Iterative Boltzmann Inversion Method Research Articles

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties.

The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well.

Derived Coarse-Grained Potentials for Semicrystalline Polymers with a Blended Multistate Iterative Boltzmann Inversion Method.

In this article, we employ the multistate iterative Boltzmann inversion (MS-IBI) method to develop coarse-grained potentials capable of representing molecular structure in both the amorphous and crystalline phases of semicrystalline polymers with improved accuracy while allowing for tunable control over the dynamics governing the α-relaxation process. A unique feature of this method is that the potentials are blended using the product of the target structural distributions, for example, the radial density function, for each phase and a weighting factor. To demonstrate this approach, a family of potentials for polyethylene is developed where the weighting factor of the crystalline phase ranges is varied from zero, incorporating information only from the amorphous phase, to unity, where the model is trained from only the crystalline phase. The most accurate representation of structural distributions was obtained when the crystalline phases is weighted at 50%. However, we show that when the crystalline phase is weighted at 90%, the model more accurately represents dynamics of the α-relaxation process, with realistic predicted values of activation energy and diffusion rates, with relatively minor impact on accuracy in structure.

Coarse-grained molecular simulation of the role of curing rates on the structure and strength of polyurea

We present a reactive coarse-grained model that can more accurately simulate the interplay between step-growth polymerization and phase segregation in elastomeric copolymers such as polyurea. The coarse-grained force fields were calibrated by the iterative Boltzmann inversion method to reproduce the target structural distributions sampled from atomistic simulations of partially-cured, oligomeric prepolymers. Using this coarse-grained model, polymerization simulations were performed at simulated curing rate spanning five decades to draw connections between the rate of polymerization, the microstructure, and the high-strain rate mechanical properties of the cured elastomer. Analysis of the simulated polyurea systems suggests that curing at faster reaction rates leads to hard domains with smaller diameter ligaments that are more strongly interconnected with bridging soft segments than those formed at slow curing rates. Simulated uniaxial compression of the cured systems indicates that the key process-induced microstructural variation influencing the hardening behavior of the elastomer is the fraction of soft segments that bridge across hard domain ligaments, which can lead to a substantial increase in the flow stress at large compressive strains.

A novel coarse-grained modeling and simulation for the microstructure evolution of ferrofluids

Ferrofluid (FF) has attracted more and more attention due to its tunable rheological properties, in which the microstructure under a magnetic field plays an important role in these properties. In this study, an innovative coarse-grained (CG) model of FF is presented for the first time to capture the microstructure evolution of FF at the nanoscale. The potential functions of carrier liquid for FF CG models are obtained by the iterative Boltzmann inversion method, and the CG nonbonded interaction between the magnetic particles (MPs) is obtained via the strain energy conservation. Then, CG molecular dynamics (CGMD) simulations are used to study the microstructure evolutions of FF under different magnetic fields. Notably, we demonstrate the existence of a carrier liquid adsorption layer on the MPs surface, and its thickness is calculated. The staggered arrangement style of MPs in the chains is also revealed by the CGMD method. The present FF CG model has fully considered the carrier liquid molecular structure and interaction with the MPs, and the CGMD is used to solve the problem that classical molecular dynamics cannot simulate the microstructure of FF, making the simulation results more in line with the actual situation.

Comparison of Friction Parametrization from Dynamics and Material Properties for a Coarse-Grained Polymer Melt.

In this work, we extend an approach to coarse-grained (CG) modeling for polymer melts in which the conservative potential is parametrized using the iterative Boltzmann inversion (IBI) method and the accelerated dynamics inherent to IBI are corrected using the dissipative Langevin thermostat with a single tunable friction parameter (J. Chem. Phys. 2021, 154, 084114). Diffusive measures from picoseconds to nanoseconds are used to determine the Langevin friction factor to apply to the CG model to recover all-atom (AA) dynamics; the resulting friction factors are then compared for consistency. Here, we additionally parametrize the CG dynamics using a material property, the zero-shear viscosity, which we measure using the Green-Kubo (GK) method. Two materials are studied, squalane as a function of temperature and the same polystyrene oligomers previously studied as a function of chain length. For squalane, the friction derived from the long-time diffusive measures and the viscosity all strongly increase with decreasing temperature, showing an Arrhenius-like dependence, and remain consistent with each other over the entire temperature range. In contrast, the friction required for the picosecond diffusive measurement, the Debye-Waller factor, is somewhat lower than the friction from long-time measures and relatively insensitive to temperature. A time-dependent friction would be required to exactly reproduce the AA measurements during the caging transition connecting these two extremes over the entire timespan at this level of coarse-graining. For the polystyrene oligomers for which we previously characterized the diffusive friction, the viscosity-parametrized frictions are consistent with the diffusive measures for the smallest chain length. However, for the longer chains, we find different trends based on measurement method with friction derived from rotational diffusion remaining nearly constant, friction derived from translational diffusion showing a modestly increasing trend, and viscosity-derived friction showing a modest decreasing trend. This seems to indicate that there is some sensitivity of the friction measurement method for systems with increased relaxation times and that in particular, the unsteady dynamics of the individual parametrization schemes plays a role in this. Increased difficulty in applying the GK method with increasing relaxation time of the longer chain systems is also discussed. Overall, we find that when the material is in a high-temperature melt state and the viscosity measurement is reliable, the friction parametrization from the diffusive friction measures is consistent and the lower cost diffusive parametrization is a reliable means for modeling viscosity. Our data give insight into the time-dependent friction one might compute using a non-Markovian approach to enable the recovery of AA dynamics over a wider range of time scales than can be computed using a single friction.

