnitude relative to repeated direct first-principles NA coupling evaluations. The application of the framework shows that alkali metal doping systematically reduces the NA coupling strength and accelerates decoherence, with Li exhibiting the strongest suppression of recombination, which is associated with enhanced charge localization and lattice fluctuations. The predicted recombination lifetimes agree closely with first-principles results, validating the reliability of the machine learning approach. This work establishes a generalizable strategy for machine learning-assisted NA dynamics simulations and provides mechanistic insights into defect-mediated recombination processes.
Machine-learning molecular dynamics prediction of frequency-dependent damping induced by Phonon–Phonon interactions in single-crystalline silicon resonators
Phonon–phonon interactions are a key intrinsic mechanism governing energy dissipation in resonators operating in the GHz to THz range, directly limiting the attainable quality factor (Q). In this work, a computational framework is established that integrates density functional theory (DFT), machine-learning-based Deep Potential (DP) training, and large-scale molecular dynamics (MD) simulations to investigate phonon–phonon interactions in single-crystalline silicon length extensional resonators. A DP model trained on DFT data is shown to reproduce forces and energies with high accuracy while enabling simulations at nanometer to sub-micrometer length scales. Within this framework, the dependence of f × Q on resonant frequency, temperature, and crystal orientation is systematically quantified. As the frequency increases from the GHz to the THz regime, Q exhibits a moderate decrease whereas f × Q increases. This behavior can be traced to a pronounced reduction in the phonon relaxation time. For a 74.7 GHz resonator, Q follows an approximate 1/T scaling, consistent with significant Akhiezer-like phonon–phonon damping, while possible boundary and finite-size contributions cannot be completely excluded in the present nanometer-scale geometries. Orientation dependent simulations further reveal that resonators aligned along the 〈110〉 crystal orientation exhibit systematically lower Q and smaller f × Q than those aligned along the 〈100〉 crystal orientation, due to enhanced phonon anisotropy and stronger coupling between acoustic and thermal phonons. These results provide an atomistic and quantitative picture of phonon–phonon interactions in silicon resonators and demonstrate that the proposed framework offers a general route for predicting and optimizing intrinsic damping in resonators based on silicon and other crystalline materials with diverse device geometries.
Controlling ⟨Ŝ2⟩ in broken-symmetry density functional theory calculations via constrained optimization
Accurate determination of magnetic exchange coupling constants (J) from density functional theory (DFT) remains challenging, particularly for open-shell systems where broken-symmetry (BS) solutions suffer from spurious spin contamination that systematically exaggerates J values. Several methods have been proposed to address this problem by adjusting the mapping scheme from the DFT energies to the Heisenberg–Dirac–van Vleck effective spin Hamiltonian energies. In this work, we explore a different route by imposing a constraint on the DFT energy that enforces a target value of the spin-squared expectation, ⟨Ŝ2⟩, using a Lagrange multiplier approach. By explicitly controlling the spin character of the electronic state, the method attempts to overcome limitations of standard BS calculations to describe magnetic interactions. As part of the theoretical formulation, we derive analytical expressions for the gradient of the spin-squared expectation value with respect to the spin-resolved density matrices, which are required for the practical implementation of the constraint within a generalized Kohn–Sham scheme. These expressions are general to any single-determinant method and remain valid for arbitrary spin states. We apply the spin-constrained approach to the calculation of J couplings and compare with three energy-difference-based schemes for a set of representative systems, including H2He, H3He3 arranged in an equilateral triangle, and a bis(μ-hydroxo) Cu(II) complex. Across all cases, the constrained formulation yields systematically lower and more consistent exchange couplings across different density functional approximations. This work establishes a robust and general route for incorporating spin-state constraints into DFT-based studies of magnetic exchange interactions.
Influence of strain rate and phase history on the spall failure of single and polycrystal tin
Spall occurs when materials are subjected to shock impacts; under this loading, the material properties can be modified through microstructural changes and phase transitions. The effect of these changes on subsequent spall has been under-explored. The anisotropy of tin’s ambient crystal structure and the accessibility of the β → γ solid–solid phase transition under shock loading mean that tin offers a rich domain in which to study spall failure. Through testing single-crystal and polycrystal samples shocked above and below this transition, the effects of these variables on the deformation behavior of tin can be determined. Although no orientation dependent spall behavior is observed, unusual strain-rate-dependent behavior is observed, indicating likely mechanisms for the high-rate behavior of tin.
