Computer simulation of local order in condensed phases of silicon

Cyclic Wear Reliability of 2D Monolayers

Understanding wear, a critical factor impacting the reliability of mechanical systems, is vital for nano-, meso-, and macroscale applications. Due to the complex nature of nanoscale wear, the behavior of nanomaterials such as two-dimensional materials under cyclic wear and their surface damage mechanism is yet unexplored. In this study, we used atomic force microscopy coupled with molecular dynamic simulations to statistically examine the cyclic wear behavior of monolayer graphene, MoS2, and WSe2. We show that graphene displays exceptional durability and lasts over 3000 cycles at 85% of the applied critical normal load before failure, while MoS2 and WSe2 last only 500 cycles on average. Moreover, graphene undergoes catastrophic failure as a result of stress concentration induced by local out-of-plane deformation. In contrast, MoS2 and WSe2 exhibit intermittent failure, characterized by damage initiation at the edge of the wear track and subsequent propagation throughout the entire contact area. In addition to direct implications for MEMS and NEMS industries, this work can also enable the optimization of the use of 2D materials as lubricant additives on a macroscopic level.

Intrinsic Stress Field for Liquid Surfaces

The microscopic stress field inhomogeneity in the interfacial region adjacent to the liquid surface is the fundamental origin of the liquid surface tension, but because of broadening due to capillary fluctuations, a detailed molecular level understanding of the stress field remains elusive. In this work, we deconvolute the capillary fluctuations to reveal the intrinsic stress field and show that the atomic-level contributions to the surface tension are similar in functional form across a variety of monatomic systems. These contributions are confined to an interfacial region approximately 1.5±0.1 times the particle diameter for all systems studied. In addition, the intrinsic density and stress profiles show a strong spatial correlation that should be useful in the development of a statistical mechanical theory for the prediction of surface stress and surface tension.

Mesomorphology of clathrate hydrates from molecular ordering

Clathrate hydrates are crystals formed by guest molecules that stabilize cages of hydrogen-bonded water molecules. Whereas thermodynamic equilibrium is well described via the van der Waals and Platteeuw approach, the increasing concerns with global warming and energy transition require extending the knowledge to non-equilibrium conditions in multiphase, sheared systems, in a multiscale framework. Potential macro-applications concern the storage of carbon dioxide in the form of clathrates, and the reduction of hydrate inhibition additives currently required in hydrocarbon production. We evidence porous mesomorphologies as key to bridging the molecular scales to macro-applications of low solubility guests. We discuss the coupling of molecular ordering with the mesoscales, including (i) the emergence of porous patterns as a combined factor from the walk over the free energy landscape and 3D competitive nucleation and growth and (ii) the role of molecular attachment rates in crystallization-diffusion models that allow predicting the timescale of pore sealing. This is a perspective study that discusses the use of discrete models (molecular dynamics) to build continuum models (phase field models, crystallization laws, and transport phenomena) to predict multiscale manifestations at a feasible computational cost. Several advances in correlated fields (ice, polymers, alloys, and nanoparticles) are discussed in the scenario of clathrate hydrates, as well as the challenges and necessary developments to push the field forward.

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

Liquid-liquid transition and ice crystallization in a machine-learned coarse-grained water model

Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water has impeded identification of the LLT line TLL(p) in experiments. While the most accurate all-atom (AA) water models display a LLT, their computational cost limits investigations of its interplay with ice formation. Coarse-grained (CG) models provide over 100-fold computational efficiency gain over AA models, enabling the study of water crystallization, but have not yet shown to have a LLT. Here, we demonstrate that the CG machine-learned water model Machine-Learned Bond-Order Potential (ML-BOP) has a LLT that ends in a critical point at pc = 170 ± 10 MPa and Tc = 181 ± 3 K. The TLL(p) of ML-BOP is almost identical to the one of TIP4P/2005, adding to the similarity in the equation of state of liquid water in both models. Cooling simulations reveal that ice crystallization is fastest at the LLT and its supercritical continuation of maximum heat capacity, supporting a mechanistic relationship between the structural transformation of water to a low-density liquid (LDL) and ice formation. We find no signature of liquid-liquid criticality in the ice crystallization temperatures. ML-BOP replicates the competition between formation of LDL and ice observed in ultrafast experiments of decompression of the high-density liquid (HDL) into the region of stability of LDL. The simulations reveal that crystallization occurs prior to the coarsening of the HDL and LDL domains, obscuring the distinction between the highly metastable first-order LLT and pronounced structural fluctuations along its supercritical continuation.

Super-Suppression of Long-Wavelength Phonons in Constricted Nanoporous Geometries

In a typical semiconductor material, the majority of the heat is carried by long-wavelength, long-mean-free-path phonons. Nanostructuring strategies to reduce thermal conductivity, a promising direction in the field of thermoelectrics, place scattering centers of size and spatial separation comparable to the mean free paths of the dominant phonons to selectively scatter them. The resultant thermal conductivity is in most cases well predicted using Matthiessen's rule. In general, however, long-wavelength phonons are not as effectively scattered as the rest of the phonon spectrum. In this work, using large-scale molecular-dynamics simulations, non-equilibrium Green's function simulations, and Monte Carlo simulations, we show that specific nanoporous geometries that create narrow constrictions in the passage of phonons lead to anticorrelated heat currents in the phonon spectrum. This effect results in super-suppression of long-wavelength phonons due to heat trapping and reductions in the thermal conductivity to values well below those predicted by Matthiessen's rule.

Unraveling thermodynamic anomalies of water: A molecular simulation approach to probe the two-state theory with atomistic and coarse-grained water models

Thermodynamic and dynamic anomalies of water play a crucial role in supporting life on our planet. The two-state theory attributes these anomalies to a dynamic equilibrium between locally favored tetrahedral structures (LFTSs) and disordered normal liquid structures. This theory provides a straightforward, phenomenological explanation for water's unique thermodynamic and dynamic characteristics. To validate this two-state feature, it is critical to unequivocally identify these structural motifs in a dynamically fluctuating disordered liquid. In this study, we employ a recently introduced structural parameter (θavg) that characterizes the local angular order within the first coordination shell to identify these LFTSs through molecular dynamics simulations. We employ both realistic water models with a liquid-liquid critical point (LLCP) and a coarse-grained water model without an LLCP to study water's anomalies in low-pressure regions below 2 kbar. The two-state theory consistently describes water's thermodynamic anomalies in these models, both with and without an LLCP. This suggests that the anomalies predominantly result from the two-state features rather than criticality, particularly within experimentally accessible temperature-pressure regions.

