There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Electrochemical capacitors, also called supercapacitors, store energy using either ion adsorption (electrochemical double layer capacitors) or fast surface redox reactions (pseudo-capacitors). They can complement or replace batteries in electrical energy storage and harvesting applications, when high power delivery or uptake is needed. A notable improvement in performance has been achieved through recent advances in understanding charge storage mechanisms and the development of advanced nanostructured materials. The discovery that ion desolvation occurs in pores smaller than the solvated ions has led to higher capacitance for electrochemical double layer capacitors using carbon electrodes with subnanometre pores, and opened the door to designing high-energy density devices using a variety of electrolytes. Combination of pseudo-capacitive nanomaterials, including oxides, nitrides and polymers, with the latest generation of nanostructured lithium electrodes has brought the energy density of electrochemical capacitors closer to that of batteries. The use of carbon nanotubes has further advanced micro-electrochemical capacitors, enabling flexible and adaptable devices to be made. Mathematical modelling and simulation will be the key to success in designing tomorrow's high-energy and high-power devices.

https://hal.archives-ouvertes.fr/hal-02417326/document

Materials for electrochemical capacitors

There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.

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Graphene and Graphene Oxide: Synthesis, Properties, and Applications

The capabilities of nanotechnology and computational chemistry are reaching a point of convergence. New computer hardware and novel computational methods have created opportunities to test proposed nanometer-scale devices, investigate molecular manufacturing and model and predict properties of new materials. Experimental methods are also beginning to provide new capabilities that make the possibility of manufacturing various devices with atomic precision tangible. In this paper, we will discuss some of the novel computational methods we have used in molecular dynamics simulations of polymer processes, neural network predictions of new materials, and simulations of proposed nano-bearings and fluid dynamics in nano- sized devices.

/pdf/advancing-manufacturing-through-computational-chemistry-2hgg5j0r7s.pdf

Advancing manufacturing through computational chemistry

Triboelectric charging strongly affects the operation cycle and handling of materials and can be used to harvest mechanical energy through triboelectric nanogenerator set-up. Despite ubiquity of triboelectric effects, a lot of mechanisms surrounding the relevant phenomena remain to be understood. Continued progress will rely on the development of rapid and reliable methods to probe accumulation and dynamics of static charges. Here, we demonstrate in-situ quantification of tribological charging with nanoscale resolution, that is applicable to a wide range of dielectric systems. We apply this method to differentiate between strongly and weakly charging compositions of industrial grade polymers. The method highlights the complex phenomena of electrostatic discharge upon contact formation to pre-charged surfaces, and directly reveals the mobility of electrostatic charge on the surface. Systematic characterization of commercial polyethylene terephthalate samples revealed the compositions with the best antistatic properties and provided an estimate of characteristic charge density up to 5x10-5 C/m2. Large-scale molecular dynamics simulations were used to resolve atomistic level structural and dynamical details revealing enrichment of oxygen containing groups near the air-interface where electrostatic charges are likely to accumulate.

Probing static discharge of polymer surfaces with nanoscale resolution

The classical rheological theories of entangled polymeric liquids are built upon two pillars: Gaussian statistics of entanglement strands and the assumption that the stress arises exclusively from the change of intramolecular configuration entropy. We show that these two hypotheses are not supported by molecular dynamics simulations of polymer melts. Specifically, the segment distribution functions at the entanglement length scale and below deviate considerably from the theoretical predictions, in both the equilibrium and deformed states. Further conformational analysis reveals that the intrachain entropic stress at the entanglement length scale is substantially smaller than the total stress, indicative of a considerable contribution from interchain entropy. Lastly, the relation between entanglement strand entropic stress and macroscopic stress exhibits a bifurcation behavior during deformation and stress relaxation, which cannot be accounted for by the classical theories.

Rethinking the Transient Network Concept in Entangled Polymer Rheology

The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. The key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Molecular dynamics simulations have been used to study the pyrolysis of eicosane (C2042 both in the gas phase and when confined to the interior of a (7,7) carbon nanotube. A reactive bond-order potential was used to model the thermal decomposition of covalent bonds. The unimolecular dissociation is first-order in both cases. The decomposition kinetics demonstrate Arrhenius temperature dependence, with similar activation barriers in both geometries. The decomposition rate is slower by approximately 30% in the confined system. This rate decrease is observed to be a result of recombination reactions due to collisions with the nanotube wall.

Bobby G. Sumpter

Papers

Advancing manufacturing through computational chemistry

Probing static discharge of polymer surfaces with nanoscale resolution

Rethinking the Transient Network Concept in Entangled Polymer Rheology

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Hydrocarbon Reactions in Carbon Nanotubes: Pyrolysis