Generalized path reweighting and history-dependent free energies
Transition interface sampling (TIS) and replica exchange TIS (RETIS) are powerful methods for computing rates of rare events inaccessible to straightforward molecular dynamics simulations. Path reweighting extends their output, enabling the evaluation of diverse thermodynamic and kinetic quantities, including reaction prediction metrics, activation barriers, committor functions, and free energies. The recently developed ∞RETIS algorithm boosts parallel efficiency through asynchronous replica exchanges in the infinite-swap limit, thereby eliminating the wall-time bottlenecks of conventional RETIS. This approach introduces fractional samples and biased sampling distributions, requiring a generalized path reweighting framework, for which we derive expressions demonstrating how exact dynamic and thermodynamic variables can be computed. We then focus on a special class of free energy surfaces defined by history-dependent conditions, whose values are influenced by kinetic factors such as particle mass and friction, unlike standard unconditional free energy surfaces. Even with suboptimal reaction coordinates, these conditional free energies can reveal kinetically relevant barriers that may be misrepresented by standard unconditional free energies, thereby providing a rigorous and versatile tool for characterizing complex molecular transitions.
Impact of luminescence coupling on two-terminal dual-band photodetector performance: A comparative modeling study
In this work, we developed two equivalent-circuit models for both back-to-back and optically addressed dual-band two-terminal photodetector architectures to investigate the impact of luminescence coupling on device performance. The equivalent circuits were constructed using a two-equivalent-diode method. Simulations were conducted using consistent parameters for both architectures to isolate luminescence coupling effects and enable direct comparison. Results show that luminescence coupling is present in both architectures but is significantly enhanced in the optically addressed architecture, leading to a reduction in the specific detectivity and signal-to-noise ratio compared to the back-to-back architecture. Notably, the two architectures exhibit distinct behavior: luminescence coupling increases with signal intensity in the back-to-back design, while it decreases in the optically addressed design. These results highlight the critical role of luminescence coupling in multiband devices and the importance of architecture selection for performance optimization in infrared photodetectors.
Effects of rim fluctuations in classical nucleation theory of virus capsids
Most spherical viruses exhibit icosahedral symmetry, yet the growth of viral shells remains poorly understood due to the short lifetimes and broad size distribution of assembly intermediates. Classical nucleation theory has been widely applied to describe this process, but it treats the boundary of a growing shell as rigid and structureless. Here, we extend classical nucleation theory by incorporating thermal fluctuations of the capsid rim using both discrete and continuum descriptions. Allowing the rim of a partially formed capsid to undergo small geometric undulations, we show that these fluctuations generate an entropic contribution that renormalizes the effective line tension. As a result, rim fluctuations can either promote or hinder capsid closure, depending on the subunit–subunit binding free energy, temperature, and fluctuation amplitude. We find that fluctuations generally lower the nucleation barrier when the binding free energy is below a threshold value, while for sufficiently strong binding, they can instead raise the barrier by stabilizing incomplete capsids through a finite-size entropy penalty associated with rim closure. By moving beyond the idealized capillarity approximation, our results provide a controlled extension of classical nucleation theory that clarifies how boundary fluctuations influence capsid nucleation and growth.
Signatures of the Kondo effect in Fe-diluted tungsten ditelluride single crystal
This paper presents the results of the synthesis, characterization, and transport properties of bulk single crystalline tungsten ditelluride WTe2 doped with 3% iron. Our discussion begins with a comparison between pure WTe2 and W0.97Fe0.03Te2, where we report significant modifications in the electrical, magneto-, and Hall resistivities resulting from doping. A distinctive feature of W0.97Fe0.03Te2 is the presence of a low-temperature minimum in the temperature dependence of its electrical resistivity, while below 10 K, the resistivity increases almost logarithmically with decreasing temperature. We demonstrate that the region encompassing the resistivity minimum can be roughly described by the Hamann equation. Moreover, the field dependence of magnetoresistance exhibits an almost quadratic negative behavior at low temperatures. We attribute these features to the Kondo effect, arising from the peculiarities of conduction electron scattering on magnetic Fe-atom impurities.