Transforming friction: unveiling sliding-induced phase transitions in CVD-grown WS2 monolayers under single-asperity sliding nanocontacts

Transition metal dichalcogenides (TMDs) exhibit diverse properties across different phases, making them promising materials for various engineering applications. In the present work, we employed a comprehensive approach, combining experimental investigations and computational simulations to elucidate the remarkable tunable frictional characteristics of chemical vapor deposition (CVD) grown WS2 monolayers through the sliding-induced transitions between the 1H and 1T' phases. Our atomic force microscopy (AFM) measurements reveal a significant contrast in friction between the two phases, with the 1H phase displaying higher friction (∼52%) than the 1T' phase. Surprisingly, under repeated scanning at constant stress, the friction of the 1H phase decreases, eventually matching the lower friction values of the 1T' phase. It was observed that the phase transformation is irreversible and is strongly dependent on contact stresses and is accelerated as the contact stress is increased by increasing the applied normal load. Molecular dynamics (MD) simulations provide further insights into the phase transition mechanism, highlighting the role of localized lateral stress and strain induced by sliding an AFM tip on the 1H phase. The simulations confirm that sliding induced localized lateral strain plays a crucial role in the phase transition, ultimately resulting in a decrease in friction. Moreover, our simulations unveil an intriguing connection between friction, potential energy surfaces, and the localized lateral strain during the phase transformation process. Our findings not only offer insights into the tribological properties of TMD materials but also open new possibilities for tailoring their performance in various applications where reducing friction and wear is crucial.

GeSn-on-GaAs with photoconductive carrier lifetime >400 ns: role of substrate orientation and atomistic simulation

Group IV GeSn quantum material finds application in electronics and silicon-compatible photonics. Synthesizing these materials with low defect density and high carrier lifetime is a potential challenge due to lattice mismatch induced defects and tin segregation at higher growth temperature. Recent advancements in the growth of these GeSn materials on Si, Ge, GaAs, and with substrate orientations, demonstrated different properties using epitaxial and chemical deposition methods. This article addresses the effect of GaAs substrate orientation and misorientation on the materials' properties and carrier lifetimes in epitaxial Ge0.94Sn0.06 layers. With starting GaAs substrates of (100)/2°, (100)/6°, (110) and (111)A orientations, Ge0.94Sn0.06 epitaxial layers were grown with an intermediate Ge buffer layer by molecular beam epitaxy and analyzed by several analytical tools. X-ray analysis displayed good crystalline quality, and Raman spectroscopy measurements showed blue shifts in phonon wavenumber due to biaxial compressive strain in Ge0.94Sn0.06 epilayers. Cross-sectional transmission electron microscopy analysis confirmed the defect-free heterointerface of Ge0.94Sn0.06/Ge/GaAs heterostructure. Minority carrier lifetimes of the unintentionally doped n-type Ge0.94Sn0.06 epilayers displayed photoconductive carrier lifetimes of >400 ns on (100)/6°, 319 ns on (100)/2°, and 434 ns on (110) GaAs substrate at 1500 nm excitation wavelength. On the other hand, Ge0.94Sn0.06 layer showed poor carrier lifetime on (111)A GaAs substrate. The observed differences in carrier lifetimes were correlated with the formation energy of the Ge on (100)/6° and (100)/2° GaAs heterointerface using Stillinger-Weber interatomic potential model-based atomistic simulation with different heterointerfacial bonding by Synopsys QuantumATK tool. Total energy computation of 6280-atom Ge/GaAs supercell on (100)/6° leads to lower formation energy than (100)/2°, making it more thermodynamically stable. Hence, the growth of the GeSn/III-V material system using misoriented (100) substrates that are more thermodynamically stable will enhance the performances of optoelectronic devices.

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

Using machine learning with atomistic surface and local water features to predict heterogeneous ice nucleation

Heterogeneous ice nucleation (HIN) has applications in climate science, nanotechnology, and cryopreservation. Ice nucleation on the earth's surface or in the atmosphere usually occurs heterogeneously involving foreign substrates, known as ice nucleating particles (INPs). Experiments identify good INPs but lack sufficient microscopic resolution to answer the basic question: What makes a good INP? We employ molecular dynamics (MD) simulations in combination with machine learning (ML) to address this question. Often, the large amount of computational cost required to cross the nucleation barrier and observe HIN in MD simulations is a practical limitation. We use information obtained from short MD simulations of atomistic surface and water models to predict the likelihood of HIN. We consider 153 atomistic substrates with some surfaces differing in elemental composition and others only in terms of lattice parameters, surface morphology, or surface charges. A range of water features near the surface (local) are extracted from short MD simulations over a time interval (≤300 ns) where ice nucleation has not initiated. Three ML classification models, Random Forest (RF), support vector machine, and Gaussian process classification are considered, and the accuracies achieved by all three approaches lie within their statistical uncertainties. Including local water features is essential for accurate prediction. The accuracy of our best RF classification model obtained including both surface and local water features is 0.89 ± 0.05. A similar accuracy can be achieved including only local water features, suggesting that the important surface properties are largely captured by the local water features. Some important features identified by ML analysis are local icelike structures, water density and polarization profiles perpendicular to the surface, and the two-dimensional lattice match to ice. We expect that this work, with its strong focus on realistic surface models, will serve as a guide to the identification or design of substrates that can promote or discourage ice nucleation.

