Monte Carlo Dynamics Research Articles

Variable‐Range Hopping Conduction in Amorphous, Non‐Stoichiometric Gallium Oxide

AbstractAmorphous, non‐stoichiometric gallium oxide (a‐GaOx, x < 1.5) is a promising material for many electronic devices, such as resistive switching memories, neuromorphic circuits and photodetectors. So far, all respective measurements are interpreted with the explicit or implicit assumption of n‐type band transport above the conduction band mobility edge. In this study, the experimental and theoretical results consistently show for the first time that for an O/Ga ratio x of 0.8 to 1.0 the dominating electron transport mechanism is, however, variable‐range hopping (VRH) between localized states, even at room temperature and above. The measured conductivity exhibits the characteristic exponential temperature dependence on T−1/4, in remarkable agreement with Mott's iconic law for VRH. Localized states near the Fermi level are confirmed by photoelectron spectroscopy and density of states (DOS) calculations. The experimental conductivity data is reproduced quantitatively by kinetic Monte Carlo (KMC) simulations of the VRH mechanism, based on the ab‐initio DOS. High electric field strengths F cause elevated electron temperatures and an exponential increase of the conductivity with F1/2. Novel results concerning surface oxidation, magnetoresistance, Hall effect, thermopower and electron diffusion are also reported. The findings lead to a new understanding of a‐GaOx devices, also with regard to metal|a‐GaOx Schottky barriers.

Real-space investigation of polarons in hematite Fe2O3.

In polarizable materials, electronic charge carriers interact with the surrounding ions, leading to quasiparticle behavior. The resulting polarons play a central role in many materials properties including electrical transport, interaction with light, surface reactivity, and magnetoresistance, and polarons are typically investigated indirectly through these macroscopic characteristics. Here, noncontact atomic force microscopy (nc-AFM) is used to directly image polarons in Fe2O3 at the single quasiparticle limit. A combination of Kelvin probe force microscopy (KPFM) and kinetic Monte Carlo (KMC) simulations shows that the mobility of electron polarons can be markedly increased by Ti doping. Density functional theory (DFT) calculations indicate that a transition from polaronic to metastable free-carrier states can play a key role in migration of electron polarons. In contrast, hole polarons are significantly less mobile, and their hopping is hampered further by trapping centers.

Sodium titanate (Na2Ti4O9) nanoribbons for effective removal of organic dyes from water: Experimental and computational studies

In this work, Na2Ti4O9 nanoribbons (NTRs) were synthesized using the hydrothermal method for its potential application in water treatment. The NTRs were characterized by X-ray diffraction, field emission scanning electron microscopy, energy dispersive X-ray spectroscopy, transmission electron microscopy, zeta potential, Fourier Transform infrared spectroscopy, and surface area analyzer. The adsorption of organic dyes, including cationic methyl green (MeG) and methylene blue (MB) model pollutants, onto the NTRs was explored for the first time. Various parameters such as pH, adsorbent dose, initial dye concentration, regeneration, contact time, the effect of temperature, and thermodynamics were studied to determine the efficiency of NTRs for removing both dyes from water. A variety of isotherm and kinetic models were applied to fit the dye adsorption data at pH 7.0, providing insights into the adsorption mechanism and process kinetics. Kinetic data for both dyes fit well with pseudo-second-order and mixed 1,2-order models. The isotherm data agreed well with Langmuir-Freundlich and Sips models. At the studied temperatures of 298.2, 318.2, and 328.2 K, the maximum adsorption capacities for MeG (353.2, 367.7, and 443.2 mg·g−1) are significantly higher than for MB (29.3, 52.6, and 67.2 mg·g−1), indicating a stronger affinity for MeG, with adsorption efficiency improving as temperature increases. The MeG samples at 328.2 K exhibited interesting behavior. After adsorption, the samples became colorless, with a final pH near 7.0, indicating effective dye removal. However, the color faintly reappeared at pH 4.0, suggesting pH-dependent behavior and incomplete dye removal. The adsorption mechanism on the NTRs surface was investigated using Monte Carlo and molecular dynamics simulations.