Structure-resolved free-energy estimation of the 38-atom Lennard-Jones cluster via population annealing
We systematically investigate the thermodynamic landscape of the 38-atom Lennard-Jones cluster LJ38 using Population Annealing (PA), a method suited for systems with challenging double-funnel energy landscapes. By employing an adaptive temperature schedule, we demonstrate that thermodynamic observables, such as internal energy and heat capacity, converge robustly when the population size is sufficiently large. To gain deeper insights into the competing basins, we introduce an integrated framework that combines PA reweighting factors with structure-resolved analysis. Using quenched configurations characterized by potential energy and Steinhardt’s bond-orientational order parameters, we identify three structural basins, face-centered cubic-like, icosahedral, and liquid-like, via dimensionality reduction and clustering. This framework enables the direct computation of structure-resolved free energy differences from population fractions, providing a quantitative mapping of the thermodynamic competition between the funnels. The resulting structural crossovers are consistent with the heat-capacity peak, demonstrating PA as a promising and scalable framework for structure-resolved thermodynamics in complex molecular systems.
Unraveling gas–solid interface charging mechanisms of insulator based on three-phase dominant charge behaviors
In surface science, surface flashover is a plasma discharge phenomenon that occurs along the gas–solid interface of an insulator under a high electric field (E), threatening the operational reliability of power systems and advanced electronic devices. Owing to the complex charge transport across the gas–solid interface, the underlying physical mechanism of surface charging remains unclear, limiting the development of flashover theory and surface-modification technologies. Herein, surface gas ionization and surface charge-dissipation equations are proposed to establish a surface-charging model for insulators, and the effects of three-phase (gas, solid, and interface) transport parameters on surface charging are investigated. The results reveal an E-dependent competitive mechanism among the gas–solid–interface charge transport processes: Enhancing gas insulation strength and bulk conductivity effectively suppresses heteropolar charge accumulation and shifts the charge-polarity reversal point (Er) to higher E, with surface charging transitioning from gas-ionization-dominated to bulk-conductivity-dominated behavior. In contrast, increasing surface conductivity accelerates charge dissipation, causing surface charging to be governed by surface charge dissipation. Based on the dominant charging mechanisms, surface-modification strategies, including O3 treatment, surface polishing, and EP/SiC coating, are applied to insulators; all methods reduce surface-charging density and increase surface flashover voltage. This study establishes a systematic competitive framework for three-phase charge transport in surface charging, providing a theoretical foundation for dielectric surface charging/discharging theory and for surface modification in advanced electrical and electronic applications.
The correlation discrete variable representation revisited
The correlation discrete variable representation (CDVR) enables efficient quantum dynamics calculation with the multi-layer multi-configurational time-dependent Hartree approach on general potential energy surfaces. It employs a time-dependent quadrature to compute potential energy matrix elements, thereby eliminating the need to refit the potential to a sum of products form. The non-hierarchical CDVR conserves the inherent symmetry properties of tree-shaped wavefunction representations and drastically reduces the number of grid points compared to the original hierarchical CDVR. However, it requires projection on the space spanned by the single-hole functions at each node of the tree, which can introduce unphysical couplings for unconverged basis sets. In this work, the non-hierarchical CDVR is revisited, and a revised approach that avoids explicit projection on the single-hole space is introduced. The computational costs of the revised approach scale favorably with the number of single-particle functions (SPFs): for a tree with three edges at each node and n SPFs at each edge, an n4 scaling is achieved. Furthermore, a revised scheme that uses artificial SPFs to systematically increase the accuracy of the CDVR quadrature is presented. Computations studying the photodissociation of NOCl, the vibrational states of methyl, and the non-adiabatic quantum dynamics of photoexcited pyrazine demonstrate the accuracy and efficiency of the revised non-hierarchical CDVR. Notably, for the 24-dimensional pyrazine system, the use of the CDVR does not increase the required wall clock time compared to calculations utilizing the sum of products form of the vibronic coupling model.
