Generalized Energy-Conserving Dissipative Particle Dynamics with Reactions

Modeling temperature-dependent transport properties in dissipative particle dynamics: A top-down coarse-graining toward realistic dynamics at the mesoscale

Dissipative particle dynamics (DPD) is a widespread computational tool to simulate the behavior of soft matter and liquids in and out of equilibrium. Although there are many applications in which the effect of temperature is relevant, most of the DPD studies have been carried out at a fixed system temperature. Therefore, this work investigates how to incorporate the effect of system temperature variation within the DPD model to capture realistic temperature-dependent system properties. In particular, this work focuses on the relationship between temperature and transport properties, and therefore, an extended DPD model for transport properties prediction is employed. Transport properties, unlike the equilibrium properties, are often overlooked despite their significant influence on the flow dynamics of non-isothermal mesoscopic systems. Moreover, before simulating the response of the system induced by a temperature change, it is important to first estimate transport properties at a certain temperature. Thus here, the same fluid is simulated across different temperature conditions using isothermal DPD with the aim to identify a temperature-dependent parametrization methodology, capable of ensuring the correctness of both equilibrium and dynamical properties. Liquid water is used as a model system for these analyses. This work proposes a temperature-dependent form of the extended DPD model where both conservative and non-conservative interaction parameters incorporate the variation of the temperature. The predictions provided by our simulations are in excellent agreement with experimental data.

Rigorous Progress in Coarse-Graining

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Assessing the structural stability and drug encapsulation efficiency of poly(ethylene glycol)-poly(L-lactic acid) nanoparticles loaded with atorvastatin calcium: Based on dissipative particle dynamics

Block polymer micelles have been proven highly biocompatible and effective in improving drug utilization for delivering atorvastatin calcium. Therefore, it is of great significance to measure the stability of drug-loading nano micelles from the perspective of block polymer molecular sequence design, which would provide theoretical guidance for subsequent clinical applications. This study aims to investigate the structural stability of drug-loading micelles formed by two diblock/triblock polymers with various block sequences through coarse-grained dissipative particle dynamics (DPD) simulations. From the perspectives of the binding strength of poly(L-lactic acid) (PLLA) and polyethylene glycol (PEG) in nanoparticles, hydrophilic bead surface coverage, and the morphological alteration of nanoparticles induced by shear force, the ratio of hydrophilic/hydrophobic sequence length has been observed to affect the stability of nanoparticles. We have found that for diblock polymers, PEG3kda-PLLA2kda has the best stability (corresponding hydrophilic coverage ratio is 0.832), while PEG4kda-PLLA5kda has the worst (coverage ratio 0.578). For triblock polymers, PEG4kda-PLLA2kda-PEG4kda has the best stability (0.838), while PEG4kda-PLLA5kda-PEG4kda possesses the worst performance (0.731), and the average performance on stability is better than nanoparticles composed of diblock polymers.

Green-Kubo expressions for transport coefficients from dissipative particle dynamics simulations revisited

This article addresses the debate about the correct application of Green-Kubo expressions for transport coefficients from dissipative particle dynamics simulations. We demonstrate that the Green-Kubo expressions are valid provided that (i) the dynamic model conserves the physical property, whose transport is studied, and (ii) the fluctuations satisfy detailed balance. As a result, the traditional expressions used in molecular dynamics can also be applied to dissipative particle dynamics simulations. However, taking the calculation of the shear viscosity as a paradigmatic example, a random contribution, whose strength scales as 1/δt1/2, with δt the time-step, can cause difficulties if the stress tensor is not separated into the different contributions. We compare our expression to that of Ernst and Brito (M. H. Ernst and R. Brito, Europhys. Lett., 2006, 73, 183-189), which arises from a diametrically different perspective. We demonstrate that the two expressions are completely equivalent and find exactly the same result both analytically and numerically. We show that the differences are not due to the lack of time-reversibility but instead from a pre-averaging of the random contributions. Despite the overall validity of Green-Kubo expressions, we find that the Einstein-Helfand relations (D. C. Malaspina et al. Phys. Chem. Chem. Phys., 2023, 25, 12025-12040) do not suffer from the need to decompose the stress tensor and can readily be used with a high degree of accuracy. Consequently, Einstein-Helfand relations should be seen as the preferred method to calculate transport coefficients from dissipative particle dynamics simulations.