Dynamic CO2 separation performance of nano-sized CHA zeolites under multi-component gas mixtures

To identify and evaluate promising adsorbents for CO2 separation, we synthesized CHA zeolites with different cations (Na+, K+, Cs+) and crystal sizes (45 nm – CHA45 and 500 nm – CHA500), and evaluated their explored CO2 adsorption performance from CO2/N2/He and CO2/CH4/He mixtures. Grand Canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations predicted CO2, N2, and CH4 adsorption isotherms and CO2 mobilities, respectively, which were compared to experimental data. Breakthrough curve analysis was used to assess the CO2 dynamic adsorption performance. The breakthrough curve analysis shows the smaller crystal sizes (45 nm) enhance the CO2 separation due to shorter interacrystalline diffusion pathways. Notably, Cs-CHA45 removed 2.2 times more CO2 from CO2/N2/He than Cs-CHA500. K-CHA45 showed the highest CO2/N2 selectivity (108) and achieved 841mmol g−1 for CO2 capture from N2 and 721mmol g−1 for CO2 from CH4. These findings underscore the potential of CHA nanocrystals for effective CO2 separation in various applications.

Experimental and computational analysis of poly(4-vinyl pyridine) and polyvinylpyrrolidone as effective corrosion inhibitors for low carbon steel in sulfuric acid

This study investigates the inhibitory effects of poly(4-vinyl pyridine) (P4VP) and polyvinylpyrrolidone (PVP) on the corrosion of low carbon steel (LCSt) in 1 M H2SO4. Protection efficacy (%PE) was measured using mass loss, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS). Results showed %PE values increased with higher polymer concentration and lower temperature. At 450 mg/L, %PE reached 93.13% and 94.19% for P4VP and PVP, respectively. These polymers function as mixed inhibitors, adsorbing onto the LCSt surface per Langmuir’s model as a mode of physical adsorption. EIS indicated that higher polymer concentration increased charge transfer resistance and decreased effective double-layer capacitance. Both P4VP and PVP also reduced pitting corrosion in chloride-containing solutions. Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT) optimized the molecular structures of the inhibitors, evaluating electronic parameters such as frontier molecular orbital (FMO) energies, energy gap, softness, hardness, electron transfer fraction, dipole moment, and hyperpolarizability ( β ° ). Monte Carlo (MC) and molecular dynamics (MD) simulations provided insights into adsorption mechanisms, corroborating experimental findings and highlighting the effect of protonation. This comprehensive study enhances our understanding of the relationship between molecular structure and function in polymer inhibitors, improving their application in corrosion protection.

Adsorption and evolution of N2 molecules over ZnO monolayer: a combined DFT and kinetic Monte-Carlo insight

A large surface area, wide band gap, and unique bonding property between Zn and O atoms make the hexagonal ZnO monolayer attractive as a gas sensor. In the present work, the adsorption and evolution of nitrogen (N2) molecules over a ZnO monolayer have been studied using two different theoretical methods: van der Waals density functional theory (vdW-DFT) and kinetic Monte-Carlo (kMC) simulation. The adsorption and diffusion (hopping over the surface) energy of a N2 gas molecule has been calculated considering the different sites over the ZnO substrate using the revPBE-vdW functional. Bader charge, electron localization function analysis, density of states and band structure plotting have been used to understand the adsorption mechanism. Lateral repulsive interaction between two N2 molecules limits the maximum packing number of gas molecules within one hexagonal ring. The output of the vdW-DFT calculation has been fed to the kMC code to predict the rate of adsorption, desorption, and diffusion, along with the overall surface coverage at different temperatures and pressures. Finally, the change in the N2 adsorption energy has been predicted with the increase of the ZnO layer number.

