Simultaneous Iterative Boltzmann Inversion for Coarse-Graining of Polyurea

Experimental and numerical study on explosion resistance of polyurea-coated shelter in petrochemical industry

To reduce the number of casualties in explosion accidents, blast-resistant shelters can be used to protect personnel in high-risk areas of petrochemical processing plants. In this work, the deformation behaviours of uncoated and polyurea-coated blast-resistant plates were studied through gas explosion tests. An ANSYS/LS-DYNA model of a polyurea-coated shelter was established, and the dynamic responses of the shelter under various explosion loads were analysed. A series of fuel-air explosion tests were carried out to investigate the explosion resistance of the full-scale shelter. The results showed that compared with the uncoated blast-resistant plate, the deformation of the polyurea-coated blast-resistant plate was significantly reduced. The overall deformation of the shelter was the central depression of the wall and the inward bending of the frame. The damage effect of a typical high-overpressure, low-duration load was greater than that of typical low-overpressure, long-duration load. The shelter remained intact under three repeated explosive loads, with cracks appearing on the inner wall but no collapse or debris splashing. The shock wave attenuation rate of the shelter reached over 90%, which could significantly reduce the number of indoor casualties.

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.

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Energy renormalization for temperature transferable coarse-graining of silicone polymer

The bottom-up prediction of thermodynamic and mechanical behaviors of polymeric materials based on molecular dynamics (MD) simulation is of critical importance in polymer physics. Although the atomistically informed coarse-grained (CG) model can access greater spatiotemporal scales and retain essential chemical specificity, the temperature-transferable CG model is still a big challenge and hinders widespread application of this technique. Herein, we use a silicone polymer, i.e., polydimethylsiloxane (PDMS), having an incredibly low chain rigidity as a model system, combined with an energy-renormalization (ER) approach, to systematically develop a temperature-transferable CG model. Specifically, by introducing temperature-dependent ER factors to renormalize the effective distance and cohesive energy parameters, the developed CG model faithfully preserved the dynamics, mechanical and conformational behaviors compared with the target all-atomistic (AA) model from glassy to melt regimes, which was further validated by experimental data. With the developed CG model featuring tremendously improved computational efficiency, we systematically explored the influences of cohesive interaction strength and temperature on the dynamical heterogeneity and mechanical response of polymers, where we observed consistent trends with other linear polymers with varying chain rigidity and monomeric structures. This study serves as an extension of our proposed ER approach of developing temperature transferable CG models with diverse segmental structures, highlighting the critical role of cohesive interaction strength on CG modeling of polymer dynamics and thermomechanical behaviors.

Efficient characterization of double-cross-linked networks in hydrogels using data-inspired coarse-grained molecular dynamics model

The network structure within polymers significantly influences their mechanical properties, including their strength, toughness, and fatigue resistance. All-atom molecular dynamics (AAMD) simulations offer a method to investigate the energy dissipation mechanism within polymers during deformation and fracture; Such an approach is, however, computationally inefficient when used to analyze polymers with complex network structures, such as the common chemically double-networked hydrogels. Alternatively, coarse-grained molecular dynamics (CGMD) models, which reduce the computational degrees of freedom by concentrating a set of adjacent atoms into a coarse-grained bead, can be employed. In CGMD simulations, a coarse-grained force field (CGFF) is a critical factor affecting the simulation accuracy. In this paper, we proposed a data-based method for predicting the CGFF parameters to improve the simulation efficiency of complex cross-linked network in polymers. Here, we utilized a typical chemically double-networked hydrogel as an example. An artificial neural network was selected, and it was trained with the tensile stress-strain data from the CGMD simulations using different CGFF parameters. The CGMD simulations using the predicted CGFF parameters show good agreement with the AAMD simulations and are almost fifty times faster. The data-inspired CGMD model presented here broadens the applicability of molecular dynamics simulations to cross-linked polymers and has the potential to provide insights that will aid the design of polymers with desirable mechanical properties.

