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Poly(N-isopropylacrylamide) (PNIPAm) is a smart polymer that presents a lower critical transition temperature (LCST) of 305 K. Interestingly, this transition point falls within the range of the human body temperature, making PNIPAma highly suitable candidate for bio-medical applications.However, it is sometimes desirable to have a rather flexible tuning of the LCST of these polymers to further increase their range of applications. In this work, we use all-atom molecular dynamics simulations to study the LCST of PNIPAm-based (co-)polymers. We study different molecular architectures where the polymer sequences are tuned either by modifying its stereochemistry or by the co-polymerization of PNIPAm with acrylamide (Am) units. Our analysis connects global polymer conformations with the microscopic intermolecular interactions. These findings suggest that the collapse of a PNIPAm chain upon heating is dependent on the hydration structure around the monomers, which is strongly dependent on the tacticity and the presence of more hydrophilic acrylamide monomers. Our results are found to be in good agreement with the existing experimental data. [ABSTRACT FROM AUTHOR]

Nucleic acids are characterized by a complex hierarchical structure and a variety of interaction mechanisms with other molecules. These features suggest the need of multiscale simulation methods in order to grasp the relevant physical properties of deoxyribonucleic acid (DNA) and RNA using in silico experiments. Here we report an implementation of a dual-resolution modeling of a DNA oligonucleotide in physiological conditions; in the presented setup only the nucleotide molecule and the solvent and ions in its proximity are described at the atomistic level; in contrast, the water molecules and ions far from the DNA are represented as computationally less expensive coarse-grained particles. Through the analysis of several structural and dynamical parameters, we show that this setup reliably reproduces the physical properties of the DNA molecule as observed in reference atomistic simulations. These results represent a first step towards a realistic multiscale modeling of nucleic acids and provide a quantitatively solid ground for their simulation using dual-resolution methods. [ABSTRACT FROM AUTHOR]

Self Consistent Field (SCF) theory serves as an efficient tool for studying mesoscale structure and thermodynamics of polymeric liquid crystals (LC). We investigate how some of the intrinsic approximations of SCF affect the description of the thermodynamics of polymeric LC, using a coarse-grained model. Polymer nematics are represented as discrete worm-like chains (WLC) where non-bonded interactions are defined combining an isotropic repulsive and an anisotropic attractive Maier-Saupe (MS) potential. The range of the potentials, σ, controls the strength of correlations due to non-bonded interactions. Increasing σ (which can be seen as an increase of coarse-graining) while preserving the integrated strength of the potentials reduces correlations. The model is studied with particle-based Monte Carlo (MC) simulations and SCF theory which uses partial enumeration to describe discrete WLC. In MC simulations the Helmholtz free energy is calculated as a function of strength of MS interactions to obtain reference thermodynamic data. To calculate the free energy of the nematic branch with respect to the disordered melt, we employ a special thermodynamic integration (TI) scheme invoking an external field to bypass the first-order isotropic-nematic transition. Methodological aspects which have not been discussed in earlier implementations of the TI to LC are considered. Special attention is given to the rotational Goldstone mode. The free-energy landscape in MC and SCF is directly compared. For moderate σ the differences highlight the importance of local non-bonded orientation correlations between segments, which SCF neglects. Simple renormalization of parameters in SCF cannot compensate the missing correlations. Increasing σ reduces correlations and SCF reproduces well the free energy in MC simulations. [ABSTRACT FROM AUTHOR]

We present a coarse-graining strategy that we test for aqueous mixtures. The method uses pair-wise cumulative coordination as a target function within an iterative Boltzmann inversion (IBI) like protocol. We name this method coordination iterative Boltzmann inversion (C-IBI). While the underlying coarse-grained model is still structure based and, thus, preserves pair-wise solution structure, our method also reproduces solvation thermodynamics of binary and/or ternary mixtures. Additionally, we observe much faster convergence within C-IBI compared to IBI. To validate the robustness, we apply C-IBI to study test cases of solvation thermodynamics of aqueous urea and a triglycine solvation in aqueous urea. [ABSTRACT FROM AUTHOR]