Abnormal condensation of water vapour at ambient temperature

The homogeneous condensation of water vapor at ambient temperature is studied using molecular dynamics simulation. We reveal that there is a droplet size at the nanoscale where water droplets can be stabilized in the condensation process. Our simulations show that the growth of water droplets is dominated by collision and coagulation between small water droplets after nucleation. This process is found to be accompanied by exceptionally fast evaporation such that droplet growth is balanced by evaporation when water droplets grow to a critical size, approximately 12.5 Å in radius, reaching a stable size distribution. The extremely high evaporation rate is attributed to the curvature dependence of surface tension. Surface tension shows a significant decrease with decreasing droplet size below 20 Å, which causes the total free energy of nanoscaled water droplets to rise after collision and coagulation. Consequently, water droplets have to shrink via fast evaporation. The curvature dependence of surface tension is related to the dielectric ordering of water molecules near the surface of water droplets. Owing to fast evaporation, secondary condensation occurs, and many small water clusters form, ultimately exhibiting a bimodal distribution of water-droplet size.

Area-selective atomic layer deposition on 2D monolayer lateral superlattices

The advanced patterning process is the basis of integration technology to realize the development of next-generation high-speed, low-power consumption devices. Recently, area-selective atomic layer deposition (AS-ALD), which allows the direct deposition of target materials on the desired area using a deposition barrier, has emerged as an alternative patterning process. However, the AS-ALD process remains challenging to use for the improvement of patterning resolution and selectivity. In this study, we report a superlattice-based AS-ALD (SAS-ALD) process using a two-dimensional (2D) MoS2-MoSe2 lateral superlattice as a pre-defining template. We achieved a minimum half pitch size of a sub-10 nm scale for the resulting AS-ALD on the 2D superlattice template by controlling the duration time of chemical vapor deposition (CVD) precursors. SAS-ALD introduces a mechanism that enables selectivity through the adsorption and diffusion processes of ALD precursors, distinctly different from conventional AS-ALD method. This technique facilitates selective deposition even on small pattern sizes and is compatible with the use of highly reactive precursors like trimethyl aluminum. Moreover, it allows for the selective deposition of a variety of materials, including Al2O3, HfO2, Ru, Te, and Sb2Se3.

Roughness Evolution Induced by Third-Body Wear

Surface roughness is a key factor when it comes to friction and wear, as well as to other physical properties. These phenomena are controlled by mechanisms acting at small scales, in which the topography of apparently flat surfaces is revealed. Roughness in natural surfaces has been reported to conform to self-affine statistics in a wide variety of settings (ranging from earthquake physics to micro-electro-mechanical devices), meaning that the height profile can be described using a spectrum where the amplitude is proportional to its wavelength raised to a constant power, which is related to a statistical parameter named Hurst exponent. We analyze the roughness evolution in atomistic surfaces during molecular dynamics simulations of wear. Both pairs of initially flat and initially rough surfaces in contact are worn by a third body formed by particles trapped between them during relative sliding. During the first sliding stages, the particles trapped between the first bodies scratch the surfaces. Once the former becomes coated with atoms from the latter, the wear process slows down and becomes "adhesive like." The initial particle sizes are consistent with the minimum size to be expected for the debris, but tend to grow by material removal from the surfaces and to agglomerate. We show that, for the particular configurations under consideration, the surface roughness seems to converge to a steady state characterized by Hurst exponent close to 0.8, independently of the initial conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11249-024-01833-9.

Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning

Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.

Gaussian and Non-Gaussian Solvent Density Fluctuations within Solute Cavities in a Water-like Solvent

We report a Monte Carlo simulation study of length-scale-dependent density fluctuations in cavities in the coarse-grained mW representation of water at ambient conditions. Specifically, we use a combination of test particle insertion and umbrella sampling techniques to examine the full range of water occupation states in spherical cavities up to 6.3 Å radius in water. As has previously been observed, water density fluctuations are found to be effectively Gaussian in nature for atomic-scale cavities, but as the cavities get larger, they exhibit a non-Gaussian "fat-tail" distribution for lower occupancy states. We introduce a new statistical thermodynamic approach to analyze non-Gaussian fluctuations based on the radial distribution of waters about cavities with varying numbers of waters within its boundaries. It is shown that the onset of these non-Gaussian fluctuations is a result of the formation of a bubble within the cavity as it is emptied, which is accompanied by the adsorption of waters onto its interior surface. We revisit a theoretical framework we previously introduced to describe Gaussian fluctuations within cavities to incorporate bubble formation by including surface tension contributions. This modified theory accurately describes density fluctuations within both atomic and meso-scale cavities. Moreover, the theory predicts the transition from Gaussian to non-Gaussian fluctuations at a specific cavity occupancy, in excellent agreement with simulation observations.

Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields

We proposed a parameter-free volume element representation that satisfies the electron counting model and obtains accurate machine learning potential energy and direct force fitting of randomly perturbed hexagonal BN. Our method preserves permutational, translational, and rotational invariance and can be extended to three-dimensional systems, verified by a system of bulk Si. As a result, we obtained 0.57 meV/atom potential energy root mean squared error (RMSE) and 59 meV/Å force RMSE for perturbed bulk BN systems and 0.43 meV/atom potential energy RMSE and 36 meV/Å force RMSE for perturbed Si systems. In addition, an unbiased perturbation-based data set construction scheme is introduced and a continuous population distribution is obtained with a training data set of 4500, which is about 1 order of magnitude smaller than standard methods based on first-principles molecular dynamics simulations and saves a large amount of computing resources. General validity of our model is verified by structure optimization, molecular dynamics simulations, and extrapolations.

Formalizing Coarse-Grained Representations of Anisotropic Interactions at Multimeric Protein Interfaces Using Virtual Sites

Molecular simulations of biomacromolecules that assemble into multimeric complexes remain a challenge due to computationally inaccessible length and time scales. Low-resolution and implicit-solvent coarse-grained modeling approaches using traditional nonbonded interactions (both pairwise and spherically isotropic) have been able to partially address this gap. However, these models may fail to capture the complex anisotropic interactions present at macromolecular interfaces unless higher-order interaction potentials are incorporated at the expense of the computational cost. In this work, we introduce an alternate and systematic approach to represent directional interactions at protein-protein interfaces by using virtual sites restricted to pairwise interactions. We show that virtual site interaction parameters can be optimized within a relative entropy minimization framework by using only information from known statistics between coarse-grained sites. We compare our virtual site models to traditional coarse-grained models using two case studies of multimeric protein assemblies and find that the virtual site models predict pairwise correlations with higher fidelity and, more importantly, assembly behavior that is morphologically consistent with experiments. Our study underscores the importance of anisotropic interaction representations and paves the way for more accurate yet computationally efficient coarse-grained simulations of macromolecular assembly in future research.