Investigating the Hydrogen Bond-Induced Self-Assembly of Polysulfamides Using Molecular Simulations and Experiments

In this paper, we present a synergistic, experimental, and computational study of the self-assembly of N,N'-disubstituted polysulfamides driven by hydrogen bonds (H-bonds) between the H-bonding donor and acceptor groups present in repeating sulfamides as a function of the structural design of the polysulfamide backbone. We developed a coarse-grained (CG) polysulfamide model that captures the directionality of H-bonds between the sulfamide groups and used this model in molecular dynamics (MD) simulations to study the self-assembly of these polymers in implicit solvent. The CGMD approach was validated by reproducing experimentally observed trends in the extent of crystallinity for three polysulfamides synthesized with aliphatic and/or aromatic repeating units. After validation of our CGMD approach, we computationally predicted the effect of repeat unit bulkiness, length, and uniformity of segment lengths in the polymers on the extent of orientational and positional order among the self-assembled polysulfamide chains, providing key design principles for tuning the extent of crystallinity in polysulfamides in experiments. Those computational predictions were then experimentally tested through the synthesis and characterization of polysulfamide architectures.

Coarse-Graining of Imaginary Time Feynman Path Integrals: Inclusion of Intramolecular Interactions and Bottom-up Force-Matching

Feynman's imaginary time path integral formalism of quantum statistical mechanics and the corresponding quantum-classical isomorphism provide a tangible way of incorporating nuclear quantum effect (NQE) in the simulation of condensed matter systems using well-developed classical simulation techniques. Our previous work has presented the many-body coarse-graining of path integral (CG-PI) theory that builds an isomorphism between the quantum partition function of N distinguishable particles and the classical partition function of 2N pseudoparticles. In this present work, we develop a generalized version of the many-body CG-PI theory that incorporates many-body interactions in the force field. Based on the new derivation, we provide a numerical CG-PI (n-CG-PI) modeling strategy parametrized from the underlying path integral molecular dynamics (PIMD) trajectories using force matching and Boltzmann inversion. The n-CG-PI models for two liquid systems are shown to capture well both the intramolecular and intermolecular structural correlations of the reference PIMD simulations. The generalized derivation of the many-body CG-PI theory and the n-CG-PI model presented in this work extend the scope of the CG-PI formalism by generalizing the previously limited theory to incorporate force fields of realistic molecular systems.

Polyurea for Blast and Impact Protection: A Review

Polyurea has attracted extensive attention from researchers and engineers in the field of blast and impact protection due to its excellent quasi-static mechanical properties and dynamic mechanical properties. Its mechanical properties and energy absorption capacity have been tuned by means of formulation optimization, molecular dynamics (MD) simulation and the addition of reinforcing materials. Owing to the special molecular structure of polyurea, the mechanism of polyurea protection against blasts and impacts is the simultaneous effect of multiple properties. For different substrates and structures, polyurea needs to provide different performance characteristics, including adhesion, hardness, breaking elongation, etc., depending on the characteristics of the load to which it is subjected. The current article reviews relevant publications in the field of polyurea blast and impact protection, including material optimization, protection mechanisms and applications in blast and impact protection.

An Enhanced Scheme for Multiscale Modeling of Thermomechanical Properties of Polymer Bulks

While multiscale modeling significantly enhances the capability of molecular simulations of polymer systems, it is well realized that the systematically derived coarse-grained (CG) models generally underestimate the thermomechanical properties. In this work, a charge-based mapping scheme has been adopted to include explicit electrostatic interactions and benchmarked against two typical polymers, atactic poly(methyl methacrylate) (PMMA) and polystyrene (PS). The CG potentials are parameterized against the oligomer bulks of nine monomers per chain to match the essential structural features and the two basic pressure-volume-temperature (PVT) properties, which are obtained from the all-atomistic (AA) molecular dynamics (MD) simulations at a single elevated temperature. The so-parameterized CG potentials are extended with the MD method to simulate the two polymer bulks of one hundred monomers per chain over a wide temperature range. Without any scaling, all the simulated results, including mass densities and bulk moduli at room temperature, thermal expansion coefficients at rubbery and glassy states, and glass transition temperatures (T_g), compare well with the corresponding experimental data. The proposed scheme not only contributes to realistically simulating various thermomechanical properties of both apolar and polar polymers but also allows for directly simulating their electrical properties.