Atomistic to Mesoscopic Modelling of Thermophysical Properties of Graphene-Reinforced Epoxy Nanocomposites.

This research addresses the need for a multiscale model for the determination of the thermophysical properties of nanofiller-enhanced thermoset polymer composites. Specifically, we analyzed the thermophysical properties of an epoxy resin containing bisphenol-A diglyceryl ether (DGEBA) as an epoxy monomer and dicyandiamide (DICY) and diethylene triamine (DETA) as cross-linking agents. The cross-linking process occurs at the atomistic scale through the formation of bonds among the reactive particles within the epoxy and hardener molecules. To derive the interatomic coarse-grained potential for the mesoscopic model and match the density of the material studied through atomic simulations, we employed the iterative Boltzmann inversion method. The newly developed coarse-grained molecular dynamics model effectively reproduces various thermophysical properties of the DGEBA-DICY-DETA resin system. Furthermore, we simulated nanocomposites made of the considered epoxy additivated with graphene nanofillers at the mesoscopic level and verified them against continuum approaches. Our results demonstrate that a moderate amount of nanofillers (up to 2 wt.%) increases the elastic modulus and thermal conductivity of the epoxy resin while decreasing the Poisson's ratio. For the first time, we present a coarse-grained model of DGEBA-DICY-DETA/graphene materials, which can facilitate the design and development of composites with tunable thermophysical properties for a potentially wide range of applications, e.g., automotive, aerospace, biomedical, or energy ones.

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.

Coarse-Grained Model Incorporating Short- and Long-Range Effective Potentials for the Fast Simulation of Micelle Formation in Solutions of Ionic Surfactants.

A coarse-grained model comprising short- and long-range effective potentials, parametrized with the iterative Boltzmann inversion (IBI) method, is presented for capturing micelle formation in aqueous solutions of ionic surfactants using as a model system sodium dodecyl sulfate (SDS). In the coarse-grained (CG) model, each SDS molecule is represented as a sequence of four beads while each water molecule is modeled as a single bead. The proposed CG scheme involves ten potential energy functions: four of them describe bonded interactions and control the distribution functions of intramolecular degrees of freedom (bond lengths, valence angles, and dihedrals) along an SDS molecule while the other six account for intermolecular interactions between pairs of SDS and water beads and control the radial distribution functions. The nonbonded effective potentials between coarse-grained SDS molecules extend up to about 12 nm and capture structural and morphological features of the micellar solution both at short and long distances. The long-range component of these potentials, in particular, captures correlations between surfactant molecules belonging to different micelles and is essential to describe ordering associated with micelle formation. A new strategy is introduced for determining the effective potentials through IBI by using information (target distribution functions) extracted from independent atomistic simulations of a micellar reference system (a salt-free SDS solution at total surfactant concentration cT equal to 103 mM, temperature T equal to 300 K, and pressure P equal to 1 atm) obtained through a multiscale approach described in an earlier study. It employs several optimization steps for bonded and nonbonded interactions and a gradual parametrization of the short- and long-range components of the latter, followed by reparametrization of the bonded ones. The proposed CG model can reproduce remarkably accurately the microstructure and morphology of the reference system within only a few hours of computational time. It is therefore very promising for future studies of structural and morphological behavior of various liquid surfactant formulations.

Coarse-Grained Water Model Development for Accurate Dynamics and Structure Prediction.