Predicting Phase Stability at Interfaces

We present the RAFFLE methodology for structural prediction of the interface between two materials and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To address the challenge of interface structure prediction, our methodology combines physical insights derived from morphological features observed in related systems with an iterative machine learning technique. This employs physical-based methods, including void-filling and n-body distribution functions to predict interface structures. For the carbon-MgO encapsulated system, we have shown the rocksalt and hexagonal phases of MgO to be the two most energetically stable in the few-layer regime. We demonstrate that monolayer rocksalt is heavily stabilized by interfacing with graphene, becoming more energetically favorable than the graphenelike monolayer hexagonal MgO. The RAFFLE methodology provides valuable insights into interface behavior, and a route to finding new materials at interfaces.

Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon

Some low-coordination materials, including water, silica, and silicon, exhibit polyamorphism, having multiple amorphous forms. However, the microscopic mechanism and kinetic pathway of amorphous-amorphous transition (AAT) remain largely unknown. Here, we use a state-of-the-art machine-learning potential and local structural analysis to investigate the microscopic kinetics of AAT in silicon after a rapid pressure change. We find that the transition from low-density-amorphous (LDA) to high-density-amorphous (HDA) occurs through nucleation and growth, resulting in non-spherical interfaces that underscore the mechanical nature of AAT. In contrast, the reverse transition occurs through spinodal decomposition. Further pressurisation transforms LDA into very-high-density amorphous (VHDA), with HDA serving as an intermediate state. Notably, the final amorphous states are inherently unstable, transitioning into crystals. Our findings demonstrate that AAT and crystallisation are driven by joint thermodynamic and mechanical instabilities, assisted by preordering, occurring without diffusion. This unique mechanical and diffusion-less nature distinguishes AAT from liquid-liquid transitions.

Multiscale Molecular Dynamics Simulations of Ice-Binding Proteins

Ice-binding proteins (IBPs) are a diverse class of proteins that are essential for the survival of organisms in cold conditions. IBPs are diverse in their function and can prevent or promote ice growth and selectively bind to specific crystallographic planes of the growing ice lattice. Moreover, IBPs are widely utilized to modulate ice crystal growth and recrystallization in the food industry and as cryoprotectants to preserve biological matter. A key unresolved aspect of the mode of action is how the ice-binding sites of these proteins distinguish between ice and water and interact with multiple crystal facets of the ice. The use of molecular dynamics (MD) simulation allows us to thoroughly investigate the binding mechanism and energetics of ice-binding proteins, to complement and expand on the mechanistic understandings gained from experiments. In this chapter, we describe a series of molecular dynamics simulation methodologies to investigate the mechanism of action of ice-binding proteins. Specifically, we provide detailed instructions to set up MD simulations to study the binding and interaction of ice-binding proteins using atomistic and coarse-grained simulations.

Light-induced shift current vortex crystals in moiré heterobilayers

Transition metal dichalcogenide (TMD) moiré superlattices provide an emerging platform to explore various light-induced phenomena. Recently, the discoveries of novel moiré excitons have attracted great interest. The nonlinear optical responses of these systems are however still underexplored. Here, we report investigation of light-induced shift currents (a second-order response generating DC current from optical illumination) in the WSe2/WS2 moiré superlattice. We identify a striking phenomenon of the formation of shift current vortex crystals-i.e., two-dimensional periodic arrays of moiré-scale current vortices and associated magnetic fields with remarkable intensity under laboratory laser setup. Furthermore, we demonstrate high optical tunability of these current vortices-their location, shape, chirality, and magnitude can be tuned by the frequency, polarization, and intensity of the incident light. Electron-hole interactions (excitonic effects) are found to play a crucial role in the generation and nature of the shift current intensity and distribution. Our findings provide a promising all-optical control route to manipulate nanoscale shift current density distributions and magnetic field patterns, as well as shed light on nonlinear optical responses in moiré quantum matter and their possible applications.

The effect of intrinsic strain on the thermal expansion behavior of Janus MoSSe nanotubes: a molecular dynamic simulation

In this study, we conducted molecular dynamic simulations to investigate the thermal expansion behavior of Janus MoSSe nanotubes. We focused on understanding how the intrinsic strain in these nanotubes affects their thermal expansion coefficient (TEC). Interestingly, we found that Janus MoSSe nanotubes with sulfur (S) on the outer surface (MoSeS) exhibit a different intrinsic strain compared to those with selenium (Se) on the outer surface (MoSSe). In light of this observation, we explored the influence of this intrinsic strain on the TEC of the nanotubes. Our results revealed distinct trends for the TEC along the radial direction (TEC-r) and the axial direction (TEC-lx) of the MoSSe and MoSeS nanotubes. The TEC-rof MoSeS nanotubes was found to be significantly greater than that of MoSSe nanotubes. Moreover, the TEC-lxof MoSeS nanotubes was smaller than that of MoSSe nanotubes. Further analysis showed that the TEC-rof MoSeS nanotubes decreased by up to 37% as the radius increased, while that of MoSSe nanotubes exhibited a slight increase with increasing radius. On the other hand, the TEC-lxof MoSeS nanotubes increased by as much as 45% with increasing radius, whereas that of MoSSe nanotubes decreased gradually. These opposite tendencies of the TECs with respect to the radius were attributed to the presence of intrinsic strain within the nanotubes. The intrinsic strain was found to play a crucial role in inducing thermally induced bending and elliptization of the nanotubes' cross-section. These effects are considered key mechanisms through which intrinsic strain influences the TEC. Overall, our study provides valuable insights into the thermal stability of Janus nanotubes. By understanding the relationship between intrinsic strain and the thermal expansion behavior of nanotubes, we contribute to the broader understanding of these materials and their potential applications.