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.

Effectiveness of coarse graining degree and speedup on the dynamic properties of homopolymer

Evaluation of effective coarse graining (CG) degree and reasonable speedup relative to all-atomistic (AA) model was conducted to provide a basis for building appropriate larger-scale model. The reproducibility of atomistic conformation and temperature transferability both act as the analysis criteria to resolve the maximum acceptable CG degree. Taking short- and long time spans into account simultaneously in the estimation of computational speedup, a dynamic scaling factor is accessible in fitting mean squared displacement ratio of CG to AA as an exponential function. Computing loss in parallel running is an indispensable component in acceleration, which was also added in the evaluation. Subsequently, a quantified prediction of CG speedup arises as a multiplication of dynamic scaling factor, computing loss, time step, and the square of reduction in the number of degrees of freedom. Polyethylene oxide was adopted as a reference system to execute the direct Boltzmann inversion and iterative Boltzmann inversion. Bonded and non-bonded potentials were calculated in CG models with 1~4 monomers per bead. The effective CG degree was determined as two at the most with a speedup of four orders magnitude over AA in this study. Determination of effectiveness CG degree and the corresponding speedup prediction provide available tools in larger spatiotemporal-scale calculations.

Coarse-grained implicit solvent lipid force field with a compatible resolution to the Cα protein representation

Biological membranes have been prominent targets for coarse-grained (CG) molecular dynamics simulations. While minimal CG lipid models with three beads per lipid and quantitative CG lipid models with >10 beads per lipid have been well studied, in between them, CG lipid models with a compatible resolution to residue-level CG protein models are much less developed. Here, we extended a previously developed three-bead lipid model into a five-bead model and parameterized it for two phospholipids, POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine). The developed model, iSoLF, reproduced the area per lipid, hydrophobic thickness, and phase behaviors of the target phospholipid bilayer membranes at the physiological temperature. The model POPC and DPPC membranes were in liquid and gel phases, respectively, in accordance with experiments. We further examined the spontaneous formation of a membrane bilayer, the temperature dependence of physical properties, the vesicle dynamics, and the POPC/DPPC two-component membrane dynamics of the CG lipid model, showing some promise. Once combined with standard Cα protein models, the iSoLF model will be a powerful tool to simulate large biological membrane systems made of lipids and proteins.

Development of Coarse-Grained Models for Poly(4-vinylphenol) and Poly(2-vinylpyridine): Polymer Chemistries with Hydrogen Bonding

In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries-poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.

Development of a coarse-grained lipid model, LIME 2.0, for DSPE using multistate iterative Boltzmann inversion and discontinuous molecular dynamics simulations

We suggest an improved version of the intermediate resolution implicit solvent model for lipids, LIME, that was previously developed for use with discontinuous molecular dynamics (DMD) simulations. LIME gets its geometrical and the energy parameters between bonded and nonbonded pairs of coarse-grained (CG) sites from atomistic simulations. The improved model, LIME 2.0, uses multiple square wells rather than the single square well used in original LIME to obtain intermolecular interactions that more faithfully mimic those from atomistic simulations. The multi-state iterative Boltzmann inversion (MS-IBI) scheme is used to determine the interaction parameters. This means that a single set of interaction parameters between coarse-grained sites can be used to represent the lipid bilayers at different temperatures. The physical properties of CG DSPE lipid bilayer are calculated using CG simulations and compared to atomistic simulations results to verify the improved model. The phase transition temperature of the lipid bilayer is measured accurately and the lipid translocation phenomenon, " flip-flop" is observed through CG simulation. These results suggest that CG parameterization using multiple square-well and the MS-IBI scheme is well suited to the study of lipid bilayers cross a range of temperatures with DMD simulations.

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.