Several coarse-graining (CG) methods have been combined to develop a CG model of water capable of the accurate prediction of structure and dynamics properties. The multiscale coarse-graining (MS-CG) method based on force matching and the PDF-based coarse-graining method were used for accurate dynamics prediction. The iterative Boltzmann inversion (IBI) method was added for accurate structure representation. The approach is applied to bulk water, and the results show close reproduction of the CG structure when compared with the reference atomistic data. The combination of MS-CG and IBI methods facilitates the development of CG force fields at different temperatures based on a single MS-CG coarse-graining procedure. The dynamic properties of the CG water model closely match those obtained from the reference atomistic system. The general application of this approach to any existing coarse-graining methods is discussed.

Multiscale Simulation of Ternary Stratum Corneum Lipid Mixtures: Effects of Cholesterol Composition.

Molecular dynamics simulations of mixtures of the ceramide nonhydroxy-sphingosine (NS), cholesterol, and a free fatty acid are performed to gain molecular-level understanding of the structure of the lipids found in the stratum corneum layer of skin. A new coarse-grained force field for cholesterol was developed using the multistate iterative Boltzmann inversion (MS-IBI) method. The coarse-grained cholesterol force field is compatible with previously developed coarse-grained force fields for ceramide NS, free fatty acids, and water and validated against atomistic simulations of these lipids using the CHARMM force field. Self-assembly simulations of multilayer structures using these coarse-grained force fields are performed, revealing that a large fraction of the ceramides adopt extended conformations, which cannot occur in the single bilayer in water structures typically studied using molecular simulation. Cholesterol fluidizes the membrane by promoting packing defects, and an increase in cholesterol content is found to reduce the bilayer thickness due to an increase in interdigitation of the C24 lipid tails, consistent with experimental observations. Using a reverse-mapping procedure, a self-assembled coarse-grained multilayer system is used to construct an equivalent structure with atomistic resolution. Simulations of this atomistic structure are found to closely agree with experimentally derived neutron scattering length density profiles. Significant interlayer hydrogen bonding is observed in the inner layers of the atomistic multilayer structure that are not found in the outer layers in contact with water or in equivalent bilayer structures. This work highlights the importance of simulating multilayer structures, as compared to the more commonly studied bilayer systems, to enable more appropriate comparisons with multilayer experimental membranes. These results also provide validation of the efficacy of the MS-IBI derived coarse-grained force fields and the framework for multiscale simulation.

Role of mapping schemes on dynamical and mechanical properties of coarse-grained models of cis-1,4-polyisoprene

We study and compare four coarse-grained models of cis-1,4-polyisoprene distinguished by mapping schemes for locating superatoms. First, coarse-grained potentials are obtained by iterative Boltzmann inversion method for the polymer melt. For all the coarse-grained models considered, time-scale factors based on translational and rotational motion were found to be different. However, coarse-grained potentials were unable to reproduce stress–strain behaviour of the underlying detailed model due to weak attractive nature of the nonbonded part of the coarse-grained potential. Consequently, the nonbonded potentials were optimized using particle swarm optimization to match the tensile behaviour of a detailed model of the polymer below glass transition temperature. While the modified potentials seemed to better predict the mechanical behaviour, the ability to accurately predict simultaneously the structural distributions also, depends on the mapping scheme.

Effects of silica/carbon black hybrid nanoparticles on the dynamic modulus of uncrosslinked cis-1,4-polyisoprene rubber: Coarse-grained molecular dynamics

Coarse-grained molecular dynamics (CGMD) simulations are employed to study the viscoelastic properties of cis-1,4-polyisoprene with silica/carbon black (CB) hybrid nanoparticles (NPs). Our simulation work for the first time investigates the rubber with both silica and CB NPs with the potential functions from the iterative Boltzmann inversion (IBI) method. We calculate the dynamic shear modulus of different models, and analyze the effect of silica/CB ratio and grafted NPs. In order to explain the differences in modulus from the microscopic view, we make the quantitative comparisons of the NP-NP, polymer-polymer and NP-polymer structures, such as using the radius of gyration, the coordination number, and the potential energy. The characteristics of dual phase filler are mainly in two aspects of potential functions. One is that the silica-CB interaction is weaker than that of their single components, and the other is that the NP-polymer potential functions are different in shape for Silica and CB. The former causes NPs to be more likely to aggregate in the form of "bridging". The latter causes that the silica-polymer interaction is weaker at small amplitudes, while the CB-polymer interaction is weaker at large amplitudes for the range of the strong CB-polymer interaction is narrow. The simulation result also shows that the break-up of the NP network is the main factor for the Payne effect, and the interfacial separation is the second factor. The reason why the storage modulus curves of the models with grafted NPs decrease slowly at small amplitudes is the hindering effect of the graft chains on the growth of the interfacial voids.

