Self-Replication and Self-Assembly for Manufacturing

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing. The research presented here is intended to be a step towards this vision. We describe a computational simulation of nanoscale machines floating in a virtual liquid. The machines can bond together to form strands (chains) that self-replicate and self-assemble into user-specified meshes. There are four types of machines and the sequence of machine types in a strand determines the shape of the mesh they will build. A strand may be in an unfolded state, in which the bonds are straight, or in a folded state, in which the bond angles depend on the types of machines. By choosing the sequence of machine types in a strand, the user can specify a variety of polygonal shapes. A simulation typically begins with an initial unfolded seed strand in a soup of unbonded machines. The seed strand replicates by bonding with free machines in the soup. The child strands fold into the encoded polygonal shape, and then the polygons drift together and bond to form a mesh. We demonstrate that a variety of polygonal meshes can be manufactured in the simulation, by simply changing the sequence of machine types in the seed

Self-Replicating Strands that Self-Assemble into User-Specified Meshes

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing. The research presented here is intended to be a step towards this vision. In previous work (JohnnyVon 1.0), we simulated machines that bonded together to form self-replicating strands. There were two types of machines (called types 0 and 1), which enabled strands to encode arbitrary bit strings. However, the information encoded in the strands had no functional role in the simulation. The information was replicated without being interpreted, which was a significant limitation for potential manufacturing applications. In the current work (JohnnyVon 2.0), the information in a strand is interpreted as instructions for assembling a polygonal mesh. There are now four types of machines and the information encoded in a strand determines how it folds. A strand may be in an unfolded state, in which the bonds are straight (although they flex slightly due to virtual forces acting on the machines), or in a folded state, in which the bond angles depend on the types of machines. By choosing the sequence of machine types in a strand, the user can specify a variety of polygonal shapes. A simulation typically begins with an initial unfolded seed strand in a soup of unbonded machines. The seed strand replicates by bonding with free machines in the soup. The child strands fold into the encoded polygonal shape, and then the polygons drift together and bond to form a mesh. We demonstrate that a variety of polygonal meshes can be manufactured in the simulation, by simply changing the sequence of machine types in the seed

Broken scaling in the Forest Fire Model

We investigate the scaling behavior of the cluster size distribution in the Drossel-Schwabl Forest Fire model (DS-FFM) by means of large scale numerical simulations, partly on (massively) parallel machines. It turns out that simple scaling is clearly violated, as already pointed out by Grassberger [P. Grassberger, J. Phys. A: Math. Gen. 26, 2081 (1993)], but largely ignored in the literature. Most surprisingly the statistics not seems to be described by a universal scaling function, and the scale of the physically relevant region seems to be a constant. Our results strongly suggest that the DS-FFM is not critical in the sense of being free of characteristic scales.Comment: 9 pages in RevTEX4 format (9 figures), submitted to PR

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5_{2.5} concentration

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from characterizing ambient air pollution exposure solely using measurements. In the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at finer spatial and temporal scales (daily, usually) with complete coverage. Some of these methods include: geostatistical techniques, such as kriging; spatial statistical models that use the information contained in air quality model outputs (statistical downscaling models); linear regression modeling approaches that leverage the information in GIS covariates (land use regression); or machine learning methods that mine the information contained in relevant variables (neural network and deep learning approaches). Although some of these exposure modeling approaches have been used in several air pollution epidemiological studies, it is not clear how much the predicted exposures generated by these methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance and computational difficulty. Using PM$_{2.5}$ in year 2011 over the continental U.S. as case study, we examine the methods' performances across seasons, rural vs urban settings, and levels of PM$_{2.5}$ concentrations (low, medium, high)

Universal bound on the efficiency of molecular motors

The thermodynamic uncertainty relation provides an inequality relating any mean current, the associated dispersion and the entropy production rate for arbitrary non-equilibrium steady states. Applying it here to a general model of a molecular motor running against an external force or torque, we show that the thermodynamic efficiency of such motors is universally bounded by an expression involving only experimentally accessible quantities. For motors pulling cargo through a viscous fluid, a universal bound for the corresponding Stokes efficiency follows as a variant. A similar result holds if mechanical force is used to synthesize molecules of high chemical potential. Crucially, no knowledge of the detailed underlying mechano-chemical mechanism is required for applying these bounds.Comment: Invited contribution to proceedings of STATPHYS26, Lyo

Primordial Evolution in the Finitary Process Soup

A general and basic model of primordial evolution--a soup of reacting finitary and discrete processes--is employed to identify and analyze fundamental mechanisms that generate and maintain complex structures in prebiotic systems. The processes--$\epsilon$-machines as defined in computational mechanics--and their interaction networks both provide well defined notions of structure. This enables us to quantitatively demonstrate hierarchical self-organization in the soup in terms of complexity. We found that replicating processes evolve the strategy of successively building higher levels of organization by autocatalysis. Moreover, this is facilitated by local components that have low structural complexity, but high generality. In effect, the finitary process soup spontaneously evolves a selection pressure that favors such components. In light of the finitary process soup's generality, these results suggest a fundamental law of hierarchical systems: global complexity requires local simplicity.Comment: 7 pages, 10 figures; http://cse.ucdavis.edu/~cmg/compmech/pubs/pefps.ht

Large-scale photonic Ising machine by spatial light modulation

Quantum and classical physics can be used for mathematical computations that are hard to tackle by conventional electronics. Very recently, optical Ising machines have been demonstrated for computing the minima of spin Hamiltonians, paving the way to new ultra-fast hardware for machine learning. However, the proposed systems are either tricky to scale or involve a limited number of spins. We design and experimentally demonstrate a large-scale optical Ising machine based on a simple setup with a spatial light modulator. By encoding the spin variables in a binary phase modulation of the field, we show that light propagation can be tailored to minimize an Ising Hamiltonian with spin couplings set by input amplitude modulation and a feedback scheme. We realize configurations with thousands of spins that settle in the ground state in a low-temperature ferromagnetic-like phase with all-to-all and tunable pairwise interactions. Our results open the route to classical and quantum photonic Ising machines that exploit light spatial degrees of freedom for parallel processing of a vast number of spins with programmable couplings.Comment: https://journals.aps.org/prl/accepted/7007eYb7N091546c41ad4108828a97d5f92006df

Spinodal decomposition of off-critical quenches with a viscous phase using dissipative particle dynamics in two and three spatial dimensions

We investigate the domain growth and phase separation of hydrodynamically-correct binary immiscible fluids of differing viscosity as a function of minority phase concentration in both two and three spatial dimensions using dissipative particle dynamics. We also examine the behavior of equal-viscosity fluids and compare our results to similar lattice-gas simulations in two dimensions.Comment: 34 pages (11 figures); accepted for publication in Phys. Rev.