Molecular simulations of increased thermal energy storage in metal–organic heat carriers with R1234ze(E)/R32 mixtures combined with IRMOF-1

Metal-organic heat carrier (MOHC) nanofluids offer a promising solution to enhance the efficiency of Organic Rankine Cycle systems. In this study, we developed a computational model to assess the thermal energy storage capacity of MOHC nanofluids using a base fluid mixture of R1234ze(E) and R32 refrigerants. Through Grand Canonical Monte Carlo and molecular dynamics simulations, we examined the adsorption characteristics, desorption heat, and thermodynamic properties of MOHCs. Our findings reveal that R32 molecules are preferentially adsorbed in IRMOF-1 compared to R1234ze(E), with adsorption selectivity of R32 over R1234ze(E) increasing under higher adsorption pressures. When the temperature during the endothermic process surpasses the phase transition point of the base fluid, the latent heat of phase transition can be fully exploited to enhance energy storage. Moreover, the inclusion of IRMOF-1 nanoparticles significantly improves the energy storage properties of both R32 and R1234ze(E), as well as their mixtures. The energy storage enhancement provided by IRMOF-1 is particularly pronounced under high working pressures and low temperature differences. These insights offer valuable guidance for selecting appropriate base fluids in MOHC systems, thereby optimizing the utilization of low-grade energy sources.

Molecular simulation study of adsorption-diffusion of CH4, CO2 and H2O in gas-fat coal

In order to clarify the microscopic dynamics mechanism of CH4, CO2 and H2O adsorption and diffusion in coal, and to reveal the mechanism of the influence of different temperatures and pressures on the adsorption and diffusion characteristics of coal adsorbed CH4, CO2 and H2O molecules. In this paper, the macromolecular structure model of Jixi gas-fat coal was constructed, based on the Giant Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) methods. The adsorption-diffusion characteristics of CH4,CO2 and H2O single-component gases in the gas-fat coal macromolecule model at temperatures ranging from 273.15 K to 313.15 K and pressures ranging from 0.01 MPa to 15 MPa were investigated by using Material Studio software. The research results indicated that: The adsorption of three gases, CH4, CO2 and H2O, increased with the increase of equilibrium pressure, and the adsorption isotherms conformed to Langmuir type I isotherms. The amount of saturated adsorption of CH4 ranged from 11.18 to 14.37 ml/g, the saturated adsorption of CO2 ranged from 20.40 to 24.70 ml/g, and the saturated adsorption of H2O ranged from 66.61 to 84.21 ml/g. With the increase of temperature, the saturated adsorption of CH4 and CO2 both decreased, and the saturated adsorption of H2O firstly increased and then decreased, and the adsorption of H2O by low temperature and high temperature had both an inhibitory effect on the adsorption of H2O. The potential energy distributions of CH4, CO2 and H2O molecules are poisson distributed. The absolute values of the most available interaction energies are, from highest to lowest: H2O > CO2 > CH4; the activation energies for diffusion of CH4, CO2 and H2O are 12.20 kJ/mol, 3.36 kJ/mol, and 8.47 kJ/mol, respectively, and the diffusion of CO2 is the more likely to occur. The adsorption of CH4 and CO2 in coal is physical adsorption, while the adsorption process of H2O molecules is beyond the scope of physical adsorption. The absolute value of the interaction energy is H2O > CO2 > CH4 in descending order.

Topological comparison of flexible and semiflexible chains in polymer melts with θ-chains.

A central paradigm of polymer physics states that chains in melts behave like random walks as intra- and interchain interactions effectively cancel each other out. Likewise, θ-chains, i.e., chains at the transition from a swollen coil to a globular phase, are also thought to behave like ideal chains, as attractive forces are counterbalanced by repulsive entropic contributions. While the simple mapping to an equivalent Kuhn chain works rather well in most scenarios with corrections to scaling, random walks do not accurately capture the topology and knots, particularly for flexible chains. In this paper, we demonstrate with Monte Carlo and molecular dynamics simulations that chains in polymer melts and θ-chains not only agree on a structural level for a range of stiffnesses but also topologically. They exhibit similar knotting probabilities and knot sizes, both of which are not captured by ideal chain representations. This discrepancy comes from the suppression of small knots in real chains, which is strongest for very flexible chains because excluded volume effects are still active locally and become weaker with increasing semiflexibility. Our findings suggest that corrections to ideal behavior are indeed similar for the two scenarios of real chains and that the structure and topology of a chain in a melt can be approximately reproduced by a corresponding θ-chain.

