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Workshop Description. Interface dissipative phenomena take place across a wide spectrum of time and length scales, from atomistic processes, as in the gliding motion of a nanocluster or a nano-motor, to mesoscale nonequilibrium physics of colloidal suspensions and micro-swimmers, up to extreme macroscopic mechanisms, as in fault dynamics and earthquake events. Due to the ubiquitous nature of this kind of dissipative processes and the enormous practical relevance, friction-related problems have been investigated over the centuries.

Especially nowadays where controlling and reducing friction is increasingly important in nanotechnological device miniaturization, the physics of dissipative dynamics at interfaces is gaining impulse in nanoscale and mesoscale experiments, simulations, and theoretical modeling.

In this workshop we will focus on different methods to simulate interfacial dynamical processes responsible for energy dissipation. The purpose of the proposed workshop is to bring together researchers working on theory, simulation and experiments of dynamics and dissipation at interfaces, and to discuss whether it is possible to go beyond phenomenological approaches and thus go beyond the existing paradigm.

Canonical sampling through velocity rescaling.

Computational Methods for Complex Liquid-Fluid Interfaces

Fehr, K. Solid State Ionics. Kamaya, N. A lithium superionic conductor. Kanno, R. Maier, J.

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Ionic conduction in space charge regions. Solid State Chem. Pushing nanoionics to the limits: charge carrier chemistry in extremely small systems. Minami, T.

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Recent progress of glass and glass-ceramics as solid electrolytes for lithium secondary batteries. Momma, K. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. Ohta, N.

Enhancement of the high-rate capability of solid-state lithium batteries by nanoscale interfacial modification. Perdew, J. Generalized gradient approximation made simple. Sakuda, A. Interfacial observation between LiCoO 2 electrode and Li 2 S—P 2 S 5 solid electrolytes of all-solid state lithium secondary batteries using transmission electron microscopy. Sulfide solid electrolyte with favorable mechanical property for all-solid state lithium batteries. Seino, Y.

A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries. Energy Environ. Sumita, M. C , 14— Takada, K. Interfacial nanoarchitectonics for solid-state lithium batteries.

Homepage of NIMS GREEN Interface Computational Science Group

Langmuir 29, — Progress and prospective of solid-state lithium batteries. Acta Mater. Interfacial phenomena in solid-state lithium battery with sulfide solid electrolyte.

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  • Interfacial modification for high-power solid-state lithium batteries. Tarascon, J. Issues and challenges facing rechargeable lithium batteries. Nature , — Wang, L. First-principles study of surface properties of LiFePO 4 : surface energy, structure, wulff shape, and surface redox potential. B 76, Wilkening, M. Ultraslow Li diffusion in spinel-type structured Li 4 Ti 5 O 12 — a comparison of results from solid state nmr and impedance spectroscopy. Xu, X. Self-organized core—shell structure for high-power electrode in solid-state lithium batteries.

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    Synthetic design of crystalline inorganic chalcogenides exhibiting fast-ion conductivity. Zhu, Y. Origin of outstanding stability in the lithium solid electrolyte materials: insights from thermodynamic analyses based on first-principles calculations. ACS Appl. Interfaces 7, — Energy Res. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

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    For example, to model a part of the brain, this modeling approach starts with a detailed description of the relevant neuroanatomy, such as a description of the three-dimensional structure of the neuron and its dendritic tree. At the single-cell level, the model represents information about neuronal morphology, including such parameters as soma size, length of interbranch segments, diameter of branches, bifurcation probabilities, and density and size of dendritic spines. At the neuronal network level, the model represents the cell types found in the network and the connectivity among them.

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    The model must also incorporate information regarding the basic physiological behavior of the modeled structure—for example, by tuning the model to replicate neuronal responses to experimentally derived data. With such a framework in place, a structural model organizes data in ways that make manifestly obvious how those data are related to neural function. By contrast, for many other kinds of databases it is not at all obvious how the data contained therein contribute to an understanding of function.

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    Shankar, D. Beeman, and J. Ascoli, ed. As models become more capable, they extend our ability to explore the functional significance of the structure and organization of biological systems.