Lightweight Extendable Stacking Framework for Structure Classification in Atomistic Simulations

Identifying an atom's local crystal structure is one crucial step in many atomistic simulation analyses. However, many traditional methods are available to only a few limited types of structures, and their performance often relies on manually determined parameters, which may lead to poor classification results in complex material systems. Machine learning models can enhance accuracy and generalizability, but they typically require large amounts of data and computation. This issue could be more severe for deep-learning-based frameworks, especially when confronted with unfamiliar crystal structures. To address this challenge, we propose a lightweight and extendable stacked structure (LESS) classifier, which adopts bond orientational order parameters as features and assembles several efficient machine learning methods as based models. The LESS classifier can recognize a variety of crystal structures, e.g., amorphous, mono, and binary structures, with over 98.8% accuracy on our validation data set, outperforming many current methods even including some deep-learning methods. Our model can also conduct probabilistic classification that aids in the interpretation of atomic structures in complicated environments such as heterogeneous interfaces. Furthermore, when exposed to a completely unknown crystal structure, the LESS framework can efficiently incorporate this new knowledge with generative sampled data from the current model. Overall, our model exhibits great potential as an accurate and flexible atomic structure identification tool featuring high efficiency in both learning and retraining.

Atomic-Level Insights into Hollow Silica-Based Materials for Drug Delivery: Effects of Wettability and Porosity

Experimental evidence has demonstrated that the drug carrier capacity can be significantly enhanced through the use of hollow silica particles. Nevertheless, the effects of varying functional drug carrier surfaces and porous structures remain ambiguous. This study employs molecular dynamics simulations to examine the effects of varying the surface wettability, pore size, and flow velocity on the transfer process. The different levels of wettability of the silica surface with the coarse-grained water model is illustrated by adjusted interaction parameters. The effect of wettability is investigated. With weak interactions, the flow molecules form a nanodroplet to transfer through the porous structure. A strong interaction will lead to molecules flowing as a liquid film to transfer through the structure. Interestingly, the "contradiction effect" is observed when the flow molecules fail to penetrate the porous structure with weak interactions, during which surface tension dominates their flow behavior. Moreover, different porous structures are considered. The flow behaviors are divided into three processes: (1) fast flowing, (2) transient point, and (3) penetration flowing. Furthermore, the concept of surface molecules is defined to quantitatively measure the effect of porosity. A recommended contact angle is proposed. The results will pave the way for more carrier structures in medical engineering.

Si/Ge interfacial thermal conductance enhancement through Sn nanoparticle embedding

The improvement of interfacial thermal conductance (ITC) is a crucial aspect of the thermal management of nanodevices. In this paper, the effect of embedding Sn nanoparticles at the Si/Ge interface on ITC was investigated using non-equilibrium molecular dynamics (NEMD) simulations. It was found that although Sn has a higher atomic weight than both silicon and germanium, the ITC can be enhanced by 1.95 times when the nanoparticles reach a suitable number and diameter. The phonon transmission functions and density of states clearly indicate that an increased ITC can be attributed to the enhanced inelastic phonon scattering facilitated by Sn nanoparticles. This enhancement opens up novel channels for interfacial phonon transport. However, when the number of nanoparticles surpasses a suitable value, elastic phonons begin to dominate heat transport, leading to a subsequent decrease in the ITC. Sensitivity analysis further underscores that the ITC exhibits greater responsiveness to changes in diameter. In addition, it is also shown that with increasing temperature, a higher frequency phonon excitation occurs, increasing phonon inelastic scattering and interface transmission. These findings offer a novel strategy for enhancing ITC and deepening our comprehension of both elastic and inelastic phonon processes in interfacial phonon transport.

Compression-induced hexa-to-tetra phase transition of confined germanene

Via molecular dynamics (MD) simulations we find the existence of the new allotrope of two-dimensional (2D) germanene, i.e. 2D tetra-germanene (tetra-Ge) which contains entirely tetragons. We compress 2D hexa-germanene (hexa-Ge) step by step over a broad density range at constant temperature and hexa-tetra Ge phase transition occurs. We find that the compression of hexa-Ge at 2000 K (not far above the melting point of hexa-Ge) leads to the formation of tetra-Ge with the highest quality. Atomic structure of the obtained tetra-Ge at 300 K is analyzed in details. Although fraction of tetragons in the tetra-Ge is very high (larger than 0.99), some defects are found in addition to the skew tetragons. Due to containing almost entirely tetragons, tetra-Ge may exhibit new behaviors unlike those of the hexa-Ge. Subsequent studies in this direction for 2D tetra-Ge. In addition, first-principles calculations under density functional theory confirm the existence of stable tetra-Ge.

Modeling of Short-Pulse Laser Interactions with Monolithic and Porous Silicon Targets with an Atomistic-Continuum Approach

The acquisition of reliable knowledge about the mechanism of short laser pulse interactions with semiconductor materials is an important step for high-tech technologies towards the development of new electronic devices, the functionalization of material surfaces with predesigned optical properties, and the manufacturing of nanorobots (such as nanoparticles) for bio-medical applications. The laser-induced nanostructuring of semiconductors, however, is a complex phenomenon with several interplaying processes occurring on a wide spatial and temporal scale. In this work, we apply the atomistic-continuum approach for modeling the interaction of an fs-laser pulse with a semiconductor target, using monolithic crystalline silicon (c-Si) and porous silicon (Si). This model addresses the kinetics of non-equilibrium laser-induced phase transitions with atomic resolution via molecular dynamics, whereas the effect of the laser-generated free carriers (electron-hole pairs) is accounted for via the dynamics of their density and temperature. The combined model was applied to study the microscopic mechanism of phase transitions during the laser-induced melting and ablation of monolithic crystalline (c-Si) and porous Si targets in a vacuum. The melting thresholds for the monolithic and porous targets were found to be 0.32 J/cm2 and 0.29 J/cm2, respectively. The limited heat conduction mechanism and the absence of internal stress accumulation were found to be involved in the processes responsible for the lowering of the melting threshold in the porous target. The results of this modeling were validated by comparing the melting thresholds obtained in the simulations to the experimental values. A difference in the mechanisms of ablation of the c-Si and porous Si targets was considered. Based on the simulation results, a prediction regarding the mechanism of the laser-assisted production of Si nanoparticles with the desired properties is drawn.