Dissipative Self-Assembly of Patchy Particles under Nonequilibrium Drive: A Computational Study.

Inspired by biology and implemented using nanotechnology, the self-assembly of patchy particles has emerged as a pivotal mechanism for constructing complex structures that mimic natural systems with diverse functionalities. Here, we explore the dissipative self-assembly of patchy particles under nonequilibrium conditions, with the aim of overcoming the constraints imposed by equilibrium assembly. Utilizing extensive Monte Carlo (MC) and Molecular Dynamics (MD) simulations, we provide insight into the effects of external forces that mirror natural and chemical processes on the assembly rates and the stability of the resulting assemblies comprising 8, 10, and 13 patchy particles. Implemented by a favorable bond-promoting drive in MC or a pulsed square wave potential in MD, our simulations reveal the role these external drives play in accelerating assembly kinetics and enhancing structural stability, evidenced by a decrease in the time to first assembly and an increase in the duration the system remains in an assembled state. Through the analysis of an order parameter, entropy production, bond dynamics, and interparticle forces, we unravel the underlying mechanisms driving these advancements. We also validated our key findings by simulating a larger system of 100 patchy particles. Our comprehensive results not only shed light on the impact of external stimuli on self-assembly processes but also open a promising pathway for expanding the application by leveraging patchy particles for novel nanostructures.

Kinetic Monte Carlo modelling of nano-oxide precipitation and its associated stability under neutron irradiation for the Fe-Ti-Y-O system

While developing nuclear materials, predicting their behavior under long-term irradiation regimes spanning decades poses a significant challenge. We developed a novel Kinetic Monte Carlo (KMC) model to explore the precipitation behavior of Y-Ti-O oxides along grain boundaries within nanostructured ferritic alloys (NFA). This model also assessed the response of the oxides to neutron irradiation, even up simulated radiation damage levels in the desired long dpa range for reactor components. Our simulations investigated how temperature and grain boundary sinks influenced the oxide characteristics of a 12YWT-like alloy during heat treatments at 1023, 1123, and 1223 K. The oxide characteristics observed in our simulations were in good agreement with existing literature. Furthermore, the impact of grain boundaries on precipitation was found to be minimal. The resulting oxide configurations and positions were used in subsequent simulations that exposed them to simulated neutron irradiation to a total accumulated dose of 8 dpa at three temperatures: 673, 773, and 873 K, and at dose rates of 10−3, 10−4, and 10−5 dpa/s. This demonstrated the expected inverse relationship between oxide size and dose rate. In a long-term irradiation simulation at 873 K and 10−3 dpa/s was taken out to 66 dpa and found the oxides in the vicinity of the grain boundary were more susceptible to dissolution. Additionally, we conducted irradiation simulations of a 14YWT-like alloy to reproduce findings from neutron irradiation experiments. The larger oxides in the 14YWT-like alloy did not dissolve and displayed stability similar to the experimental results.

Minimum Energy Atomic Deposition: A novel, efficient atomistic simulation method for thin film growth

Thin-film growth is an area of research concerned with complex phenomena happening at atomic scales. Therefore, molecular simulation has been an important tool to confront experimental results to theoretical assumptions. However, the traditional thin film growth simulation methods, i.e., Molecular Dynamics (MD) and kinetic Monte-Carlo (kMC) and combinations thereof, suffer from limitations inherent to their design, i.e., limitations in system size and simulation time for MD and predetermined reaction rates and reaction sites for kMC. Consequently, it is practically impossible to simulate the evolution of polycrystalline growth resulting in ∼100nm thick films with realistic stress fields and defect structures, such as grain boundaries, stacking faults, etc. In this work, we propose a versatile and efficient atomistic simulation method (Minimum Energy Atomic Deposition) which works by direct insertion of atoms at points of minimal potential energy through efficient scanning of candidate positions and rapid relaxation of the system. This method allows simulating ≥100nm film thickness while mimicking experimental growth rates and high crystallinity and low-defect concentration and enables in-depth studies of atomic growth mechanisms, the evolution of crystal defects, and residual stress build-up. We demonstrate the efficiency and versatility of the method through the deposition of Al on Si, Al on Al, and Si on Si. The simulation results are systematically compared with experimental observations of thin-film deposition, yielding consistent observations. The method has been implemented in open-source LAMMPS software, making it easily accessible to the research community.

