Rupture mechanism of liquid crystal thin films realized by large-scale molecular simulations.

The ability of liquid crystal (LC) molecules to respond to changes in their environment makes them an interesting candidate for thin film applications, particularly in bio-sensing, bio-mimicking devices, and optics. Yet the understanding of the (in)stability of this family of thin films has been limited by the inherent challenges encountered by experiment and continuum models. Using unprecedented large-scale molecular dynamics (MD) simulations, we address the rupture origin of LC thin films wetting a solid substrate at length scales similar to those in experiment. Our simulations show the key signatures of spinodal instability in isotropic and nematic films on top of thermal nucleation, and importantly, for the first time, evidence of a common rupture mechanism independent of initial thickness and LC orientational ordering. We further demonstrate that the primary driving force for rupture is closely related to the tendency of the LC mesogens to recover their local environment in the bulk state. Our study not only provides new insights into the rupture mechanism of liquid crystal films, but also sets the stage for future investigations of thin film systems using peta-scale molecular dynamics simulations.

New insights into the dynamics and morphology of P3HT:PCBM active layers in bulk heterojunctions.

Organic photovoltaics (OPVs) are a topic of extensive research because of their potential application in solar cells. Recent work has led to the development of a coarse-grained model for studying poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) blends using molecular simulations. Here we provide further validation of the force field and use it to study the thermal annealing process of P3HT:PCBM blends. A key finding of our study is that, in contrast to a previous report, the annealing process does not converge at the short time scales reported. Rather, we find that the self-assembly of the blends is characterized by three rate dependent stages that require much longer simulations to approach convergence. Using state-of-the-art high performance computing, we are able to study annealing at length and time scales commensurate with devices used in experiments. Our simulations show different phase segregated morphologies dependent on the P3HT chain length and PCBM volume fraction in the blend. For short chain lengths, we observed a smectic morphology containing alternate P3HT and PCBM domains. In contrast, a phase segregated morphology containing domains of P3HT and PCBM distributed randomly in space is found for longer chain lengths. Theoretical arguments justifying stabilization of these morphologies due to shape anisotropy of P3HT (rod-like) and PCBM (sphere-like) are presented. Furthermore, results on the structure factor, miscibility of P3HT and PCBM, domain spacing and kinetics of phase segregation in the blends are presented in detail. Qualitative comparison of these results with published small-angle neutron scattering experiments in the literature is presented and an excellent agreement is found.

Poly(3-hexylthiophene) Molecular Bottlebrushes via Ring-Opening Metathesis Polymerization: Macromolecular Architecture Enhanced Aggregation.

We report a facile synthetic strategy based on a grafting through approach to prepare well-defined molecular bottlebrushes composed of regioregular poly(3-hexylthiophene) (rr-P3HT) as the conjugated polymeric side chain. To this end, the exo-norbornenyl-functionalized P3HT macromonomer was synthesized by Kumada catalyst transfer polycondensation (KCTP) followed by postpolymerization modifications, and the resulting conjugated macromonomer was successfully polymerized by ring-opening metathesis polymerization (ROMP) in a controlled manner. The P3HT molecular bottlebrushes display an unprecedented strong physical aggregation upon drying during recovery, as verified by several analyses of the solution and solid states. This remarkably strong aggregation behavior is attributed to a significant enhancement in the number of π-π interactions between grafted P3HT side chains, brought about due to the bottlebrush architecture. This behavior is qualitatively supported by coarse-grained molecular dynamics simulations.

A Case Study of Truncated Electrostatics for Simulation of Polyelectrolyte Brushes on GPU Accelerators.

Numerous issues have disrupted the trend for increasing computational performance with faster CPU clock frequencies. In order to exploit the potential performance of new computers, it is becoming increasingly desirable to re-evaluate computational physics methods and models with an eye toward approaches that allow for increased concurrency and data locality. The evaluation of long-range Coulombic interactions is a common bottleneck for molecular dynamics simulations. Enhanced truncation approaches have been proposed as an alternative method and are particularly well-suited for many-core architectures and GPUs due to the inherent fine-grain parallelism that can be exploited. In this paper, we compare efficient truncation-based approximations to evaluation of electrostatic forces with the more traditional particle-particle particle-mesh (P(3)M) method for the molecular dynamics simulation of polyelectrolyte brush layers. We show that with the use of GPU accelerators, large parallel simulations using P(3)M can be greater than 3 times faster due to a reduction in the mesh-size required. Alternatively, using a truncation-based scheme can improve performance even further. This approach can be up to 3.9 times faster than GPU-accelerated P(3)M for many polymer systems and results in accurate calculation of shear velocities and disjoining pressures for brush layers. For configurations with highly nonuniform charge distributions, however, we find that it is more efficient to use P(3)M; for these systems, computationally efficient parametrizations of the truncation-based approach do not produce accurate counterion density profiles or brush morphologies.

Efficient hybrid evolutionary optimization of interatomic potential models.

The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to "rediscover" the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.

Liquid crystal nanodroplets in solution.

The aggregation of liquid crystal nanodroplets from a homogeneous solution is studied by molecular dynamics simulations. The liquid crystal particles are modeled as elongated ellipsoidal Gay-Berne particles while the solvent is modeled as spherical Lennard-Jones particles. Extending previous studies of Berardi et al. [J. Chem. Phys. 126, 044905 (2007)], we find that liquid crystal nanodroplets are not stable and that after sufficiently long times the nanodroplets always aggregate into a single large droplet. Results describing the droplet shape and orientation for different temperatures and shear rates are presented. The implementation of the Gay-Berne potential for biaxial ellipsoidal particles in a parallel molecular dynamics code is also briefly discussed.

Algorithmic dimensionality reduction for molecular structure analysis.