Reactive Martini: Chemical Reactions in Coarse-Grained Molecular Dynamics Simulations

Chemical reactions are ubiquitous in both materials and the biophysical sciences. While coarse-grained (CG) molecular dynamics simulations are often needed to study the spatiotemporal scales present in these fields, chemical reactivity has not been explored thoroughly in CG models. In this work, a new approach to model chemical reactivity is presented for the widely used Martini CG Martini model. Employing tabulated potentials with a single extra particle for the angle dependence, the model provides a generic framework for capturing bonded topology changes using nonbonded interactions. As a first example application, the reactive model is used to study the macrocycle formation of benzene-1,3-dithiol molecules through the formation of disulfide bonds. We show that starting from monomers, macrocycles with sizes in agreement with experimental results are obtained using reactive Martini. Overall, our reactive Martini framework is general and can be easily extended to other systems. All of the required scripts and tutorials to explain its use are provided online.

A coarse-grain reactive model of RDX: Molecular resolution at the μm scale

Predictive models for the thermal, chemical, and mechanical response of high explosives at extreme conditions are important for investigating their performance and safety. We introduce a particle-based, reactive model of 1,3,5-trinitro-1,3,5-triazinane (RDX) with molecular resolution utilizing generalized energy-conserving dissipative particle dynamics with reactions. The model is parameterized with respect to the data from atomistic molecular dynamics simulations as well as from quantum mechanical calculations, thus bridging atomic processes to the mesoscales, including microstructures and defects. It accurately captures the response of RDX under a range of thermal loading conditions compared to atomistic simulations. In addition, the Hugoniot response of the CG model in the overdriven regime reasonably matches atomistic simulations and experiments. Exploiting the model's high computational efficiency, we investigate mesoscale systems involving millions of molecules and characterize size-dependent criticality of hotspots in RDX. The combination of accuracy and computational efficiency of our reactive model provides a tool for investigation of mesoscale phenomena, such as the role of microstructures and defects in the shock-to-deflagration transition, through particle-based simulation.

Generalized Energy-Conserving Dissipative Particle Dynamics with Mass Transfer. Part 1: Theoretical Foundation and Algorithm

An extension of the generalized energy-conserving dissipative particle dynamics method (GenDPDE) that allows mass transfer between mesoparticles via a diffusion process is presented. By considering the concept of the mesoparticles as property carriers, the complexity and flexibility of the GenDPDE framework were enhanced to allow for interparticle mass transfer under isoenergetic conditions, notated here as GenDPDE-M. In the formulation, diffusion is described via the theory of mesoscale irreversible processes based on linear relationships between the fluxes and thermodynamic forces, where their fluctuations are described by Langevin-like equations. The mass exchange between mesoparticles is such that the mass of the mesoparticle remains unchanged after the transfer process and requires additional considerations regarding the coupling with other system properties such as the particle internal energy. The proof-of-concept work presented in this article is the first part of a two-part article series. In Part 1, the development of the GenDPDE-M theoretical framework and the derivation of the algorithm are presented in detail. Part 2 of this article series is targeted for practitioners, where applications, demonstrations, and practical considerations for implementing the GenDPDE-M method are presented and discussed.

Generalized Energy-Conserving Dissipative Particle Dynamics with Mass Transfer. Part 2: Applications and Demonstrations

We present the second part of a two-part paper series intended to address a gap in computational capability for coarse-grain particle modeling and simulation, namely, the simulation of phenomena in which diffusion via mass transfer is a contributing mechanism. In part 1, we presented a formulation of a dissipative particle dynamics method to simulate interparticle mass transfer, termed generalized energy-conserving dissipative particle dynamics with mass transfer (GenDPDE-M). In the GenDPDE-M method, the mass of each mesoparticle remains constant following the interparticle mass exchange. In part 2 of this series, further verification and demonstrations of the GenDPDE-M method are presented for mesoparticles with embedded binary mixtures using the ideal gas (IG) and van der Waals (vdW) equation-of-state (EoS). The targeted readership of part 2 is toward practitioners, where applications and practical considerations for implementing the GenDPDE-M method are presented and discussed, including a numerical discretisztion algorithm for the equations-of-motion. The GenDPDE-M method is verified by reproducing the particle distributions predicted by Monte Carlo simulations for the IG and vdW fluids, along with several demonstrations under both equilibrium and non-equilibrium conditions. GenDPDE-M can be generally applied to multi-component mixtures and to other fundamental EoS, such as the Lennard-Jones or Exponential-6 models, as well as to more advanced EoS models such as Statistical Associating Fluid Theory.