Dynamic Adaptation of Active Site Driven by Dual-side Adsorption in Single-Atomic Catalysts During CO2 Electroreduction.

Single-atom iron embedded in N-doped carbon (Fe-N-C) is among the most representative single-atomic catalysts (SACs) for electrochemical CO2 reduction reaction (CO2RR). Despite the simplicity of the active site, the CO2-to-CO mechanism on Fe-N-C remains controversial. Firstly, there is a long debate regarding the rate-determining step (RDS) of the reactions. Secondly, recent computational and experimental studies are puzzled by the fact that the CO-poisoned Fe centers still remain highly active at high potentials. Thirdly, there are ongoing challenges in elucidating the high selectivity of hydrogen evolution reaction (HER) over CO2RR at high potentials. In this work, we introduce a novel CO2RR mechanism on Fe-N-C, which was inspired by the dynamic of active sites in biological systems. By employing grand-canonical density functional theory and kinetic Monte-Carlo, we found that the RDS is not fixed but changes with the applied potential. We demonstrated that our proposed dual-side mechanisms could clarify the reason behind the high catalytic activity of CO-poisoned metal centers, as well as the high selectivity of HER over CO2RR at high potential. This study provides a fundamental explanation for long-standing puzzles of an important catalyst and calls for the importance of considering the dynamic of active sites in reaction mechanisms.

Anticorrosion and rheological properties of Calyptocarpus vialis extract as a green coating for mild steel in 2MH2SO4

This study assesses the rheological properties and corrosion inhibition efficacy of Calyptocarpus vialis (CV) extract, a wild plant in Asteraceae family. Shear and dynamic rheology studies show that CV extract has shear-thinning behaviour, with viscosity changes occurring at higher shear rates, most likely owing to aggregate formation. These aggregates may develop due to certain solvent characteristics and a jamming tendency is due to the presence of extract phytochemicals. Dynamic rheology confirms its viscoelastic nature. Corrosion inhibition effectiveness was investigated using Mild Steel (MS) in 2 M H2SO4, resulting in an 82.69 % inhibition with an extract concentration of 1 g/L. Adsorption isotherm indicates phytochemical adsorption on MS surface. Contact angles of polished MS (87.30°), with extract (80.10°), and without extract (51.25°) show decrease in wetting and increase in hydrophobicity which indicates that a thin protective layer is formed at MS surface. FE-SEM (Field Emission Scanning Electron Microscopy) images reveal that the CV extract forms a protective barrier on the MS surface, significantly reducing corrosion as compared to the unprotected surface exposed to the mild acid solution. Theoretical calculations, including Density Functional Theory (DFT) simulations, Monte Carlo (MC), and Molecular Dynamics (MD) simulations, support the experimental findings, elucidating phytochemicals adsorption on the MS surface. Further research is required to examine the scalability of CV extracts for industrial applications, as well as their long-term stability and efficacy in various corrosive conditions.

