Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Nature utilizes the available resources to construct lightweight, strong and tough materials under constrained environmental conditions. The impact surface of the fast-striking dactyl club from the mantis shrimp is an example of one such composite material; the shrimp has evolved the capability to localize damage and avoid catastrophic failure from high-speed collisions during its feeding activities. Here we report that the dactyl club of mantis shrimps contains an impact-resistant coating composed of densely packed (about 88 per cent by volume) ~65-nm bicontinuous nanoparticles of hydroxyapatite integrated within an organic matrix. These mesocrystalline hydroxyapatite nanoparticles are assembled from small, highly aligned nanocrystals. Under impacts of high strain rates (around 104 s−1), particles rotate and translate, whereas the nanocrystalline networks fracture at low-angle grain boundaries, form dislocations and undergo amorphization. The interpenetrating organic network provides additional toughening, as well as substantial damping, with a loss coefficient of around 0.02. An unusual combination of stiffness and damping is therefore achieved, outperforming many engineered materials. A coating made from densely packed hydroxyapatite particles in an organic matrix endows the dactyl club of mantis shrimps with high stiffness and energy damping.

A natural impact-resistant bicontinuous composite nanoparticle coating

A critical review of the preparation strategies of thermally conductive and electrically insulating polymeric materials and their applications in heat dissipation of electronic devices

Materials chemistry is at the forefront of the global “Fourth Industrial Revolution”, in part by establishing a “Materials 4.0” paradigm. A key aspect of this paradigm is developing methods to effectively integrate hardware, software, and biological systems. Towards this end, we must have intimate knowledge of the virtual space in materials design: materials omics (materiomics), materials informatics, computational modelling and simulations, artificial intelligence (AI), and big data. We focus on the discovery and design of next-generation bio-inspired materials because the design space is so huge as to be almost intractable. With nature providing researchers with specific guiding principles, this material design space may be probed most efficiently through digital, high-throughput methods. Therefore, to enhance awareness and adoption of digital approaches in soft polymeric bio-inspired materials discovery and design, we detail multiscale simulation techniques in soft matter from the molecular level to the macroscale. We also highlight the unique role that artificial intelligence and materials databases will play in molecular simulations as well as soft materials discovery. Finally, we showcase several case studies that concretely apply computational modelling and simulations for integrative soft bio-inspired materials design with experiments.

Discovery and design of soft polymeric bio-inspired materials with multiscale simulations and artificial intelligence

In this article, we review thermal transport in polymers with different morphologies from aligned fibers to bulk amorphous states. We survey early and recent efforts in engineering polymers with high thermal conductivity by fabricating polymers with large-scale molecular alignments. The experimentally realized extremely high thermal conductivity of polymer nanofibers are highlighted, and understanding of thermal transport physics from molecular simulations are discussed. We then transition to the discussion of bulk amorphous polymers with an emphasize on the physics of thermal transport and its relation with the conformation of molecular chains in polymers. We also discuss the current understanding of how the chemistry of polymers would influence thermal transport in amorphous polymers and some limited, but important chemistry-structural-property relationships. Lastly, challenges, perspectives and outlook of this field are presented. We hope this review will inspire more fundamental and applied research in the polymer thermal transport field to advance scientific understanding and engineering applications.

Thermal Transport in Polymers: A Review

Although tremendous efforts have been devoted to enhance thermal conductivity in polymer fibers, correlation between the thermal-drawing conditions and the resulting chain alignment, crystallinity, and phonon transport properties have remained obscure. Using a carefully trained coarse-grained force field, we systematically interrogate the thermal-drawing conditions of bulk polyethylene samples using large-scale molecular dynamics simulations. An optimal combination of moderate drawing temperature and strain rate is found to achieve highest degrees of chain alignment, crystallinity, and the resulting thermal conductivity. Such combination is rationalized by competing effects in viscoelastic relaxation and condensed to the Deborah number, a predictive metric for the thermal-drawing protocols, showing a delicate balance between stress localizations and chain diffusions. Upon tensile deformation, the thermal conductivity of amorphous polyethylene is enhanced to 80% of the theoretical limit, that is, its pure crystalline counterpart. An effective-medium-theory model, based on the serial-parallel heat conducting nature of semicrystalline polymers, is developed here to predict the impacts from both chain alignment and crystallinity on thermal conductivity. The enhancement in thermal conductivity is mainly attributed to the increases in the intrinsic phonon mean free path and the longitudinal group velocity. This work provides fundamental insights into the polymer thermal-drawing process and establishes a complete process–structure–property relationship for enhanced phonon transport in all-organic electronic devices and efficiency of polymeric heat dissipaters.

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Tailored morphology and highly enhanced phonon transport in polymer fibers: a multiscale computational framework

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Tailored morphology and highly enhanced phonon transport in polymer fibers: a multiscale computational framework