Incremental viscosity by non-equilibrium molecular dynamics and the Eyring model

Microscopic Origins of Flow Activation Energy in Biomolecular Condensates

Material properties of biomolecular condensates dictate their form and function, influencing the diffusion of regulatory molecules and the dynamics of biochemical reactions. The increasing quality and quantity of microrheology experiments on biomolecular condensates necessitate a deeper understanding of the molecular grammar that encodes their material properties. Recent reports have identified a characteristic timescale related to network relaxation dynamics in condensates, which governs their temperature-dependent viscoelastic properties. This timescale is intimately connected to an activated process involving the dissociation of sticker regions, with the energetic barrier referred to as flow activation energy. The microscopic origin of activation energy is a complex function of sequence patterns, component stoichiometry, and external conditions. This study elucidates the microscopic origins of flow activation energy in single and multicomponent condensates composed of model peptide sequences with varying sticker and spacer motifs, with RNA as a secondary component. We dissected the effects of condensate density, RNA stoichiometry, and peptide sequence patterning using extensive sequence-resolved coarse-grained simulations. We found that flow activation energy is closely linked to the lifetime of sticker-sticker pairs under certain conditions, though the presence of multiple competing stickers further complicates this relationship. The insights gained in this study should help establish predictive multiscale models for the material properties and serve as a valuable guide for the programmable design of condensates.

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

Microscopic Origins of the Viscosity of a Lennard-Jones Liquid

Unlike crystalline solids or ideal gases, transport properties remain difficult to describe from a microscopic point of view in liquids, whose dynamics result from complex energetic and entropic contributions at the atomic scale. Two scenarios are generally proposed: one represents the dynamics in a fluid as a series of energy-barrier crossings, leading to Arrhenius-like laws, while the other assumes that atoms rearrange themselves by collisions, as exemplified by the free volume model. To assess the validity of these two views, we computed, using molecular dynamics simulations, the transport properties of the Lennard-Jones fluid and tested to what extent the Arrhenius equation and the free volume model describe the temperature dependence of the viscosity and of the diffusion coefficient at fixed pressure. Although both models reproduce the simulation results over a wide range of pressure and temperature covering the liquid and supercritical states of the Lennard-Jones fluid, we found that the parameters of the free volume model can be estimated directly from local structural parameters, also obtained in the simulations. This consistency of the results gives more credibility to the free volume description of transport properties in liquids.

Fluctuation theorem convergence in a viscoelastic medium demonstrated experimentally using a dusty plasma

The convergence of the steady-state fluctuation theorem (SSFT) is investigated in a shear-flow experiment performed in a dusty plasma. This medium has a viscoelastic property characterized by the Maxwell relaxation time τ_{M}. Using measurements of the time series of the entropy production rate, for subsystems of various sizes, it is discovered that the SSFT convergence time decreases with the increasing system size until it eventually reaches a minimum value of τ_{M}, no matter the size of the subsystem. This result indicates that the convergence of the SSFT is limited by the energy-storage property of the viscoelastic medium.

On interfacial viscosity in nanochannels

Capillary driven transport of liquids in nanoscopic channels is an omnipresent phenomenon in nature and technology including fluid flow in the human body and plants, drug delivery, nanofluidic devices, and energy/water systems. However, the kinetics of this mass transport mechanism remains in question as the well-known Lucas-Washburn (LW) model predicts significantly faster flow rates compared to the experimental observations. We here showed the role of interfacial viscosity in capillary motion slowdown in nanochannels through a combination of experimental, analytical and molecular dynamics techniques. We showed that the slower liquid flow is due to the formation of a thin liquid layer adjacent to the channel walls with a viscosity substantially greater than the bulk liquid. By incorporating the effect of the interfacial layer, we presented a theoretical model that accurately predicts the capillarity kinetics in nanochannels of different heights. Non-equilibrium molecular dynamics simulation confirmed the obtained interfacial viscosities. The viscosities of isopropanol and ethanol within the interfacial layer were 9.048 mPa s and 4.405 mPa s, respectively (i.e. 279% and 276% greater than their bulk values). We also showed that the interfacial layers are 6.4 nm- and 5.3 nm-thick for isopropanol and ethanol, respectively.

Experimental and simulation study of the high-pressure behavior of squalane and poly-α-olefins

The equation of state, dynamical properties, and molecular-scale structure of squalane and mixtures of poly-α-olefins at room temperature are studied with a combination of state-of-the-art, high-pressure experiments and molecular-dynamics simulations. Diamond-anvil cell experiments indicate that both materials are non-hydrostatic media at pressures above ∼1 GPa. The equation of state does not exhibit any sign of a first-order phase transition. High-pressure x-ray diffraction experiments on squalane show that there are no Bragg peaks, and hence, the apparent solidification occurs without crystallization. These observations are complemented by a survey of the equation of state and dynamical properties using simulations. The results show that molecular diffusion is essentially arrested above about 1 GPa, which supports the hypothesis that the samples are kinetically trapped in metastable amorphous-solid states. The shear viscosity becomes extremely large at very high pressures, and the coefficient governing its increase from ambient pressure is in good agreement with the available literature data. Finally, simulated radial distribution functions are used to explore the evolution of the molecular-scale structure with increasing pressure. Subtle changes in the short-range real-space correlations are related to a collapse of the molecular conformations with increasing pressure, while the evolution of the static structure factor shows excellent correlation with the available x-ray diffraction data. These results are of indirect relevance to oil-based lubricants, as the pressures involved are comparable to those found in engines, and hence, the ability of lubricating thin films to act as load-bearing media can be linked to the solidification phenomena studied in this work.

Thermophysical properties of glyceline–water mixtures investigated by molecular modelling

Water activity and shear viscosity of water–glyceline mixtures are important process parameters that can be effectively calculated using molecular modelling.