Combining non-equilibrium simulations and coarse-grained modelling allows for a fine-grained decomposition of solvation dynamics

2016 ◽  
Vol 18 (45) ◽  
pp. 30954-30960 ◽  
Author(s):  
Michael Schmollngruber ◽  
Daniel Braun ◽  
Othmar Steinhauser
Keyword(s):  
Ionic Liquids ◽  
Stokes Shift ◽  
Time Dependent ◽  
Coarse Grained ◽  
Solvation Dynamics ◽  
Fine Grained ◽  
Non Equilibrium

The time-dependent Stokes shift is shown to be a localized and short-ranged effect in ionic liquids.

scholarly journals Time-Resolved Probing of the Nonequilibrium Structural Solvation Dynamics by the Time-Dependent Stokes Shift

2020 ◽  
Vol 124 (27) ◽  
pp. 5717-5722
Author(s):  
Henning Kirchberg ◽  
Michael Thorwart
Keyword(s):  
Stokes Shift ◽  
Time Dependent ◽  
Solvation Dynamics ◽  
Time Resolved

The Danger of Self-Organizing Structures in Materials Subjected to Dynamical Non-Equilibrium Processes

2013 ◽  
Vol 577-578 ◽  
pp. 525-528 ◽  
Author(s):  
Volodymyr Hutsaylyuk ◽  
Lucjan Śnieżek ◽  
Mykola Czausow ◽  
Valentin Berezin ◽  
Andriy Pylypenko
Keyword(s):  
Critical Parameter ◽  
Coarse Grained ◽  
Self Organization ◽  
Initial Structure ◽  
Fine Grained ◽  
Nano Structures ◽  
Equilibrium Processes ◽  
Properties Of Materials ◽  
Aluminum Alloy 2024 ◽  
Non Equilibrium

Researched the effect of DNP on mechanical properties of materials with a coarse-grained and fine-grained initial structure of aluminum alloy 2024 - T3, D16 and nano-crystallite titanium VT1-0. It has been shown that self-organization of structures at dynamic non-equilibrium processes is a critical parameter for materials with a nano-structures, since it significantly reduces the strength at the subsequent loading.

scholarly journals Solvation dynamics in polar solvents and imidazolium ionic liquids: failure of linear response approximations

2018 ◽  
Vol 20 (7) ◽  
pp. 5246-5255 ◽  
Author(s):  
Esther Heid ◽  
Christian Schröder
Keyword(s):  
Ionic Liquids ◽  
Linear Response ◽  
Large Scale ◽  
Linear Response Theory ◽  
Time Dependent ◽  
Solvation Dynamics ◽  
Polar Solvents ◽  
Imidazolium Ionic Liquids ◽  
Fluorescence Measurements ◽  
Solvent Systems

Large scale computer simulations of different fluorophore-solvent systems reveal when and why linear response theory applies to time-dependent fluorescence measurements.

scholarly journals To unravel the connection between the non-equilibrium and equilibrium solvation dynamics of tryptophan: success and failure of the linear response theory of fluorescence Stokes shift

RSC Advances ◽  
2020 ◽  
Vol 10 (31) ◽  
pp. 18348-18354
Author(s):  
Xiaofang Wang ◽  
Jirui Guo ◽  
Tanping Li ◽  
Zhiyi Wei
Keyword(s):  
Excited State ◽  
Linear Response ◽  
Stokes Shift ◽  
Linear Response Theory ◽  
Solvation Dynamics ◽  
Response Theory ◽  
Gaussian Statistics ◽  
Non Equilibrium

Success and failure of linear response theory for Stokes shift correlate with the excited state Gaussian statistics.

scholarly journals Solvation dynamics: improved reproduction of the time-dependent Stokes shift with polarizable empirical force field chromophore models

2019 ◽  
Vol 21 (32) ◽  
pp. 17703-17710 ◽  
Author(s):  
Esther Heid ◽  
Stella Schmode ◽  
Payal Chatterjee ◽  
Alexander D. MacKerell ◽  
Christian Schröder
Keyword(s):  
Force Field ◽  
Stokes Shift ◽  
Time Dependent ◽  
Solvation Dynamics ◽  
Empirical Force Field ◽  
Induced Dipoles

The inclusion of polarizability slows down the computed solvation dynamics due to interactions of induced dipoles, improving agreement to experiment.

Solvation Dynamics in Room Temperature Ionic Liquids: Dynamics Stokes Shift Studies of Fluorescence of Dipolar Molecules

ChemInform ◽  
2006 ◽  
Vol 37 (35) ◽  
Author(s):  
Prasun K. Mandal ◽  
Satyen Saha ◽  
Rana Karmakar ◽  
Anunay Samanta
Keyword(s):  
Ionic Liquids ◽  
Room Temperature ◽  
Stokes Shift ◽  
Room Temperature Ionic Liquids ◽  
Solvation Dynamics ◽  
Dipolar Molecules

Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

1990 ◽  
Vol 48 (4) ◽  
pp. 928-929
Author(s):  
Wang Zheng-fang ◽  
Z.F. Wang
Keyword(s):  
Stainless Steel ◽  
Thermal Cycle ◽  
Secondary Phase ◽  
Corrosion Cracking ◽  
Coarse Grained ◽  
Test Environment ◽  
Fine Grained ◽  
Evaluation Test ◽  
Welding Thermal Cycle ◽  
Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

scholarly journals Balanced Sparsity for Efficient DNN Inference on GPU

2019 ◽  
Vol 33 ◽  
pp. 5676-5683 ◽  
Author(s):  
Zhuliang Yao ◽  
Shijie Cao ◽  
Wencong Xiao ◽  
Chen Zhang ◽  
Lanshun Nie
Keyword(s):  
Deep Neural Networks ◽  
General Purpose ◽  
Coarse Grained ◽  
Efficient Computation ◽  
Model Accuracy ◽  
Sparse Model ◽  
Model Inference ◽  
Fine Grained ◽  
Practical Inference ◽  
Speed Up

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.

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