Molecular insights into dual competitive modes of CH4/CO2 in shale nanocomposites: Implications for CO2 sequestration and enhanced gas recovery in deep shale reservoirs

CO2 enhanced shale gas recovery has significant potential for improving the recovery rate of adsorbed gas and facilitating the geological sequestration of CO2. Nevertheless, the feasibility and microscopic mechanisms of CO2 enhanced gas recovery in deep shale gas reservoirs remain to be elucidated. In this study, a quartz-kerogen slit model was constructed to represent the shale composite nanopores of deep shale reservoirs. Utilizing the grand canonical Monte Carlo and molecular dynamics methods, the adsorption and diffusion behavior of CH4, CO2 and their mixtures was investigated under high-temperature and high-pressure conditions representative of deep shale reservoirs. The influences of temperature, pressure and aperture size were analyzed. The microscopic mechanisms governing the CH4/CO2 competitive adsorption in deep shale nanopores were uncovered by considering the competitive interactions between different rock constituents and quantifying the various gas storage states. The results show that the adsorption capacity of CO2 is greater than that of CH4 under the deep shale reservoir conditions. The replacement efficiency of CH4 by CO2 is higher in small shale pores. High temperature has a more significant negative impact on the adsorption of CO2 compared to that of CH4. Compared to mid-deep shale reservoirs, the nanopores of deep shale reservoirs have a higher proportion of free CH4/CO2 states. Quartz exhibits greater affinity for CH4/CO2 than kerogen, but kerogen possesses a larger specific surface area, resulting in a higher adsorption capacity per unit volume. The hierarchy of CH4/CO2 diffusion capacity in various storage states is as follows: dissolved gas in the kerogen matrix < adsorbed gas on the quartz surface < adsorbed gas on the kerogen surface < free gas in the pore center. This study improves the understanding of CO2 enhanced gas recovery in deep shale gas reservoirs.

Microscopic adsorption study from coal rank comparison on the feasibility of CO2-rich industrial waste gas injection enhanced coal bed methane recovery

A new method of injecting CO2-rich industrial waste gas (CO2-rich IWG) to displace coal bed methane (CBM) is proposed for energy conservation and green growth. In this study, theadsorption mechanisms of this method were studied by the Giant canonical Monte Carlo and molecular dynamics methods in macromolecular coal models with three ranks (brown, bituminous, and anthracite coal). It is found that the proportion of gas molecules in the adsorbed state is higher in the anthracite coal slit than in the brown and bituminous slits, indicating that anthracite coal seams are more appropriate for waste gas sequestration. The adsorption selectivity of NO and CO2 is consistent among the three coal ranks, with the order of brown > bituminous > anthracite > 1.0, and that of N2 orders as 1.0 > bituminous > anthracite > brown.This is due to the different dominant factors affecting the competitive adsorption between different waste gas components and CBM, namely competition for adsorption sites in the NO and CO2 systems and thefilling effect in the N2 system. There is a positive correlation between the number of major competitive adsorption sites and the adsorption selectivity of theCO2(NO) system. CO2 and CH4 compete for COC structures in brown and bituminous coal and OH structures in anthracite coal, and OH structures in the three coals are the major adsorption sites for NO systems. Our work advances the understanding of the microscopic adsorption mechanisms of CO2-rich IWG in the CBM reservoirs, which provides a theoretical basis for CO2-rich injection-enhanced CBM recovery technology.

Single particle tracking reveals through-membrane diffusion of bacteriophage during process disruption of virus filtration

In the biopharmaceutical industry, virus filters are crucial for ensuring the removal of endogenous and adventitious viruses as part of the viral clearance strategy. Although traditionally described as a size-exclusion mechanism, virus retention has a process-dependent nature where challenging conditions, such as process disruptions, may compromise membrane retention and significantly increase virus filtrate concentrations. The detailed mechanisms underlying this loss of retention are challenging to determine using traditional breakthrough experiments. In this work, single particle tracking and kinetic simulations were employed to connect individual particle behavior to the observed macroscopic losses in virus retention. Our experiments, using fluorescently labeled ΦX174 bacteriophage as a model parvovirus, replicated conditions representative of process disruptions within the Pegasus SV4, a homogeneous polymeric virus filtration membrane. During flow, phage particles retained were trapped within relatively large cavity spaces that had downstream constrictions aligned with the flow direction; the trapped particles were dynamic and exhibited significant intra-cavity motion. Upon flow stoppage, particles escaped from these retention locations rapidly, with approximately 90 % of previously trapped particles being remobilized for process disruption times ranging from 2 to 10 min, suggesting that local cavity escape had reached saturation at these timescales. Diffusion experiments within the membrane revealed isotropic and Fickian motion, hindered by more than an order of magnitude compared to diffusion in unconfined liquid. Despite the reduced mobility within the membrane, the substantial diffusion coefficient of 4.19 ± 0.06 μm2/s indicated that virus particles could travel tortuous but non-retentive pathways through the membrane on length scales equal to or greater than the membrane thickness during a disruption event. A 1D kinetic Monte-Carlo simulation successfully connected single-particle behavior to macroscopically observed virus release, indicating that significant diffusive release into the filtrate can occur even without the resumption of flow. This work provides crucial insights into the retention behavior of homogeneous membranes during periods of disruption, enabling the design of more robust mitigation strategies and filter designs.