Dimensionality reduction approaches have been used to exploit the redundancy in a Cartesian coordinate representation of molecular motion by producing low-dimensional representations of molecular motion. This has been used to help visualize complex energy landscapes, to extend the time scales of simulation, and to improve the efficiency of optimization. Until recently, linear approaches for dimensionality reduction have been employed. Here, we investigate the efficacy of several automated algorithms for nonlinear dimensionality reduction for representation of trans, trans-1,2,4-trifluorocyclo-octane conformation--a molecule whose structure can be described on a 2-manifold in a Cartesian coordinate phase space. We describe an efficient approach for a deterministic enumeration of ring conformations. We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods. We discuss the use of dimensionality reduction algorithms for estimating intrinsic dimensionality and the relationship to the Whitney embedding theorem. Additionally, we investigate the influence of the choice of high-dimensional encoding on the reduction. We show for the case studied that, in terms of reconstruction error root mean square deviation, Cartesian coordinate representations and encodings based on interatom distances provide better performance than encodings based on a dihedral angle representation.

Efficient calculation of molecular properties from simulation using kernel molecular dynamics.

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure-property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.

Using product kernels to predict protein interactions.

There is a wide variety of experimental methods for the identification of protein interactions. This variety has in turn spurred the development of numerous different computational approaches for modeling and predicting protein interactions. These methods range from detailed structure-based methods capable of operating on only a single pair of proteins at a time to approximate statistical methods capable of making predictions on multiple proteomes simultaneously. In this chapter, we provide a brief discussion of the relative merits of different experimental and computational methods available for identifying protein interactions. Then we focus on the application of our particular (computational) method using Support Vector Machine product kernels. We describe our method in detail and discuss the application of the method for predicting protein-protein interactions, beta-strand interactions, and protein-chemical interactions.

Optimal neuronal tuning for finite stimulus spaces.

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.

Designing novel polymers with targeted properties using the signature molecular descriptor.

A method for solving the inverse quantitative structure-property relationship (QSPR) problem is presented which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat capacity, and density. We present novel QSPRs based on the signature molecular descriptor capable of predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene sebacamide) despite the fact that the polymer was used not used in the training of this model.

Prediction of beta-strand packing interactions using the signature product.

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].

Reverse engineering chemical structures from molecular descriptors: how many solutions?

Physical, chemical and biological properties are the ultimate information of interest for chemical compounds. Molecular descriptors that map structural information to activities and properties are obvious candidates for information sharing. In this paper, we consider the feasibility of using molecular descriptors to safely exchange chemical information in such a way that the original chemical structures cannot be reverse engineered. To investigate the safety of sharing such descriptors, we compute the degeneracy (the number of structure matching a descriptor value) of several 2D descriptors, and use various methods to search for and reverse engineer structures. We examine degeneracy in the entire chemical space taking descriptors values from the alkane isomer series and the PubChem database. We further use a stochastic search to retrieve structures matching specific topological index values. Finally, we investigate the safety of exchanging of fragmental descriptors using deterministic enumeration.

A deterministic algorithm for constrained enumeration of transmembrane protein folds.

A deterministic algorithm for enumeration of transmembrane protein folds is presented. Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space. The end result is a set of distinct protein conformations, which can be scored and refined as part of a process designed for computational elucidation of transmembrane protein structures.

Creating artificial binding pocket boundaries to improve the efficiency of flexible ligand docking.

Traditionally, algorithms for binding site characterization or identification focus on the problem of identifying atoms within a macromolecule that might be responsible for ligand binding. In this manuscript, we focus on the binding pocket problem from a different perspective as a challenge of calculating an artificial binding pocket boundary that is sufficient to isolate binding pocket volume. The approach involves the calculation of a macromolecule encapsulating surface (MES) that separates binding pocket volume from outside space. We show that the MES can be used to increase the efficiency of flexible docking as implemented in AutoDock 3.0. The most significant improvement in docking efficiency is seen when the entire protein is searched and results show additional support for the use of AutoDock, in and of itself, as a feasible tool for binding-site identification for cases in which a protein ligand is known.

Comparative structural analysis and kinetic properties of lactate dehydrogenases from the four species of human malarial parasites.

Parasite lactate dehydrogenase (pLDH) is a potential drug target for new antimalarials owing to parasite dependence on glycolysis for ATP production. The pLDH from all four species of human malarial parasites were cloned, expressed, and analyzed for structural and kinetic properties that might be exploited for drug development. pLDH from Plasmodium vivax, malariae, and ovale exhibit 90-92% identity to pLDH from Plasmodium falciparum. Catalytic residues are identical. Resides I250 and T246, conserved in most LDH, are replaced by proline in all pLDH. The pLDH contain the same five-amino acid insert (DKEWN) in the substrate specificity loops. Within the cofactor site, pLDH from P. falciparum and P. malariae are identical, while pLDH from P. vivax and P. ovale have one substitution. Homology modeling of pLDH from P. vivax, ovale, and malariae with the crystal structure of pLDH from P. falciparum gave nearly identical structures. Nevertheless, the kinetic properties and sensitivities to inhibitors targeted to the cofactor binding site differ significantly. Michaelis constants for pyruvate and lactate differ 8-9-fold; Michaelis constants for NADH, NAD(+), and the NAD(+) analogue 3-acetylpyridine adenine dinucleotide differ up to 4-fold. Dissociation constants for the inhibitors differ up to 21-fold. Molecular docking studies of the binding of the inhibitors to the cofactor sites of all four pLDH predict similar orientations, with the docked ligands positioned at the nicotinamide end of the cofactor site. pH studies indicate that inhibitor binding is independent of pH in the pH 6-8 range, suggesting that differences in dissociation constants for a specific inhibitor are not due to altered active site pK values among the four pLDH.