Molecular Simulations of Controlled Polymer Crystallization in Polyethylene.

Multilamella polymer crystals are grown from the melt for the first time, in molecular dynamics simulations of a united-monomer model, with in excess of 1500000 united-monomers. Two-component systems comprised of equal weight fractions of 2000 united-monomer long chains and 200 united-monomer short chains are considered, with varying numbers of short butyl branches placed along the long chains. Utilizing two different cooling protocols, continuous-cooling and self-seeding, drastically different multilamella structures are revealed, which depend heavily on the branch content and crystallization protocol used. By self-seeding, well-aligned multilamella crystals are grown, which more clearly reveal the subtle alterations an increasing number of branches create on the size and shape of the crystallites in the early stages of spherulite formation. Under continuous cooling, this observation is almost completely obscured. At maximum thickness, chain portions as long as 100 united-monomers (200 carbons) are extended inside the crystalline lamella.

Ripple-like instability in the simulated gel phase of finite size phosphocholine bilayers.

Atomistic molecular dynamics simulations have reached a degree of maturity that makes it possible to investigate the lipid polymorphism of model bilayers over a wide range of temperatures. However if both the fluid Lα and tilted gel [Formula: see text] states are routinely obtained, the [Formula: see text] ripple phase of phosphatidylcholine lipid bilayers is still unsatifactorily described. Performing simulations of lipid bilayers made of different numbers of DPPC (1,2-dipalmitoylphosphatidylcholine) molecules ranging from 32 to 512, we demonstrate that the tilted gel phase [Formula: see text] expected below the pretransition cannot be obtained for large systems (equal or larger than 94 DPPC molecules) through common simulations settings or temperature treatments. Large systems are instead found in a disordered gel phase which display configurations, topography and energies reminiscent from the ripple phase [Formula: see text] observed between the pretransition and the main melting transition. We show how the state of the bilayers below the melting transition can be controlled and depends on thermal history and conditions of preparations. A mechanism for the observed topographic instability is suggested.

MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.

Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.

A machine learning study of the two states model for lipid bilayer phase transitions.

We have adapted a set of classification algorithms, also known as machine learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic molecular dynamics conformations in the low and high temperature phases as learning sets, the algorithm was trained to categorise individual lipid configurations as fluid or gel, in relation with the usual two-states phenomenological description of the lipid melting transition. We demonstrate that our machine can learn and sort lipids according to their most likely state without prior assumption regarding the nature of the order parameter of the transition. Results from our machine learning study provide strong support in favour of a two-states model approach of membrane fluidity.

The role of shape disorder in the collective behaviour of aligned fibrous matter.

We study the compression of bundles of aligned macroscopic fibers with intrinsic shape disorder, as found in human hair and in many other natural and man-made systems. We show by a combination of experiments, numerical simulations and theory how the statistical properties of the shapes of the fibers control the collective mechanical behaviour of the bundles. This work paves the way for designing aligned fibrous matter with purposed-designed properties from large numbers of individual strands of selected geometry and rigidity.

Topological energy storage of work generated by nanomotors.

Most macroscopic machines rely on wheels and gears. Yet, rigid gears are entirely impractical on the nano-scale. Here we propose a more useful method to couple any rotary engine to any other mechanical elements on the nano- and micro-scale. We argue that a rotary molecular motor attached to an entangled polymer energy storage unit, which together form what we call the "tanglotron" device, is a viable concept that can be experimentally implemented. We derive the torque-entanglement relationship for a tanglotron (its "equation of state") and show that it can be understood by simple statistical mechanics arguments. We find that a typical entanglement at low packing density costs around 6kT. In the high entanglement regime, the free energy diverges logarithmically close to a maximal geometric packing density. We outline several promising applications of the tanglotron idea and conclude that the transmission, storage and back-conversion of topological entanglement energy are not only physically feasible but also practical for a number of reasons.

Mechanical behavior of linear amorphous polymers: comparison between molecular dynamics and finite-element simulations.

This paper studies the rheology of weakly entangled polymer melts and films in the glassy domain and near the rubbery domain using two different methods: molecular dynamics (MD) and finite element (FE) simulations. In a first step, the uniaxial mechanical behavior of a bulk polymer sample is studied by means of particle-based MD simulations. The results are in good agreement with experimental data, and mechanical properties may be computed from the simulations. This uniaxial mechanical behavior is then implemented in FE simulations using an elasto-viscoelasto-viscoplastic constitutive law in a continuum mechanics (CM) approach. In a second step, the mechanical response of a polymer film during an indentation test is modeled with the MD method and with the FE simulations using the same constitutive law. Good agreement is found between the MD and CM results. This work provides evidence in favor of using MD simulations to investigate the local physics of contact mechanics, since the volume elements studied are representative and thus contain enough information about the microstructure of the polymer model, while surface phenomena (adhesion and surface tension) are naturally included in the MD approach.