Limited Time Scale Research Articles

Thinking (too?) fast and slow: An example of professional judgement and decision making processes in athletics

Recently there has been interest in using examining coaching practice through a lens of Professional Judgement and Decision Making. One core theory of decision making, Recognition Primed Decision Making, examines how people make decisions in limited time scales. Alternatively, when time allows, there is an opportunity for people to engage in slower thoughtful and Type 2 judgement and decision making (DM). In both cases it is hypothesised that professional practitioners (coaches) should draw on formalistic scientific rather than substantive heuristics or rules to maintain a professional standard. However, despite these ideas relatively little is known about the actual decision-making behaviour of coaches in practice. Against this premise 12 long jump coaches were asked to identify the strengths and weaknesses of a long jump athlete and offer a view on how they would work to improve his performance. All coaches were asked to identify what they would do if their first approach didn't work. Findings suggest that coaches have an initial tendency to engage in RPD type behaviour but drawing mainly on substantive rules. Notably, uncertainty pushed coaches to become more considered, and formalistic. In conclusion, coaches have the capacity to be ‘professional’ in their DM behaviour but may not use this capacity as the first resort.

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD

The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

The role of the water contact layer on hydration and transport at solid/liquid interfaces

Understanding the structure in the nanoscopic region of water that is in direct contact with solid surfaces, so-called contact layer, is key to quantifying macroscopic properties that are of interest to e.g. catalysis, ice nucleation, nanofluidics, gas adsorption, and sensing. We explore the structure of the water contact layer on various technologically relevant solid surfaces, namely graphene, MoS[Formula: see text], Au(111), Au(100), Pt(111), and Pt(100), which have been previously hampered by time and length scale limitations of ab initio approaches or force field inaccuracies, by means of molecular dynamics simulations based on ab initio machine learning potentials built using an active learning scheme. Our results reveal that the in-plane intermolecular correlations of the water contact layer vary greatly among different systems: Whereas the contact layer on graphene and on Au(111) is predominantly homogeneous and isotropic, it is inhomogeneous and anisotropic on MoS[Formula: see text], on Au(100), and on the Pt surfaces, where it additionally forms two distinct sublayers. We apply hydrodynamics and the theory of the hydrophobic effect, to relate the energy corrugation and the characteristic length-scales of the contact layer with wetting, slippage, the hydration of small hydrophobic solutes and diffusio-osmotic transport. Thus, this work provides a microscopic picture of the water contact layer and links it to macroscopic properties of liquid/solid interfaces that are measured experimentally and that are relevant to wetting, hydrophobic solvation, nanofluidics, and osmotic transport.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Activity-induced diffusion recovery in crowded colloidal suspensions.

We show that the forces generated by active enzyme molecules are strong enough to influence the dynamics of their surroundings under artificial crowded environments. We measured the behavior of polymer microparticles in a quasi-two-dimensional system under aqueous environment, at various area fraction values of particles. In the presence of enzymatic activity, not only was the diffusion of the suspended particles enhanced at shorter time-scales, but the system also showed a transition from subdiffusive to diffusive dynamics at longer time-scale limits. Similar observations were also recorded with enzyme-functionalized microparticles. Brownian dynamics simulations have been performed to support the experimental observations.

Enhanced Sampling with Machine Learning.

Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe timescale limitations. To address this, enhanced sampling methods have been developed to improve the exploration of configurational space. However, implementing these methods is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques into different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration with enhanced sampling is more natural with many common underlying synergies. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints. It offers a comprehensive overview of this rapidly evolving field, which can be difficult to stay updated on. We highlight successful strategies such as dimensionality reduction, reinforcement learning, and flow-based methods. Finally, we discuss open problems at the exciting ML-enhanced MD interface.

Atomistic Scale Modeling of Anode/Electrolyte Interfaces in Li-Ion Batteries.

A key issue in lithium-ion batteries is understanding the solid electrolyte interphase (SEI) resulting from a reductive reaction on the anode/electrolyte interface. The presence of the SEI layer affects the transport behavior of the ions and electrons between the anode and electrolyte. Despite the influence on interfacial properties, the formation and evolution mechanism of the SEI layer are unclear owing to their complexity and dynamic nature. Atomistic-scale simulations have promoted the understanding of the reaction mechanism on the anode/electrolyte interface, the formation and evolution of the SEI layer, and their fundamental properties. This Perspective discusses the modeling and interpretations of anode/SEI/electrolyte interfaces through computational methods at the atomic-scale and highlights interfacial modeling techniques for a realistic interface design, which can overcome the limited time and length scale with high accuracy.