100th Anniversary of Macromolecular Science Viewpoint: Modeling and Simulation of Macromolecules with Hydrogen Bonds: Challenges, Successes, and Opportunities

Macromolecular materials with directional interactions such as hydrogen bonds exhibit numerous attractive features in terms of structure, thermodynamics, and dynamics. Besides enabling precise tuning of desirable geometries in the assembled state (e.g., programmable coordination numbers depending on the valency of the directional interaction), mixing in a blend/composite through stabilization via hydrogen bonds between the various components, hydrogen bonds can also impart responsiveness to external stimuli (e.g., temperature, pH). In biomacromolecules (e.g., proteins, DNA, polysaccharides), hydrogen bonds play a key role in stabilizing secondary and tertiary structures, which in turn define the function of these macromolecules. In this Viewpoint, I present the challenges, successes, and opportunities for molecular modeling and simulations to conduct fundamental and application-focused research on macromolecular materials with hydrogen bonding interactions. The past successes and limitations of atomistic simulations are discussed first, followed by highlights from recent developments in coarse-grained modeling and their use in studies of (synthetic and biologically relevant) macromolecular materials. Model development focused on polynucleotides (e.g., DNA, RNA, etc.), polypeptides, polysaccharides, and synthetic polymers at experimentally relevant conditions are highlighted. This viewpoint ends with potential future directions for macromolecular modeling and simulations with other types of directional interactions beyond hydrogen bonding.

DEFORMATION INDEX APPLIED TO IMPACT

Almost three decades ago, S. Futamura devised the deformation index concept for determining the control parameter for the viscoelastic response of deformed elastomers. We have extended this concept to impact mitigation, wherein material hardness and energy dissipation typically both affect the behavior. Laboratory impact tests were carried out on a series of compounds to deduce the deformation index pertinent to the rubber component. We then analyzed ballistic experiments, wherein material failure is associated with more complex conditions. The utility and limitations of this approach are discussed.

Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials

Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.

A DNA Origami Platform for Single-Pair Förster Resonance Energy Transfer Investigation of DNA-DNA Interactions and Ligation

DNA double-strand breaks (DSBs) pose an everyday threat to the conservation of genetic information and therefore life itself. Several pathways have evolved to repair these cytotoxic lesions by rejoining broken ends, among them the nonhomologous end-joining mechanism that utilizes a DNA ligase. Here, we use a custom-designed DNA origami nanostructure as a model system to specifically mimic a DNA DSB, enabling us to study the end-joining of two fluorescently labeled DNA with the T4 DNA ligase on the single-molecule level. The ligation reaction is monitored by Förster resonance energy transfer (FRET) experiments both in solution and on surface-anchored origamis. Due to the modularity of DNA nanotechnology, DNA double strands with different complementary overhang lengths can be studied using the same DNA origami design. We show that the T4 DNA ligase repairs sticky ends more efficiently than blunt ends and that the ligation efficiency is influenced by both DNA sequence and the incubation conditions. Before ligation, dynamic fluctuations of the FRET signal are observed due to transient binding of the sticky overhangs. After ligation, the FRET signal becomes static. Thus, we can directly monitor the ligation reaction through the transition from dynamic to static FRET signals. Finally, we revert the ligation process using a restriction enzyme digestion and religate the resulting blunt ends. The here-presented DNA origami platform is thus suited to study complex multistep reactions occurring over several cycles of enzymatic treatment.

Sequence-Dependent Three Interaction Site Model for Single- and Double-Stranded DNA

