Coupling discrete and continuum concentration particle models for multiscale and hybrid molecular-continuum simulations

Simulation of a Particle Domain in a Continuum, Fluctuating Hydrodynamics Reservoir

In molecular simulation and fluid mechanics, the coupling of a particle domain with a continuum representation of its embedding environment is an ongoing challenge. In this Letter, we show a novel approach where the latest version of the adaptive resolution scheme (AdResS), with noninteracting tracers as particles' reservoir, is combined with a fluctuating hydrodynamics (FHD) solver. The resulting algorithm, supported by a solid mathematical model, allows for a physically consistent exchange of matter and energy between the particle domain and its fluctuating continuum reservoir. Numerical tests are performed to show the validity of the algorithm. Differently from previous algorithms of the same kind, the current approach allows for simulations where, in addition to density fluctuations, also thermal fluctuations can be accounted for, thus large complex molecular systems, as, for example, hydrated biological membranes in a thermal field, can now be efficiently treated.

Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process

In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving optimization method is applied to overcome the error accumulation problem when using RNN models. The optimization goal is to achieve the desired average etching depth and average bottom roughness within the least amount of time. The simulation results show that our prediction model is accurate enough and the optimization objectives can be completed well.

The physics of open systems for the simulation of complex molecular environments in soft matter

Molecular dynamics (MD) has become one of the most powerful tools of investigation in soft matter. Despite such success, simulations of large molecular environments are mostly run using the approximation of closed systems without the possibility of exchange of matter. Due to the molecular complexity of soft matter systems, an optimal simulation strategy would require the application of concurrent multiscale resolution approaches such that each part of a large system can be considered as an open subsystem at a high resolution embedded in a large coarser reservoir of energy and particles. This paper discusses the current capability and the future perspectives of multiscale adaptive resolution MD methods to satisfy the conceptual principles of open systems and to perform simulations of complex molecular environments in soft matter.

Multiscale Simulation of Protein Hydration Using the SWINGER Dynamical Clustering Algorithm

To perform computationally efficient concurrent multiscale simulations of biological macromolecules in solution, where the all-atom (AT) models are coupled to supramolecular coarse-grained (SCG) solvent models, previous studies resorted to modified AT water models, such as the bundled-simple point charge (SPC) models, that use semiharmonic springs to restrict the relative movement of water molecules within a cluster. Those models can have a significant impact on the simulated biomolecules and can lead, for example, to a partial unfolding of a protein. In this work, we employ the recently developed alternative approach with a dynamical clustering algorithm, SWINGER, which enables a direct coupling of original unmodified AT and SCG water models. We perform an adaptive resolution molecular dynamics simulation of a Trp-Cage miniprotein in multiscale water, where the standard SPC water model is interfaced with the widely used MARTINI SCG model, and demonstrate that, compared to the corresponding full-blown AT simulations, the structural and dynamic properties of the solvated protein and surrounding solvent are well reproduced by our approach.

A quantitative study of enhanced drying flux from a narrow liquid–air interface of colloidal suspensions during directional drying

Drying flux changes by the drying interfacial area of a colloidal suspension that affects the formation kinetics of particulate films.