Thermodynamic anomalies, polyamorphism and all that

The appearance and evolution of thermodynamics anomalies, and related properties, are studied for two classes of system, modelling those dominated by covalent and ionic interactions, respectively. Such anomalies are most familiar in the density but are also present in other thermodynamics variables such as the compressibility and heat capacity. By systematically varying key model parameters the emergence and evolution of these anomalies can be tracked across the phase space. The interaction of the anomalies can often be rationalized by thermodynamics 'rules'. The emergence of these anomalies may also be correlated with the appearance of polyamorphism, the existence of multiple amorphous states which differ in density and entropy. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Heat flux concentrators based on nanoscale phononic metastructures

In recent years, nanoscale heat flux regulation has been at the forefront of research. Nanoscale heat flux concentration is of potential importance in various applications, but no research has been conducted on local heat flux concentration. In this paper, we designed two heat flux concentrators using patterned amorphous and nanomesh structures, respectively. Using molecular dynamics simulation, we find that the heat flux in the central regions is much higher than that in the adjacent regions, with the concentration ratio arriving at 9-fold. Thus a heat flux concentrator is realized using these nanophononic metastructures. The phonon localization theory was used to explain the underlying mechanism. This work provides a direct design strategy for thermal concentrators using practical nanofabrication technologies.

Effect of Nanopillars on the Wetting State and Adhesion Characteristics of Molten Aluminum Droplets

To solve the adhesion problem between molten aluminum and vacuum ladle liner during the electrolytic aluminum production process, the wetting state and adhesion properties of molten aluminum droplets on substrate surfaces with different nanopillars are investigated based on molecular dynamics. The results show that the adhesion strength of molten aluminum droplets in different wetting states has the pattern Young state > Wenzel state > Cassie state. Effects of increasing nanopillar height or interval are poles apart in the wetting state and adhesion characteristics of aluminum molten droplets. The critical height and critical interval of the nanopillar where the wetting state transition occurs are obtained. The increase of the nanopillar width can induce the wetting state transition from the Cassie state to the Wenzel state. In addition, the phantom wall method is applied to study the variation of the separation force. It is found that a peak in the separation force curve occurs when the molten droplet separates from the bottom of the nanopillar interval or the top of the nanopillar. The separation force curves of the droplets in the Young state and the Cassie state have single peaks, while the droplets in the Wenzel state have double peaks.

Effective elastic moduli of composites with a strongly disordered host material

The local elastic properties of strongly disordered material are investigated using the theory of correlated random matrices. A significant increase in stiffness is shown in the interfacial region, the thickness of which depends on the strength of disorder. It is shown that this effect plays a crucial role in nanocomposites, in which interfacial regions are formed around each nanoparticle. The studied interfacial effect can significantly increase the influence of nanoparticles on the macroscopic stiffness of nanocomposites. The obtained thickness of the interfacial region is determined by the heterogeneity lengthscale and is of the same order as the lengthscale of the boson peak.

Numerical Investigation on the Effects of Grain Size and Grinding Depth on Nano-Grinding of Cadmium Telluride Using Molecular Dynamics Simulation

Cadmium telluride (CdTe) is known as an important semiconductor material with favorable physical properties. However, as a soft-brittle material, the fabrication of high-quality surfaces on CdTe is quite challenging. To improve the fundamental understanding of the nanoscale deformation mechanisms of CdTe, in this paper, MD simulation was performed to explore the nano-grinding process of CdTe with consideration of the effects of grain size and grinding depth. The simulation results indicate that during nano-grinding, the dominant grinding mechanism could switch from elastic deformation to ploughing, and then cutting as the grinding depth increases. It was observed that the critical relative grain sharpness (RGS) for the transition from ploughing to cutting is greatly influenced by the grain size. Furthermore, as the grinding depth increases, the dominant subsurface damage mechanism could switch from surface friction into slip motion along the <110> directions. Meanwhile, as the grain size increases, less friction-induced damage is generated in the subsurface workpiece, and more dislocations are formed near the machined groove. Moreover, regardless of the grain size, it was observed that the generation of dislocation is more apparent as the dominant grinding mechanism becomes ploughing and cutting.

Modulating Friction by the Phase of the Vertical Vibrational Excitation at Washboard Frequency

Applying external vibrations at the resonant frequencies of the frictional system has been a highly effective approach to suppress friction but usually requires additional energy consumption. In this study, we find that in addition to exerting the vibration at the resonant frequency of the frictional system, the friction force on the atomically flat silicon surface can also present a local minimum when the oscillation frequency of the vertical vibrational excitation equals the washboard frequency with respect to the sliding velocity. Moreover, compared with the additional energy consumption at the resonant frequency, applying vertical vibrational excitation at the washboard frequency requires much less energy consumption. The study further shows that the friction force under the washboard frequency can be effectively mediated depending on how the initial phase angle of the vertical vibrational excitation affects the effective substrate potential barrier at the slip moment of the tip. We have also extended the proposed friction modulation technique on atomically flat surfaces to periodic textured surfaces and confirmed its practicality and great potential for controlling friction.

Molecular Dynamics Modeling of Pulsed Laser Fragmentation of Solid and Porous Si Nanoparticles in Liquid Media

The production of non-toxic and homogeneous colloidal solutions of nanoparticles (NPs) for biomedical applications is of extreme importance nowadays. Among the various methods for generation of NPs, pulsed laser ablation in liquids (PLAL) has proven itself as a powerful and efficient tool in biomedical fields, allowing chemically pure silicon nanoparticles to be obtained. For example, laser-synthesized silicon nanoparticles (Si NPs) are widely used as contrast agents for bio visualization, as effective sensitizers of radiofrequency hyperthermia for cancer theranostics, in photodynamic therapy, as carriers of therapeutic radionuclides in nuclear nanomedicine, etc. Due to a number of complex and interrelated processes involved in the laser ablation phenomenon, however, the final characteristics of the resulting particles are difficult to control, and the obtained colloidal solutions frequently have broad and multimodal size distribution. Therefore, the subsequent fragmentation of the obtained NPs in the colloidal solutions due to pulsed laser irradiation can be utilized. The resulting NPs' characteristics, however, depend on the parameters of laser irradiation as well as on the irradiated material and surrounding media properties. Thus, reliable knowledge of the mechanism of NP fragmentation is necessary for generation of a colloidal solution with NPs of predesigned properties. To investigate the mechanism of a laser-assisted NP fragmentation process, in this work, we perform a large-scale molecular dynamics (MD) modeling of FS laser interaction with colloidal solution of Si NPs. The obtained NPs are then characterized by their shape and morphological properties. The corresponding conclusion about the relative input of the properties of different laser-induced processes and materials to the mechanism of NP generation is drawn.