Asymptotic Matching between Weather and Climate Models

Abstract The Deep Numerical Analysis for Climate (DNA-Climate) is a pilot project to develop an Earth system model on a kilometer-scale horizontal mesh. The acronym “DNA” is based on the analogies between the hierarchical structures of atmospheric phenomena and living organisms. The multiscale structure of clouds and circulations may be analogous to the multiscale structure of cells and organs organized according to the blueprint, deoxyribonucleic acid (DNA). Whereas global cloud-resolving models (CRMs) can produce better solutions on shorter time scales that are decisively governed by the initial conditions, global climate models (GCMs) may generate reliable solutions on longer time scales that are largely determined to balance energy inputs and outputs. Our challenge is to build a physically valid model that consistently bridges the shorter- and longer-time-scale solutions in the intermediate time scales. Research topics of DNA-Climate are configured in consideration of the structural similarity between the climate modeling and the technique of matched asymptotic expansions in mathematics. The central question is whether a single modeling framework using only either global CRM or GCM will work adequately at all time scales of climate, or whether a multiscale modeling framework combining several models, of which each is only valid for limited time scales, will be needed. A multiscale modeling is an attractive framework for advancing climate modeling and would be an intriguing topic to be studied in parallel with global CRMs and GCMs.

Impacts of the Pandemic on Hospitality Operations: The Managerial Perspective

Despite the fact that there has been much research in the field of COVID-19 and its impacts, the existing literature has been extensively dominated by a limited time scale and departments in the hospitality industry. Therefore, this study investigates the potential short-term impacts of a pandemic on hospitality operations, specifically relating to the revision of annual budgets and the provision of regular services and how these operations progressed in 2 years. The study aims to fill the literature gap and convey the mission to guide entrepreneurs and managers from an industrial perspective. Data were collected twice: the first was in the early weeks of 2021while the second corresponds to the early weeks of 2022 through personal interviews with 13 general managers of hospitality businesses operating in Turkey. Findings indicate that the pandemic has substantially changed the proposed annual budgets and hospitality operations. However, such changes have yet to be fully stable later.

Molecular simulation approaches to study crystal nucleation from solutions: Theoretical considerations and computational challenges

AbstractNucleation is the initial step in the formation of crystalline materials from solutions. Various factors, such as environmental conditions, composition, and external fields, can influence its outcomes and rates. Indeed, controlling this rate‐determining step toward phase separation is critical, as it can significantly impact the resulting material's structure and properties. Atomistic simulations can be exploited to gain insight into nucleation mechanisms—an aspect difficult to ascertain in experiments—and estimate nucleation rates. However, the microscopic nature of simulations can influence the phase behavior of nucleating solutions when compared to macroscale counterparts. An additional challenge arises from the inadequate timescales accessible to standard molecular simulations to simulate nucleation directly; this is due to the inherent rareness of nucleation events, which may be apparent in silico at even high supersaturations. In recent decades, molecular simulation methods have emerged to circumvent length‐ and timescale limitations. However, it is not always clear which simulation method is most suitable to study crystal nucleation from solution. This review surveys recent advances in this field, shedding light on typical nucleation mechanisms and the appropriateness of various simulation techniques for their study. Our goal is to provide a deeper understanding of the complexities associated with modeling crystal nucleation from solution and identify areas for further research. This review targets researchers across various scientific domains, including materials science, chemistry, physics and engineering, and aims to foster collaborative efforts to develop new strategies to understand and control nucleation.This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Molecular and Statistical Mechanics > Free Energy Methods Theoretical and Physical Chemistry > Statistical Mechanics

Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics

Ion storage in electric double layer (EDL) of microporous carbon (< 1 nm) has been demonstrated to be a partially desolvated structure, leading to a capacitance increase. Due to the relevance of the interface structure to capacitance behavior and charge storage mechanism, it is critical to give a deep insight into the micropore/electrolyte interface at the molecular scale. Ab initio molecular dynamics (AIMD) simulation can describe the electronic structure and dynamic properties of the EDL. However, the complex EDL is hard to be well equilibrated at the limited time scale of the AIMD simulation. Here, we have performed machine learning force field accelerated molecular dynamics (MLMD) to construct the EDL of microporous carbon at different electrode potentials with a much lower cost while keeping ab initio accuracy. Based on the MLMD with a longer time scale, we have clarified the microscopic information of the interface, such as the distributions of ions and water molecules. By simulating the Na+ intercalation process, the critical role of the electrode potential on ion storage in pore is clarified. In addition, according to the MLMD simulation on micropore/electrolyte EDLs with different pore sizes, the pore size of ∼0.65 nm is appropriate for desolvation effect due to the match between the pore size and the solvated ion size. These theoretical results provide a way for studying micropore/electrolyte EDL by MLMD and improving the electrochemical performance of microporous carbon electrodes.

A New Multi-Physics Coupled Method for the Temperature Field of Dry Clutch Assembly

The temperature field of the clutch assembly is critical for the clutch design and operation life. Current modeling methods of the temperature of the clutch assembly suffer from insufficient accuracy or a limited time scale for the complicated multi-physics coupling between the contact force, friction-generated heat, heat transfer, and thermal deformation in the clutch assembly in harsh operation conditions. In order to improve the accuracy of temperature field simulation and achieve long-term time scale, a new approach to modeling the temperature is proposed based on CFD simulation and decoupling technology. Firstly, the flow-thermal bi-directional coupling method is employed to determine the convective boundary conditions between the clutch assembly and the ambient air, improving the model’s accuracy. Secondly, the thermal-solid decoupling method is then used to reduce the computational time. The temperature of the clutch assembly during the continuous engagement and disengagement process is performed using this approach and verified by the rig test. The results demonstrate that the temperature of the outer, middle, and inner diameters of the pressure plate by the model agrees well with that by the rig test. For the first engagement and disengagement processes, the proportion of simulated temperature deviations exceeding 5 °C from the measured data is only 3.03%. For the last engagement and disengagement process, while the maximum temperature of the clutch is above 350 °C, the maximum temperature deviation between simulation and measurement is 4.99%. It proves that the approach proposed for modeling the dry clutch assembly temperature field has high accuracy while achieving long-term time-scale simulation.

Stress-dependent activation entropy in thermally activated cross-slip of dislocations

Cross-slip of screw dislocations in crystalline solids is a stress-driven thermally activated process essential to many phenomena during plastic deformation, including dislocation pattern formation, strain hardening, and dynamic recovery. Molecular dynamics (MD) simulation has played an important role in determining the microscopic mechanisms of cross-slip. However, due to its limited timescale, MD can only predict cross-slip rates in high-stress or high-temperature conditions. The transition state theory can predict the cross-slip rate over a broad range of stress and temperature conditions, but its predictions have been found to be several orders of magnitude too low in comparison to MD results. This discrepancy can be expressed as an anomalously large activation entropy whose physical origin remains unclear. Here, we resolve this discrepancy by showing that the large activation entropy results from anharmonic effects, including thermal softening, thermal expansion, and soft vibrational modes of the dislocation. We expect these anharmonic effects to be significant in a wide range of stress-driven thermally activated processes in solids.

An Interactive Web-Based Dashboard to Examine Trending Topics: Application to Financial Journals

Understanding trends is helpful to identify future behaviours in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering method. The developed and freely available web application enables users to detect well-defined trending topics by experimenting with various levels of temporal bias — from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Meso-timescale atomistic simulations on coalescence process of He bubbles in Fe by SEAKMC method

He bubbles are characteristic microstructures under fusion reactor conditions. They approach and coalesce through their own migration, which significantly impacts the microstructure and material properties. However, these processes, which involve multiple migrations of metal atoms, cannot be treated by molecular dynamics (MD) due to its timescale limitation. In this study, self-evolving atomistic kinetic Monte Carlo (SEAKMC) was used to expand the timescale and reproduce bubble coalescences in Fe. To enhance selections of events that led to the process by avoiding trivial events with an extremely low activation energy such as tiny vibrations of a He atom or short-range displacements of the Fe atom, we introduced two algorithms into SEAKMC, a two-step saddle point search for the former measure and setting a threshold for a displacement distance of the Fe atom for the latter. Furthermore, by adding another algorithm to set an upper bound for the activation energy to prevent selections of events with an impractically high activation energy, we succeeded to reproduce the change in the configuration from dumbbell to elliptical up to a simulated time of 10−1 s, 8 orders longer than MD timescales. The developed method is effective for analyzing microstructures of metallic materials containing light elements and is the only method that can reach timescales comparable to those of experiments.