Coarse-grained molecular dynamics simulation of cis-1,4-polyisoprene with silica nanoparticles under extreme uniaxial tension

Coarse-grained (CG) cis-1,4-polyisoprene (PI) models with multiple silica nanoparticles (NPs) are built to study the effect of NPs and crosslinks in the uniaxial tensile simulation. The potential functions of the CG models are obtained mainly via the iterative Boltzmann inversion method. The tensile simulation results show that the grafted silica NPs and the crosslinked structure play reinforcing roles while the smooth silica NPs do the opposite, which have the similar trends with the experiment results. The differences of mechanical properties for these models are studied from different microscopic aspects, such as the network of NPs, the bond lengths, the free molecular chains, the entanglements, the stress and strain distribution and the microvoid evolution. As a result, the main reasons for the weakening of PI models with smooth silica NPs come from the weak interfacial interaction, the inhomogeneity of structural deformation and the reduction of the number of entanglements. However, if there are graft chains, the interfacial interaction can be enhanced by entangling with the matrix molecular chains. The graft chains can make it possible for the aggregated NPs to separate and can hinder the growth of microvoids at the interface. In addition, the inconsistency of the stress and strain distributions at the microscopic level is verified and the nucleation mechanism of microvoids is believed to be caused by the local violent movement of molecular chains.

Coarse-grained model of a nanoscale-segregated ionic liquid for simulations of low-temperature structure and dynamics

We perform molecular dynamics simulations to study the structure and dynamics of the ionic liquid [Omim][TFSI] in a broad temperature range. A particular focus is the progressing nanoscale segregation into polar and nonpolar regions upon cooling. As this analysis requires simulations of large systems for long times, we use the iterative Boltzmann inversion method to develop a new coarse-grained (CG) model from a successful all-atom (AA) model. We show that the properties are similar for both levels of description at room temperature, while the CG model shows stronger nanoscale segregation and faster diffusion dynamics than its AA counterpart at low temperatures. Exploiting these features of the CG model, we find that the characteristic length scale of the structural inhomogeneity nearly doubles to ∼3 nm when the temperature is decreased to about 200 K. Moreover, we observe that the nanoscale segregation is characterized by a bicontinuous morphology. In worm-like nonpolar regions, the ends of the octyl rests of the cations preferentially aggregate in the centers, while the other parts of the alkyl chains tend to be aligned parallel on a next-neighbor level and point outward, allowing for an integration of the imidazolium head groups of the cations into polar regions together with the anions, resembling to some degree the molecular arrangement in cylindrical micelles.

Optimization assisted coarse-grained modeling of agglomerated nanoparticle reinforced thermosetting polymers

The interactions between nanoparticles and thermosetting polymers strongly control the overall mechanical properties of polymer nanocomposites. In this study, a coarse-grained model for agglomerated boehmite nanoparticle/epoxy nanocomposites is developed to capture the interactions at the nanoscale. An optimization assisted modified iterative Boltzmann inversion method is proposed to calibrate coarse-grained force fields with two different levels of coarse-graining for an epoxy matrix. Furthermore, the coarse-grained force field of nanoparticles is obtained using the strain energy conservation between coarse-grained models and all-atom systems. The proposed model has the ability to obtain transferable force fields allowing the prediction of material behavior in a broad range of temperatures at significantly lower computational cost compared to all-atom simulations. The applicability of the coarse-grained model to estimate the elastic properties of the polymer reinforced nanocomposites is evaluated using experimental data. It is also shown that the elastic properties of the composites depends on the weight fraction and distribution of nanoparticles. The simulation results reveal that although the modification of epoxy matrices with nano-additives is a crucial factor in enhancing the elastic properties of epoxy matrices, the aggregation of nanoparticles decreases their effectiveness.

Dynamically consistent coarse-grain simulation model of chemically specific polymer melts via friction parameterization.