Analytical and Parameter-Free Hückel Theory Made Possible for Symmetric Hx Clusters

It is widely accepted that energetics of chemical bond breaking and formation can be described with simple mathematical forms only at the expense of extensive parameterization. In this work, the discovery of a simple tight-binding-type mathematical framework that can accurately predict the relative energetics of regular Hx polygons (2 ≤ x ≤ 15) in the ground states with their respective spin multiplicities using no parameters has been reported. The framework recasts Hückel theory in a density functional theory form by making use of Anderson and Adams-Gilbert theories of localized orbitals. For the systems examined, the method exhibits mean absolute errors of ∼0.02 Å (edge lengths) and ∼0.15 eV/atom (energy minima) relative to correlated-electron quantum chemistry calculations. Its accuracy is found to be comparable to the generalized gradient approximation and superior to standard parameterized tight binding and reactive potentials applied to Hx structures. Generalization of the theoretical framework to systems of many-electron atoms is presented, along with the comparison of the method to existing semiempirical tight binding and bond order potential approaches.

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy─comparable or better than existing state-of-the-art machine learning models─with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. The accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator

Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "bypass all physics laws" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.

Defect engineering for thermal transport properties of nanocrystalline molybdenum diselenide

Molybdenum diselenide (MoSe2) is attracting great attention as a transition metal dichalcogenide (TMDC) due to its unique applications in micro-electronics and beyond. In this study, the role of defects in the thermal transport properties of single-layer MoSe2 is investigated using non-equilibrium molecular dynamics (NEMD) simulations. Specifically, this work quantifies how different microstructural defects such as vacancies and grain boundaries (GBs) and their concentration (N) alter the thermal conductivity (TC) of single crystal and nanocrystalline MoSe2. These results show a significant drop in thermal conductivity as the concentration of defects increases. Specifically, point defects lower the TC of MoSe2 in the form of N-β where β is 0.5, 0.48 and 0.36 for VMo, VMo-Se and VSe vacancies, respectively. This study also examines the impact of grain boundaries on the thermal conductivity of nanocrystalline MoSe2. These results suggest that GB migration and stress-assisted twinning along with localized phase transformation (2H to 1T) are the primary factors affecting the thermal conductivity of nanocrystalline MoSe2. Based on MD simulations, TC of polycrystalline MoSe2 increases with the average grain size (d̄) in the form of d̄4.5. For example, the TC of nanocrystalline MoSe2 with d̄ = 11 nm is around 40% lower than the TC of the pristine monocrystalline sample with the same dimensions. Finally, the influence of sample size and temperature is studied to determine the sensitivity of quantitative thermal properties to the length scale and phonon scattering, respectively. The results of this work could provide valuable insights into the role of defects in engineering the thermal properties of next generation semiconductor-based devices.

Charge equilibration model with shielded long-range Coulomb for reactive molecular dynamics simulations

Atomic description of electrochemical systems requires reactive interaction potential to explicitly describe the chemistry between atoms and molecules and the evolving charge distribution and polarization effects. Calculating Coulomb electrostatic interactions and polarization effects requires a better estimate of the partial charge distribution in molecular systems. However, models such as reactive force fields and charge equilibration (QEq) include Coulomb interactions up to a short-distance cutoff for better computational speeds. Ignoring long-distance electrostatic interaction affects the ability to describe electrochemistry in large systems. We studied the long-range Coulomb effects among charged particles and extended the QEq method to include long-range effects. By this extension, we anticipate a proper account of Coulomb interactions in reactive molecular dynamics simulations. We validate the approach by computing charges on a series of metal-organic frameworks and some simple systems. Results are compared to regular QEq and quantum mechanics calculations. The study shows slightly overestimated charge values in the regular QEq approach. Moreover, our method was combined with Ewald summation to compute forces and evaluate the long-range effects of simple capacitor configurations. There were noticeable differences between the calculated charges with/without long-range Coulomb interactions. The difference, which may have originated from the long-range influence on the capacitor ions, makes the Ewald method a better descriptor of Coulomb electrostatics for charged electrodes. The approach explored in this study enabled the atomic description of electrochemical systems with realistic electrolyte thickness while accounting for the electrostatic effects of charged electrodes throughout the dielectric layer in devices like batteries and emerging solid-state memory.

An Efficient Reactive Force Field without Explicit Coordination Dependence for Studying Caustic Aluminum Chemistry

Reactive force fields (RFFs) are an expedient approach to sample chemical reaction paths in complex systems, relative to density functional theory. However, there is continued need to improve efficiencies, specifically in systems that have slow transverse degrees of freedom, as in highly viscous and superconcentrated solutions. Here, we present an RFF that is differentiated from current models (e.g., ReaxFF) by omitting explicit dependence on the atom coordination and employing a small parameter set based on Lennard-Jones, Gaussian, and Stillinger-Weber potentials. The model was parametrized from AIMD simulation data and is used to model aluminate reactivity in sodium hydroxide solutions with extensive validation against experimental radial distribution functions, computed free energy profiles for oligomerization, and formation energies. The model enables simulation of early stage Al(OH)₃ nucleation which has significant relevance to industrial processing of aluminum and has a computational cost that is reduced by 1 order of magnitude relative to ReaxFF.

Finding defects in glasses through machine learning

Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Due to their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.

Symmetries of many-body systems imply distance-dependent potentials

Considering an interatomic potential U(q), where q=[q_{1},q_{2},⋯,q_{N}]∈R^{3N} is a vector describing positions q_{i}∈R^{3}, it is shown that U can be defined as a function of the interatomic distance variables r_{ij}=|q_{i}-q_{j}| provided the potential U satisfies some symmetry assumptions. Moreover, the potential U can be defined as a function of a proper subset of the distance variables r_{ij}, provided N>5, with the number of distance variables used scaling linearly with the number of atoms N.