Molecular simulations can provide microscopic insight into the physical and chemical driving forces of complex molecular processes. Despite continued advancement of simulation methodology, model errors may lead to inconsistencies between simulated and reference (e.g., from experiments or higher-level simulations) observables. To bound the microscopic information generated by computer simulations within reference measurements, we propose a method that reweights the microscopic transitions of the system to improve consistency with a set of coarse kinetic observables. The method employs the well-developed Markov state modeling framework to efficiently link microscopic dynamics with long-time scale constraints, thereby consistently addressing a wide range of time scales. To emphasize the robustness of the method, we consider two distinct coarse-grained models with significant kinetic inconsistencies. When applied to the simulated conformational dynamics of small peptides, the reweighting procedure systematically improves the time scale separation of the slowest processes. Additionally, constraining the forward and backward rates between metastable states leads to slight improvement of their relative stabilities and, thus, refined equilibrium properties of the resulting model. Finally, we find that difficulties in simultaneously describing both the simulated data and the provided constraints can help identify specific limitations of the underlying simulation approach. [ABSTRACT FROM AUTHOR]

We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space. [ABSTRACT FROM AUTHOR]

Mesoscale behavior of polymers is frequently described by universal laws. This physical property motivates us to propose a new modeling concept, grouping polymers into classes with a common long-wavelength representation. In the same class, samples of different materials can be generated from this representation, encoded in a single library system. We focus on homopolymer melts, grouped according to the invariant degree of polymerization. They are described with a bead-spring model, varying chain stiffness and density to mimic chemical diversity. In a renormalization group-like fashion, library samples provide a universal blob-based description, hierarchically backmapped to create configurations of other class-members. Thus, large systems with experimentally relevant invariant degree of polymerizations (so far accessible only on very coarse-grained level) can be microscopically described. Equilibration is verified comparing conformations and melt structure with smaller scale conventional simulations. [ABSTRACT FROM AUTHOR]

A fully atomistic modelling of many biophysical and biochemical processes at biologically relevant length- and time scales is beyond our reach with current computational resources, and one approach to overcome this difficulty is the use of multiscale simulation techniques. In such simulations, when system properties necessitate a boundary between resolutions that falls within the solvent region, one can use an approach such as the Adaptive Resolution Scheme (AdResS), in which solvent particles change their resolution on the fly during the simulation. Here, we apply the existing AdResS methodology to biomolecular systems, simulating a fully atomistic protein with an atomistic hydration shell, solvated in a coarse-grained particle reservoir and heat bath. Using as a test case an aqueous solution of the regulatory protein ubiquitin, we first confirm the validity of the AdResS approach for such systems, via an examination of protein and solvent structural and dynamical properties. We then demonstrate how, in addition to providing a computational speedup, such a multiscale AdResS approach can yield otherwise inaccessible physical insights into biomolecular function. We use our methodology to show that protein structure and dynamics can still be correctly modelled using only a few shells of atomistic water molecules. We also discuss aspects of the AdResS methodology peculiar to biomolecular simulations. [ABSTRACT FROM AUTHOR]

The functionality of many polymeric materials depends on their glass transition temperatures (Tg). In computer simulations, Tg is often calculated from the gradual change in macroscopic properties. Precise determination of this change depends on the fitting protocols. We previously proposed a robust data-driven approach to determine Tg from the molecular dynamics simulation data of a coarse-grained semiflexible polymer model. In contrast to the global macroscopic properties, our method relies on high-resolution microscopic details. Here, we demonstrate the generality of our approach by using various dimensionality reduction and clustering methods and apply it to an atomistic model of acrylic polymers. Our study reveals the explicit contribution of the side chain and backbone residues in influencing the determination of the glass transition temperature. [ABSTRACT FROM AUTHOR]

The utilization of solution‐processable organic semiconducting (OSC) polymers and the development of industrial‐relevant printing techniques enable cost‐efficient fabrication of optoelectronic devices for the mass market. Yet, the adaptation of viscoelastic properties of a functional ink to the respective printing technology is challenging. One crucial parameter is the formulation of the ink, which can be adjusted by selecting the combination of solvents that are mixed with the OSC. The current study considers model functional inks composed of a poly‐phenylene‐vinylene‐based OSC and two solvents, empirically known to be good. Their quality is quantified using the Hansen solubility parameters. The influence of the composition of the solvent mixture on structural, dynamical, and rheological behavior of the ink is investigated with light scattering, viscometry, and rheometry. Although both solvents are considered good, polymer aggregation is found at all compositions. Aggregation depends on composition in a nontrivial way. For dilute and semi‐dilute inks, the effects of aggregates on the ink viscosity are hidden by the difference in viscosities of the neat solvents. For elevated concentrations, the aggregates produce a hysteresis in the shear‐dependent viscosity, which should be considered when developing a functional ink for a particular printing technique. [ABSTRACT FROM AUTHOR]