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (SS316L) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M23C6, γ’, and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate for periods of up to 3000 h. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25 % volume fraction and mechanical properties within 6 % relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ∼1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that microstructural evolution under variable conditions can be predicted using a statistics-based model informed by a practicably obtainable dataset. The potential applications of this type of modeling framework are discussed.

Rationalize the Functional Roles of Protein-Protein Interactions in Targeted Protein Degradation by Kinetic Monte-Carlo Simulations.

Targeted protein degradation is a promising therapeutic strategy to tackle disease-causing proteins that lack binding pockets for traditional small-molecule inhibitors. Its first step is to trigger the proximity between a ubiquitin ligase complex and a target protein through a heterobifunctional molecule, such as proteolysis targeting chimeras (PROTACs), leading to the formation of a ternary complex. The properties of protein-protein interactions play an important regulatory role during this process, which can be reflected by binding cooperativity. Unfortunately, although computer-aided drug design has become a cornerstone of modern drug development, the endeavor to model targeted protein degradation is still in its infancy. The development of computational tools to understand the impacts of protein-protein interactions on targeted protein degradation, therefore, is highly demanded. To reach this goal, we constructed a non-redundant structural benchmark of the most updated ternary complexes and applied a kinetic Monte-Carlo method to simulate the association between ligases and PROTAC-targeted proteins in the benchmark. Our results show that proteins in most complexes with positive cooperativity tend to associate into native-like configurations more often. In contrast, proteins very likely failed to associate into native-like configurations in complexes with negative cooperativity. Moreover, we compared the protein-protein association through different interfaces generated from molecular docking. The native-like binding interface shows a higher association probability than all the other alternative interfaces only in the complex with positive cooperativity. These observations support the idea that the formation of ternary complexes is closely regulated by the binary interactions between proteins. Finally, we applied our method to cyclin-dependent kinases 4 and 6 (CDK4/6). We found that their interactions with the ligase are not as similar as their structures. Altogether, our study paves the way for understanding the role of protein-protein interactions in PROTACE-induced ternary complex formation. It can potentially help in searching for degraders that selectively target specific proteins.

Stability and dynamics of magnetic skyrmions in FM/AFM heterostructures

Magnetic skyrmions have garnered attention for their potential roles in spintronic applications, such as information carriers in computation, data storage, and nano-oscillators due to their small size, topological stability, and the requirement of small electric currents to manipulate them. Two key challenges in harnessing skyrmions are the stabilization requirement through a strong out-of-plane field, and the skyrmion Hall effect (SkHE). Here, we present a systematic model study of skyrmions in ferromagnetic/antiferromagnetic (FM/AFM) multilayer structures by employing both atomistic Monte Carlo and atomistic spin dynamics simulations. We demonstrate that skyrmions stabilized by exchange bias have superior stability to field-stabilized skyrmions due to the formation of a magnetic imprint within the AFM layer. Additionally, stacking two skyrmion hosting FM layers between two AFM layers suppresses the SkHE and enables the transport of AFM-coupled skyrmions with high velocity in the order of a few km/s. This proposed multilayer configuration could serve as a pathway to overcome existing limitations in the development of skyrmion-based devices, and the insights obtained through this study contribute significantly to the broader understanding of topological spin textures in magnetic materials. Published by the American Physical Society 2024