Nanoscale friction and wear behavior of a CVD-grown aged WS2 monolayer: the role of wrinkles and surface chemistry

Friction reduction by transition metal dichalcogenide (TMD) monolayers is well documented; however, wrinkle formation on the surface of TMDs takes place due to strain relaxation over time and leads to the deterioration of the tribological properties at a small scale. Herein, we report the role of wrinkles on the wear behavior of a chemical vapor deposition (CVD) grown aged WS2 monolayer and the comparison with wrinkle-free regions. Atomic force microscopy (AFM) was utilized to perform load-dependent experiments, and we noticed that the wear initiated near wrinkles resulted in the disintegration of the monolayer. In contrast, in the wrinkle-free regions, wear occurred at significantly higher loads, similar to that of freshly grown WS2, although the coefficient of friction (COF) was increased due to the changes in surface chemistry as a result of aging, which was confirmed using X-ray photoelectron spectroscopy (XPS). In the presence of wrinkles, a ten-fold reduction in the load-carrying capacity was observed compared to the wrinkle-free regions. Molecular dynamics (MD) simulations were used to corroborate experimental findings, which demonstrate the role of wrinkles in the initiation of wear due to the stress concentration under sliding nanocontacts near the wrinkles. In addition, simulations help establish a relationship between the adsorbed chemical species on the surface and increased COF.

Transferability of interatomic potentials for silicene

The ability of various interatomic potentials to reproduce the properties of silicene, that is, 2D single-layer silicon, polymorphs was examined. Structural and mechanical properties of flat, low-buckled, trigonal dumbbell, honeycomb dumbbell, and large honeycomb dumbbell silicene phases, were obtained using density functional theory and molecular statics calculations with Tersoff, MEAM, Stillinger-Weber, EDIP, ReaxFF, COMB, and machine-learning-based interatomic potentials. A quantitative systematic comparison and a discussion of the results obtained are reported.

Liquid-Phase Friction of Two-Dimensional Molybdenum Disulfide at the Atomic Scale

Tribological properties depend strongly on environmental conditions such as temperature, humidity, and operation liquid. However, the origin of the liquid effect on friction remains largely unexplored. Herein, taking molybdenum disulfide (MoS₂) as a model system, we explored the nanoscale friction of MoS₂ in polar (water) and nonpolar (dodecane) liquids through friction force microscopy. The friction force exhibits a similar layer-dependent behavior in liquids as in air; i.e., thinner samples have a larger friction force. Interestingly, friction is significantly influenced by the polarity of the liquid, and it is larger in polar water than in nonpolar dodecane. Atomically resolved friction images together with atomistic simulations reveal that the polarity of the liquid has a substantial effect on friction behavior, where liquid molecule arrangement and hydrogen-bond formation lead to a higher resistance in polar water in comparison to that in nonpolar dodecane. This work provides insights into the friction on two-dimensional layered materials in liquids and holds great promise for future low-friction technologies.

Disordered interfaces of alkaline aluminate salt hydrates provide glimpses of Al3+ coordination changes

HYPOTHESIS

The precipitation and dissolution of aluminum-bearing mineral phases in aqueous systems often proceed via changes in both aluminum coordination number and connectivity, complicating molecular-scale interpretation of the transformation mechanism. Here, the thermally induced transformation of crystalline sodium aluminum salt hydrate, a phase comprised of monomeric octahedrally coordinated aluminate which is of relevance to industrial aluminum processing, has been studied. Because intermediate aluminum coordination states during melting have not previously been detected, it is hypothesized that the transition to lower coordinated aluminum ions occurs within ahighly disordered quasi-two-dimensional phase at the solid-solution interface.

EXPERIMENTS AND SIMULATIONS

In situ X-ray diffraction (XRD), Raman and²⁷Al nuclear magnetic resonance (NMR) spectroscopy were used to monitor the melting transition of nonasodium aluminate hydrate (NSA, Na₉[Al(OH)₆]₂·3(OH)·6H₂O). A mechanistic interpretation was developed based on complementary classical molecular dynamics (CMD) simulations including enhanced sampling. A reactive forcefield was developed to bridge speciation in the solution and in the solid phase.

FINDINGS

In contrast to classical dissolution, aluminum coordination change proceeds through a dynamically stabilized ensemble of intermediate states in a disordered layer at the solid-solution interface. In both melting and dissolution of NSA, octahedral, monomeric aluminum transition through an intermediate of pentahedral coordination. The intermediate dehydroxylates to form tetrahedral aluminate (Al(OH)₄^-) in the liquid phase. This coordination change is concomitant with a breaking of the ionic aluminate-sodium ionlinkages. The solution phase Al(OH)₄^- ions subsequently polymerize into polynuclear aluminate ions. However, there are some differences between bulk melting and interfacial dissolution, with the onset of the surface-controlled process occurring at a lower temperature (∼30 °C) and the coordination change taking place more gradually as a function of temperature. This work to determine the local structure and dynamics of aluminum in the disordered layer provides a new basis to understand mechanisms controlling aluminum phase transformations in highly alkaline solutions.

In/Ga-Doped Si as Anodes for Si-Air Batteries with Restrained Self-Corrosion and Surface Passivation: A First-Principles Study

Silicon-air batteries (SABs) are attracting considerable attention owing to their high theoretical energy density and superior security. In this study, In and Ga were doped into Si electrodes to optimize the capability of Si-air batteries. Varieties of Si-In/SiO₂ and Si-Ga/SiO₂ atomic interfaces were built, and their properties were analyzed using density functional theory (DFT). The adsorption energies of the SiO₂ passivation layer on In- and Ga-doped silicon electrodes were higher than those on pure Si electrodes. Mulliken population analysis revealed a change in the average number of charge transfers of oxygen atoms at the interface. Furthermore, the local device density of states (LDDOS) of the modified electrodes showed high strength in the interfacial region. Additionally, In and Ga as dopants introduced new energy levels in the Si/SiO₂ interface according to the projected local density of states (PLDOS), thus reducing the band gap of the SiO₂. Moreover, the I-V curves revealed that doping In and Ga into Si electrodes enhanced the current flow of interface devices. These findings provide a mechanistic explanation for improving the practical efficiency of silicon-air batteries through anode doping and provide insight into the design of Si-based anodes in air batteries.