Interplay between VSD, pore and membrane lipids in electromechanical coupling in HCN channels.

Hyperpolarized-activated and Cyclic Nucleotide-gated (HCN) channels are the only members of the voltage-gated ion channel superfamily in mammals that open upon hyperpolarization, conferring them pacemaker properties that are instrumental for rhythmic firing of cardiac and neuronal cells. Activation of their voltage-sensor domains (VSD) upon hyperpolarization occurs through a downward movement of the S4 helix bearing the gating charges, which triggers a break in the alpha-helical hydrogen bonding pattern at the level of a conserved Serine residue. Previous structural and molecular simulation studies had however failed to capture pore opening that should be triggered by VSD activation, presumably because of a low VSD/pore electromechanical coupling efficiency and the limited timescales accessible to such techniques. Here, we have used advanced modeling strategies, including enhanced sampling molecular dynamics simulations exploiting comparisons between non-domain swapped voltage-gated ion channel structures trapped in closed and open states to trigger pore gating and characterize electromechanical coupling in HCN1. We propose that the coupling mechanism involves the reorganization of the interfaces between the VSD helices, in particular S4, and the pore-forming helices S5 and S6, subtly shifting the balance between hydrophobic and hydrophilic interactions in a 'domino effect' during activation and gating in this region. Remarkably, our simulations reveal state-dependent occupancy of lipid molecules at this emergent coupling interface, suggesting a key role of lipids in hyperpolarization-dependent gating. Our model provides a rationale for previous observations and a possible mechanism for regulation of HCN channels by the lipidic components of the membrane.

Temperature Transferable and Thermodynamically Consistent Coarse-Grained Model for Binary Polymer Systems

For the simulations of polymer systems, coarse-grained (CG) models are often developed to tackle the length and time scale limitations that are not feasible by all-atom molecular dynamic simulations. However, due to the necessary simplification or reduction in atomistic degrees of freedom, CG models usually have poor transferability over various temperatures, compositions, or thermodynamic states. In this work, the structure-based iterative Boltzmann inversion (IBI) method is further developed to obtain temperature transferable and thermodynamically consistent CG potentials for binary polymer systems. In particular, the conventional IBI procedure is first utilized to optimize single-component CG potentials from homopolymer systems while cross-interaction potentials from a random block copolymer system. Afterward, a thermodynamic integration method is applied to refine the cross-interaction potential between two constituent components in binary systems until the Flory–Huggins parameter (χ) between two components is matched with experimental value. We apply the above optimization procedure for the polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) as an example, which is widely studied experimentally, and the χ value can be easily obtained in the literature. To validate the transferability of the obtained CG potential, systems of both PS and PMMA homopolymers, random block copolymers, diblock copolymers, and PS/PMMA blends with various compositions are simulated. Many properties, including the mass density, radius distribution function, and bonded distribution functions, between CG beads generated from CG simulations match very well with those from atomistic simulations in the simulated temperature range, i.e., from 453 to 500 K. More importantly, phase morphologies of diblock copolymers and phase diagram of PS/PMMA binary blends over wide temperature and compositional ranges generated from CG simulations using our model are in good agreement with experimental results and predictions from self-consistent field theory.

GNN-assisted phase space integration with application to atomistics

Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations—especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, highlight the impact on time-coarsened atomistic predictions, and discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics.

Hydrogen distribution between the Earth's inner and outer core

Hydrogen, the most abundant element in the universe, has likely been incorporated into Earth's core due to its tendency to dissolve in iron at high pressure. Using machine-learning methods to overcome the length- and timescale limitations of quantum mechanical simulations and the difficulties in laboratory experiments, we characterize the phase behavior of superionic FeHx (x denotes nonstoichiometric compositions) in the Earth's core. We show that hexagonal packing of Fe is thermodynamically stable with and without H, and its melting temperature decreases by 700–900 K with 0.34 wt% H at the pressure of the inner core boundary. We further predict H partition coefficients between solid and liquid Fe as 0.50(3)–0.62(3), implying 1 and 33 times the mass of H in the ocean in the inner and outer core, respectively, which holds a key promise for H to explain seismic characteristics of the core.