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

Polymer based photovoltaic devices promise solar technologies that are inexpensive enough to be widely exploited and therefore provide a significant fraction of the future energy needs. There are many promising polymer-fullerene mixtures that are promising materials candidates for achieving high performance devices, but their exploitation requires and improved understanding of their structure-property relationships. Of particular relevance is the phase behavior of the mixtures. The phase behavior of donor-acceptor materials for photovoltaic applications is of key importance [1,2]: i) to gain a fundamental understanding and control of morphology development in the donor-acceptor blends; ii) to appropriately choose the operating window for thermal annealing; iii) to understand the long-term stability of the blended film morphology and consequently of the photovoltaic performance of the corresponding solar cells. In this work the phase behavior of polymer-fullerene mixtures is being studied using Differential Scanning Calorimetry (DSC), Wide-Angle X-Ray Scattering (WAXS), Small-Angle Neutron Scattering (SANS) and theoretical ab initio Density Functional Theory (DFT) calculations.

/pdf/thermodynamic-effects-in-morphological-evolution-of-polymer-3s5g51f80q.pdf

Thermodynamic effects in morphological evolution of polymer-fullerene nanocomposites for photovoltaic applications

Using first-principles calculations we model the out-of-plane switching of local dipoles in CuInP$_2$S$_6$ (CIPS) that are largely induced by Cu off-centering. Previously, a coherent switching of polarization via a quadruple-well potential was proposed for these materials. In the super-cells we considered, we find multiple structures with similar energies but with different local polar order. Our results suggest that the individual dipoles are weakly coupled in-plane and under an electric field at very low temperatures these dipoles in CIPS should undergo incoherent disordered switching. The barrier for switching is determined by the single Cu-ion switching barrier. This in turn suggests a scale-free polarization with a switching barrier of $\sim$ 203.6-258.0 meV, a factor of five smaller than that of HfO$_2$ (1380 meV) a prototypical scale-free ferroelectric. The mechanism of polarization switching in CIPS is mediated by the switching of each weakly interacting dipole rather than the macroscopic polarization itself as previously hypothesized. These findings reconcile prior observations of a quadruple well with sloping hysteresis loops, large ionic conductivity even at 250~K well below the Curie temperature (315~K), and a significant wake-up effects where the macroscopic polarization is slow to order and set-in under an applied electric field. We also find that computed piezoelectric response and the polarization show a linear dependence on the local dipolar order. This is consistent with having scale-free polarization and other polarization-dependent properties and opens doors for engineering tunable metastability by-design in CIPS (and related family of materials) for neuromorphic applications.

Scale-free switching of polarization in the layered ferroelectric material CuInP$_2$S$_6$

Electron and scanning probe microscopies have become one of the primary techniques to investigate systems in the domain of physical and life sciences at the atomic to mesoscopic length scales [1-5]. From the perspective of computational simulations performed at different length scales, there has also been significant advancements with accessibility to faster, parallelizable CPU/GPU architectures. This means there is a heap of information from the simulations performed on a variety of materials, particularly investigating corresponding structures and functionalities. Hence, structural, and spectral data resulting from these experiments along with simulations create an enormous exploratory platform to utilize, design deep learning (DL), machine learning (ML) workflows for feature finding along with atomic manipulation. However, systematic studies combining all three avenues to go beyond comparing endpoint properties, performing DL/ML analyses on static data, assessing structure-property relationships are still in its infancy.

Finding Features from Microscopes to Simulations Via Ensemble Learning and Atomic Manipulation

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remains a challenge even as high-throughput scanning tunneling electron microscopy (STEM) methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings, and potentially chart paths forward for automated discovery of materials defect phases in experiments.

Bridging Theory with Experiment: Digital Twins and Deep Learning Segmentation of Defects in Monolayer MX2 Phases

Present day semiconductor devices are rapidly approaching their physical limits, prompting an increasing number of researchers across multiple disciplines to attempt devising innovative ways for decreasing the size and increasing the performance of critical features in microelectronic circuits. One possible route is based on the idea of using molecules and molecular structures as functional electronic devices. Carbon nanotubes may provide one of the best materials for molecular electronic devices as they present a flexible and well structured architecture. However, practical realizations of new nanotube-based electronic devices hinge on a number of outstanding problems, such as the capability of achieving large-scale air-stable and controlled doping. Amphoteric doping by encapsulating suitable organic molecules inside of nanotubes may hold tremendous promise in this respect. In order to investigate and optimize the electronic transport properties in carbon nanotubes doped with organic molecules we have performed large-scale quantum electronic structure calculations coupled with a Green's function formulation for determining the conductance. By implementing this hybrid computational approach for examination of the electronic properties of molecular-based structures, an efficient and accurate procedure has been demonstrated for studying the effects of amphoteric doping of carbon nanotubes. With this method, a computational framework for the optimal design ofmore » nanotube based electronic devices is becoming routinely accessible. Results from our calculations suggest that the electronic structure of a carbon nanotube can be easily manipulated by encapsulating appropriate organic molecules leading to charge transfer processes that induce efficient n- and p-type doping of the carbon nanotube. Even though a molecule may cause n- or p-doping, we have found it to generally have minor effects on the transport properties of the nanotube as compared to a pristine tube.« less

Bobby G. Sumpter

Papers

Thermodynamic effects in morphological evolution of polymer-fullerene nanocomposites for photovoltaic applications

Scale-free switching of polarization in the layered ferroelectric material CuInP$_2$S$_6$

Finding Features from Microscopes to Simulations Via Ensemble Learning and Atomic Manipulation

Bridging Theory with Experiment: Digital Twins and Deep Learning Segmentation of Defects in Monolayer MX2 Phases

Optimizing the Electronic Properties of Carbon Nanotubes using Amphoteric Doping