Coarse-grained (CG) models of polymers involve grouping many atoms in an all-atom (AA) representation into single sites to reduce computational effort yet retain the hierarchy of length and time scales inherent to macromolecules. Parameterization of such models is often via "bottom-up" methods, which preserve chemical specificity but suffer from artificially accelerated dynamics with respect to the AA model from which they were derived. Here, we study the combination of a bottom-up CG model with a dissipative potential as a means to obtain a chemically specific and dynamically correct model. We generate the conservative part of the force-field using the iterative Boltzmann inversion (IBI) method, which seeks to recover the AA structure. This is augmented with the dissipative Langevin thermostat, which introduces a single parameterizable friction factor to correct the unphysically fast dynamics of the IBI-generated force-field. We study this approach for linear polystyrene oligomer melts for three separate systems with 11, 21, and 41 monomers per chain and a mapping of one monomer per CG site. To parameterize the friction factor, target values are extracted from the AA dynamics using translational monomer diffusion, translational chain diffusion, and rotational chain motion to test the consistency of the parameterization across different modes of motion. We find that the value of the friction parameter needed to bring the CG dynamics in line with AA target values varies based on the mode of parameterization with short-time monomer translational dynamics requiring the highest values, long-time chain translational dynamics requiring the lowest values, and rotational dynamics falling in between. The friction ranges most widely for the shortest chains, and the span narrows with increasing chain length. For longer chains, a practical working value of the friction parameter may be derived from the rotational dynamics, owing to the contribution of multiple relaxation modes to chain rotation and a lack of sensitivity of the translational dynamics at these intermediate levels of friction. A study of equilibrium chain structure reveals that all chains studied are non-Gaussian. However, longer chains better approximate ideal chain dimensions than more rod-like shorter chains and thus are most closely described by a single friction parameter. We also find that the separability of the conservative and dissipative potentials is preserved.

Coarse-grained modeling of nanocube self-assembly system and transition network analyses

An implicit-solvent coarse-grained (CG) model of a self-assembling molecular capsule called nanocube is developed based on all-atom molecular dynamics (MD) simulations and the iterative Boltzmann inversion method. The present model well describes the chemical specificity of constituent monomers (gear-shaped amphiphiles) and effective intermonomer interactions in aqueous methanol. Standard and replica exchange CG-MD simulations are performed and it is confirmed that the CG model can reproduce a highly ordered self-assembled structure similar to the AA model. Furthermore, the transition network is studied based on a cluster analysis, which suggests several major routes for the assembly via metastable bipyramidal states.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved.

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.

A coarse-graining approach for modeling nonlinear mechanical behavior of FCC nano-crystals

Abstract The ever-increasing growth of nano-technology has elevated the necessity for development of new computational methods that are capable of evaluating large systems at nano-scale. The existing techniques, such as the molecular dynamics, lack the ability to simulate large systems of practical size and time scales. In order to provide a realistic simulation of large models, the multi-scale methods such as coarse-graining, have therefore become very popular. The coarse-grained models have mostly been used to simulate large biomolecules, such as proteins, lipids, DNA and polymers. In this paper, the Iterative Boltzmann Inversion (IBI) coarse-graining technique is applied to FCC nano-crystals; the successful application of this technique enables the simulation of large metallic models, the simulation of which using conventional molecular dynamics methods would be time and energy consuming. The application process consists of two steps. In the first step, the IBI method is employed to develop an interatomic potential for the all-atom system. The efficiency of generated potential is, then, compared with EAM potential. Once the efficiency of IBI method is established in producing a proper potential to simulate all-atom behavior, the mechanical behavior of the coarse-grained system is compared, in the second step, with the behavior of all-atom system. Finally, several numerical simulations are provided to investigate the efficiency of proposed coarse-graining technique in predicting the nonlinear mechanical behavior of FCC nano-crystals.

Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees

We present a practical computational framework for the coarse-graining of cross-linked epoxies by developing a machine-learning technique, which integrates molecular dynamics simulations with artificial neural network (ANN) assisted particle swarm optimization (PSO) algorithm. Key features of the framework include two aspects: (1) determining the bonded interactions via the iterative Boltzmann inversion method to emulate the local structures of the epoxies and, (2) optimizing the nonbonded interaction potentials through the machine-learning approach to reproduce the mechanical properties. Such machine-learning based technique is computationally efficient in searching for the optimal solution of nonbonded potential parameters and enables the CG model to become transferable within a wide range of cross-linking degrees. This is mainly attributed to the fact that ANN can give good predictions based on training database obtained from CG simulations and thus greatly accelerates the PSO algorithm in achieving the optimal solution. On the basis of the DOC-transferable CG model, the cohesive interaction strength is phenomenologically adjusted to preserve the temperature-dependent properties. The CG model allows the mechanical properties of cross-linked epoxies to be predicted with reasonable accuracy over wide ranges of cross-linking degrees and temperature. The proposed framework will become highly beneficial to the design of high performance epoxy-matrix nanocomposites.