Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks

Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experiments were performed on disordered nano-particle networks that have no predefined design. The material has been treated as a blackbox, and genetic algorithms have been used to find appropriate configuration voltages to enable the target functionality. In order to support future experiments, we developed simulation tools for predicting candidate functionalities. One of these tools is based on a physical model, but the one we introduce in this paper is based on an artificial neural network. The advantage of this newly presented approach is that, after training the neural network to match either the real material or its physical model, it can be configured using gradient descent instead of a black-box optimisation. The experiments we report here demonstrate that the neural network can model the simulated nano-material quite accurately. The differentiable, neural network-based material model is then used to find logic gates, as a proof of principle. This shows that the new approach has great potential for partly replacing costly and time-consuming experiments with the real materials. Therefore, this approach has a high relevance for future computing, either as an alternative to digital computing or as an alternative way of producing multi-functional reconfigurable devices

Challenges in Ceramic Science: A Report from the Workshop on Emerging Research Areas in Ceramic Science

In March 2012, a group of researchers met to discuss emerging topics in ceramic science and to identify grand challenges in the field. By the end of the workshop, the group reached a consensus on eight challenges for the future:—understanding rare events in ceramic microstructures, understanding the phase-like behavior of interfaces, predicting and controlling heterogeneous microstructures with unprecedented functionalities, controlling the properties of oxide electronics, understanding defects in the vicinity of interfaces, controlling ceramics far from equilibrium, accelerating the development of new ceramic materials, and harnessing order within disorder in glasses. This paper reports the outcomes of the workshop and provides descriptions of these challenges

Programmable interactions with biomimetic DNA linkers at fluid membranes and interfaces

At the heart of the structured architecture and complex dynamics of biological systems are specific and timely interactions operated by biomolecules. In many instances, biomolecular agents are spatially confined to flexible lipid membranes where, among other functions, they control cell adhesion, motility and tissue formation. Besides being central to several biological processes, \emph{multivalent interactions} mediated by reactive linkers confined to deformable substrates underpin the design of synthetic-biological platforms and advanced biomimetic materials. Here we review recent advances on the experimental study and theoretical modelling of a heterogeneous class of biomimetic systems in which synthetic linkers mediate multivalent interactions between fluid and deformable colloidal units, including lipid vesicles and emulsion droplets. Linkers are often prepared from synthetic DNA nanostructures, enabling full programmability of the thermodynamic and kinetic properties of their mutual interactions. The coupling of the statistical effects of multivalent interactions with substrate fluidity and deformability gives rise to a rich emerging phenomenology that, in the context of self-assembled soft materials, has been shown to produce exotic phase behaviour, stimuli-responsiveness, and kinetic programmability of the self-assembly process. Applications to (synthetic) biology will also be reviewed.Comment: 63 pages, revie

Review of Computational approaches for predicting the physicochemical and biological properties of nanoparticles

In the growing field of nanotechnology there is a need to determine the physicochemical and potential toxicological properties of nanomaterials since many industrial, medical and consumer applications are based on an understanding of these properties and on a controlled exposure to the materials. This document provides a literature review on the current status of computational studies aimed at predicting the physicochemical properties and biological effects (including toxicity) of nanomaterials, with an emphasis on medical applications. Although a number of models have been published for physicochemical property prediction, very few models have been published for predicting biological effects, toxicity or the underlying mechanisms of action. This is due to two main reasons: a) nanomaterials form a colloidal phase when in contact with biological systems making the definition and calculation of properties (descriptors) suitable for the prediction of toxicity a new and challenging task, and b) nanomaterials form a very heterogeneous class of materials, not only in terms of their chemical composition, but also in terms of size, shape, agglomeration state, and surface reactivity. There is thus an urgent need to extend the traditional structure-activity paradigm to develop methods for predicting the toxicity of nanomaterials, and to make the resulting models readily available. This document concludes by proposing some lines of research to fill the gap in knowledge and predictive methodologyJRC.I.6-Systems toxicolog

Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017

Molecular Dynamic Simulation of Structures and Interfaces in Amorphous/Ordered Composites.

This thesis describes molecular dynamics simulation studies of the structure-property relationships of molecular network systems, including inorganic and organic bulk amorphous systems, as well as two different amorphous polymers at the interface with ordered substrates. A series of soda lime silicate glasses were simulated, with up to 50% total modification and varying ratios of sodium and calcium. The clustering of cations and second-neighbor connectivity affect vibrational modes and the compressibility vs. pressure behavior. Mean-field theory is unable to account for mixed modifier effects in soda lime silicates. The structure and tensile behavior of a dynamically reacted bulk epoxy network were studied, demonstrating an improved polymerization method for continuously monitoring properties as a function of network growth, including volumetric shrinkage and internal stresses. A bifunctional epoxy resin is reacted with two aliphatic amines at room temperature, comparing simulation size, amine functionality, and stoichiometry. The elastic properties change by only 1-2 GPa during the growth of the network within the achieved degree of conversion. Tensile strength increases by ~100 MPa. Systems with surplus amine hardener reach higher degrees of epoxide conversion, but lag in formation of an infinite network. As a simple model system for amorphous/ordered interfaces, a thin alkane film was placed onto a metallic substrate. The ordered substrate creates a layered polymer configuration within the adjacent 10 Å, as shown by density profiles, pair correlation functions, and monomer orientation statistics. This structural change also affects the mechanical properties, as the elastic moduli of nanoconfined alkane systems are higher than would be expected for a simple laminate composite, based on extrapolating from the bulk properties of the two materials. Lastly, epoxy/carbon laminate systems were investigated, comparing different epoxy layer thicknesses and amine functionality. The cure and shrinkage behavior mimic the bulk epoxy, though the percolation of an infinite cluster is delayed. Post-annealed structures show a nearly uniform decrease in both the elastic modulus and tensile strength. Local heterogeneity is important in predicting nanoscale mechanics for all systems investigated. Larger system size provides better accuracy in determining mechanical properties of simulated highly cross-linked network polymers.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111417/1/kabeck_1.pd