Physics-informed machine learning model for accurate prediction of electron affinities
Accurate wavefunction methods such as CCSD(T) can predict chemical and thermodynamic properties of small molecules with near-experimental precision. However, their steep computational cost limits their use for large systems or extensive datasets. In contrast, density functional theory (DFT) is faster and more practical for large molecules but often fails to accurately capture electronic changes with quantitative accuracy. To address these limitations, we have developed a machine learning model for the prediction of electron affinities with physics-based structural features (RDKit and SMARTS) and quantum chemistry-based electronic features (Mulliken charge analysis). These features, which capture both local bonding motifs and global electronic context, are used as input descriptors to an XGBR (eXtreme Gradient Boosting Regressor) model in a ΔML framework. By embedding QM-based electronic features to the ΔML model, our enhanced ΔML+ model attains a mean absolute error of 0.03 eV with respect to G4MP2 values, surpassing conventional chemical accuracy targets while exhibiting markedly reduced dependence on the underlying DFT functional. By analyzing both vertical and adiabatic EAs within the same framework, we highlight the crucial role of geometry relaxation in predictive modeling. Overall, our approach offers an efficient and transferable route to benchmark accurate electron affinity predictions, pointing toward next generation computational protocols that overcome the limitations of standalone DFT.
High repetition rate picosecond pulse terahertz parametric amplifier driven by a synchronized fiber laser
Applications such as observing transient phenomena in materials and real-time terahertz (THz) spectroscopic imaging of molecules have highlighted the importance of high repetition rate, high power THz radiation sources with a broad frequency tuning range. Nevertheless, the development of such sources remains a bottleneck, hindering the advancement of these applications. We have recently developed a high repetition rate, picosecond-pulsed THz parametric amplifier system driven by a passively mode-locked femtosecond fiber laser, which can be synchronized to an external reference signal with sub-picosecond timing jitter. The system operates stably with a frequency tuning range from 1 to 6 THz. It achieves a maximum peak power of 4.3 kW at 5.7 THz when operating at 10 kHz and a maximum average power of 470 μW at the same frequency under 100 kHz operation. This THz source can not only serve as a seed for THz free-electron laser amplifiers, but also provide precise timing control for various THz applications.
Spin–orbit effects on the molecular properties of Group 14 tetracoordinate compounds TX4 (T = Ge, Sn, and Pb; X = H, F, Cl, Br, and I): Natural electron configuration-based rationalization of structural variations
The spin–orbit (SO) effects on the molecular properties of Group 14 tetracoordinate compounds TX4 (T = Ge, Sn, and Pb; X = H, F, Cl, Br, and I) were systematically investigated using two-component spin–orbit density functional theory with the PBE0 and MN15 functionals. The spin–orbit coupling (SOC)-induced changes in the T–X equilibrium bond lengths exhibit a non-monotonic pattern across the 15 TX4 compounds, governed by the competition between central-atom p1/2 contraction and ligand p3/2 elongation. The natural electron configuration-based spinor occupation inference (NSOI) framework, extended from diatomic to polyatomic systems, accounts for all observed structural variations, including the counterintuitive enhancement of bond contraction from TF4 to TCl4 and the crossover from contraction to elongation in PbI4. For the TX2 series, the NSOI framework consistently rationalizes not only the SOC-induced bond length changes but also the bond angle changes, which are interpreted as secondary geometric consequences of changes in bond lengths and X–X interactions. The robustness of the NSOI approach was confirmed with the MN15 functional. The reaction energies for TX4 → TX2 + X2 decrease systematically from Ge to Pb, consistent with the inert-pair effect. Time-dependent density functional theory calculations reveal that SOC activates singlet–triplet mixing, transforming the UV–Vis absorption spectra of heavy-atom TX4 and providing theoretical reference data for experimentally elusive species such as PbX4. In contrast to the structural SO effects, which are attenuated by partial cancellation between central-atom and ligand contributions, the spectral SO effects are more pronounced because singlet–triplet mixing introduces absorption features absent in the scalar-relativistic spectra.