We develop a robust coarse-grained model for single- and double-stranded DNA by representing each nucleotide by three interaction sites (TIS) located at the centers of mass of sugar, phosphate, and base. The resulting TIS model includes base-stacking, hydrogen bond, and electrostatic interactions as well as bond-stretching and bond angle potentials that account for the polymeric nature of DNA. The choices of force constants for stretching and the bending potentials were guided by a Boltzmann inversion procedure using a large representative set of DNA structures extracted from the Protein Data Bank. Some of the parameters in the stacking interactions were calculated using a learning procedure, which ensured that the experimentally measured melting temperatures of dimers are faithfully reproduced. Without any further adjustments, the calculations based on the TIS model reproduce the experimentally measured salt and sequence-dependence of the size of single-stranded DNA (ssDNA), as well as the persistence lengths of poly(dA) and poly(dT) chains. Interestingly, upon application of mechanical force, the extension of poly(dA) exhibits a plateau, which we trace to the formation of stacked helical domains. In contrast, the force-extension curve (FEC) of poly(dT) is entropic in origin and could be described by a standard polymer model. We also show that the persistence length of double-stranded DNA, formed from two complementary ssDNAs, is consistent with the prediction based on the worm-like chain. The persistence length, which decreases with increasing salt concentration, is in accord with the Odijk-Skolnick-Fixman theory intended for stiff polyelectrolyte chains near the rod limit. Our model predicts the melting temperatures of DNA hairpins with excellent accuracy, and we are able to recover the experimentally known sequence-specific trends. The range of applications, which did not require adjusting any parameter after the initial construction based solely on PDB structures and melting profiles of dimers, attests to the transferability and robustness of the TIS model for ssDNA and dsDNA.

Conformational Dynamics of Mechanically Compliant DNA Nanostructures from Coarse-Grained Molecular Dynamics Simulations

Structural DNA nanotechnology, the assembly of rigid 3D structures of complex yet precise geometries, has recently been used to design dynamic, mechanically compliant nanostructures with tunable equilibrium conformations and conformational distributions. Here we use coarse-grained molecular dynamics simulations to provide insights into the conformational dynamics of a set of mechanically compliant DNA nanostructures-DNA hinges that use single-stranded DNA "springs" to tune the equilibrium conformation of a layered double-stranded DNA "joint" connecting two stiff "arms" constructed from DNA helix bundles. The simulations reproduce the experimentally measured equilibrium angles between hinge arms for a range of hinge designs. The hinges are found to be structurally stable, except for some fraying of the open ends of the DNA helices comprising the hinge arms and some loss of base-pairing interactions in the joint regions coinciding with the crossover junctions, especially in hinges designed to exhibit a small bending angle that exhibit large local stresses resulting in strong kinks in their joints. Principal component analysis reveals that while the hinge dynamics are dominated by bending motion, some twisting and sliding of hinge arms relative to each other also exists. Forced deformation of the hinges reveals distinct bending mechanisms for hinges with short, inextensible springs versus those with longer, more extensible springs. Lastly, we introduce an approach for rapidly predicting equilibrium hinge angles from individual force-deformation behaviors of its single- and double-stranded DNA components. Taken together, these results demonstrate that coarse-grained modeling is a promising approach for designing, predicting, and studying the dynamics of compliant DNA nanostructures, where conformational fluctuations become important, multiple deformation mechanisms exist, and continuum approaches may not yield accurate properties.

A comparative study of coarse-graining methods for polymeric fluids: Mori-Zwanzig vs. iterative Boltzmann inversion vs. stochastic parametric optimization

We construct effective coarse-grained (CG) models for polymeric fluids by employing two coarse-graining strategies. The first one is a forward-coarse-graining procedure by the Mori-Zwanzig (MZ) projection while the other one applies a reverse-coarse-graining procedure, such as the iterative Boltzmann inversion (IBI) and the stochastic parametric optimization (SPO). More specifically, we perform molecular dynamics (MD) simulations of star polymer melts to provide the atomistic fields to be coarse-grained. Each molecule of a star polymer with internal degrees of freedom is coarsened into a single CG particle and the effective interactions between CG particles can be either evaluated directly from microscopic dynamics based on the MZ formalism, or obtained by the reverse methods, i.e., IBI and SPO. The forward procedure has no free parameters to tune and recovers the MD system faithfully. For the reverse procedure, we find that the parameters in CG models cannot be selected arbitrarily. If the free parameters are properly defined, the reverse CG procedure also yields an accurate effective potential. Moreover, we explain how an aggressive coarse-graining procedure introduces the many-body effect, which makes the pairwise potential invalid for the same system at densities away from the training point. From this work, general guidelines for coarse-graining of polymeric fluids can be drawn.