541 research outputs found

    Ion Transport and the True Transference Number in Nonaqueous Polyelectrolyte Solutions for Lithium Ion Batteries.

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    Nonaqueous polyelectrolyte solutions have been recently proposed as high Li+ transference number electrolytes for lithium ion batteries. However, the atomistic phenomena governing ion diffusion and migration in polyelectrolytes are poorly understood, particularly in nonaqueous solvents. Here, the structural and transport properties of a model polyelectrolyte solution, poly(allyl glycidyl ether-lithium sulfonate) in dimethyl sulfoxide, are studied using all-atom molecular dynamics simulations. We find that the static structural analysis of Li+ ion pairing is insufficient to fully explain the overall conductivity trend, necessitating a dynamic analysis of the diffusion mechanism, in which we observe a shift from largely vehicular transport to more structural diffusion as the Li+ concentration increases. Furthermore, we demonstrate that despite the significantly higher diffusion coefficient of the lithium ion, the negatively charged polyion is responsible for the majority of the solution conductivity at all concentrations, corresponding to Li+ transference numbers much lower than previously estimated experimentally. We quantify the ion-ion correlations unique to polyelectrolyte systems that are responsible for this surprising behavior. These results highlight the need to reconsider the approximations typically made for transport in polyelectrolyte solutions

    Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

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    In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to obtain a sound and ({\delta}-)complete decision procedure. Our method also employs a data-driven approach to learn a verification policy that guides abstract interpretation during proof search. We have implemented the proposed approach in a tool called Charon and experimentally evaluated it on hundreds of benchmarks. Our experiments show that the proposed approach significantly outperforms three state-of-the-art tools, namely AI^2 , Reluplex, and Reluval
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