Reduction of red-kink effect in Cu(In,Ga)Se2 thin-film bottom solar cells for tandem devices by sodium induced passivation of defects in CdS buffer layer
When using Cu(In,Ga)Se2 (CIGS) thin-film solar cells as bottom cells in tandem devices, they are not illuminated with blue light. In this case, the red-kink effect, a distortion in the current–density voltage (JV) curve under illumination with red-light can be very pronounced and limit the efficiency of the bottom cell. JV measurements with different edge transmission filters revealed that the reason for the red-kink effect is located in the CdS buffer layer. The red-kink effect is very pronounced in sodium-free CIGS solar cells and can be strongly reduced by doping of CIGS with sodium (Na) or white light soaking. We observe a connection between the red-kink effect and the grain size of the CIGS layer. The red-kink effect in Na-free fine-grained CIGS is observed to be higher than in samples with coarse-grained CIGS. In contrast, after Na doping of the CIGS layer, the red-kink effect is more strongly reduced in fine-grained CIGS layers, which can be explained by better Na diffusion out of CIGS grain boundaries into the CdS layer in the case of fine-grained CIGS. The JV curves could be simulated by acceptor-like defects in the CdS layer, which lead to compensated CdS. These defects can either be passivated by Na or by illumination with blue light, leading to a decrease in the red-kink effect. Hence, a sufficient supply of Na into the CdS layer is necessary to passivate these defects in the CdS layer and to produce high efficient CIGS bottom cells for tandem applications.
On the importance of relativistic corrections to the vibrational averaging of molecular properties: A zeroth-order regular approximation study on selected mercury compounds
Relativistic effects play a crucial role in the accurate prediction of spectroscopic properties of heavy-element compounds, yet their impact on vibrational corrections remains insufficiently explored. In this work, we assess the influence of scalar and spin–orbit relativistic treatments on vibrational corrections to electric field gradients (EFGs), nuclear magnetic resonance (NMR) shielding constants, chemical shifts, and spin–spin coupling constants (SSCCs) for seven mercury(II) compounds: HgCl2, HgBr2, HgI2, Hg(SH)2, H3CHgCl, H3CHgBr, and H3CHgI. Calculations were performed within density functional theory using the BHandHLYP functional for EFGs and PBE0 for NMR parameters, combined with the Zeroth-Order Regular Approximation (ZORA) scalar and spin–orbit relativistic approaches. Vibrational averaging employed the QZ4P basis set primarily, with QZ4P-J used for SSCCs, and additional basis-set tests were carried out for HgCl2. Our results demonstrate that relativistic effects substantially modify vibrational corrections for all investigated properties and that scalar relativistic treatments alone are generally insufficient. While replacing QZ4P with TZ2P for cubic force constants or property derivatives yields only minor absolute deviations, the relative changes can be significant due to the small magnitude of the corrections. The inclusion of zero-point vibrational effects at the spin–orbit ZORA level consistently improves agreement with experimental data. Methodological investigations further reveal that accurate numerical derivatives require larger step lengths for these compounds than typically assumed, with an optimal value near 0.5 for HgCl2. Overall, this study highlights the necessity of incorporating spin–orbit relativistic effects in vibrational corrections for heavy-element spectroscopic properties and provides guidance for robust computational protocols.
Power limitations of nanosecond high-voltage pulsers in atmospheric pressure discharges
This work investigates the operational limitations of commercial nanosecond pulsers when driving atmospheric pressure discharges. We demonstrated how these limitations can lead to unstable and incorrect results in plasma parameter measurements. Through a combination of resistive loads and using atmospheric pressure plasma jets, we experimentally demonstrated that when the nanosecond pulser is pushed to its maximum performance limits (high voltage, high frequency, and long pulse width), it becomes power-limited, causing the output voltage to collapse. This power-limited state creates a feedback loop with the plasma, leading to frequent transitions between different discharge regimes (filament and glow). In our case, this instability is driven by small, 500 V oscillations in the applied voltage, and traditional measurement techniques relying on multi-shot averaging might become unreliable under these conditions. We demonstrated that tuning of nanosecond pulser parameters can significantly improve the stability and performance of the plasma discharge, which can be verified by long, consecutive-shot scanning.
Steering and collimation of electron beams in graphene by a phased array
We theoretically demonstrate that a set of laterally arranged p–n barriers in a graphene sample, with heights modulated by a linear potential, can act as an electronic phased array. The phase accumulated by electrons as they traverse each barrier produces a progressive phase shift between adjacent emitters, enabling control of the emission angle of the resulting beam. By combining an analytical model based on the massless Dirac equation with numerical quantum-transport simulations, we identify the condition for maximum intensity and derive the relationship between the emission angle, the potential gradient, and the carrier energy. The device operates coherently and allows continuous steering or collimation of the electron beam by tuning the electrostatic profile.