We elucidate the microscopic mechanism of long-range proton conduction in poly[vinyl phosphonic acid] (PVPA), a highly promising proton conducting polymer. Using a steered ab initio molecular dynamics approach, we characterize the charge transport functionality of acid groups interacting with nonbulk water molecules intercalated in the polymer. Our results show that in PVPA, unlike in Nafion, water has a local vehicle/carrier function for excess protons. This function must however be combined with the Grotthuss-type conduction mechanism that is supplied by the acid groups in order to yield long-range charge transport. As an additional aspect, we find that contrary to common intuition, systems with disordered/amorphous morphology leave a considerably more pronounced functionality than well-ordered crystalline systems.
Amides and imides of alkali metals are a very promising class of materials for use as a hydrogen-storage system, as they are able to store and release hydrogen via a chemical route at controllable temperatures and pressures. We critically revise the present picture of the atomic structure of the lightest member (LiNH(2)/Li(2)NH) by using a combined computational and experimental approach. Specifically, ab initio path integral molecular dynamics simulations and solid-state (1)H NMR techniques are combined. The results show that the presently assumed local structure might be inconsistent or at least incomplete and needs considerable revision. In particular, the Li atoms turn out to be more mobile and more disordered than suggested by structural data obtained from X-ray scattering. Also, the configuration of the hydrogen atoms, which is accessible via the NMR experiment and the corresponding first-principles calculations, is different from the previously assumed data. The computed and experimentally observed (1)H NMR parameters are in very good mutual agreement and illustrate the unusual chemical environment of the hydrogen atoms in this system. Incorporating our results on the new lithium data, we show that the effect of nuclear quantum delocalization for the hydrogen atoms is considerably reduced compared to the perfect crystal structure.
Learning algorithms need generally the ability to compare several streams of information. Neural learning architectures hence need a unit, a comparator, able to compare several inputs encoding either internal or external information, for instance, predictions and sensory readings. Without the possibility of comparing the values of predictions to actual sensory inputs, reward evaluation and supervised learning would not be possible. Comparators are usually not implemented explicitly. Necessary comparisons are commonly performed by directly comparing the respective activities one-to-one. This implies that the characteristics of the two input streams (like size and encoding) must be provided at the time of designing the system. It is, however, plausible that biological comparators emerge from self-organizing, genetically encoded principles, which allow the system to adapt to the changes in the input and the organism. We propose an unsupervised neural circuitry, where the function of input comparison emerges via self-organization only from the interaction of the system with the respective inputs, without external influence or supervision. The proposed neural comparator adapts in an unsupervised form according to the correlations present in the input streams. The system consists of a multilayer feedforward neural network, which follows a local output minimization (anti-Hebbian) rule for adaptation of the synaptic weights. The local output minimization allows the circuit to autonomously acquire the capability of comparing the neural activities received from different neural populations, which may differ in population size and the neural encoding used. The comparator is able to compare objects never encountered before in the sensory input streams and evaluate a measure of their similarity even when differently encoded.
Free association is a task that requires a subject to express the first word to come to their mind when presented with a certain cue. It is a task which can be used to expose the basic mechanisms by which humans connect memories. In this work we have made use of a publicly available database of free associations to model the exploration of the averaged network of associations using a statistical and the ACT-R model. We performed, in addition, an online experiment asking participants to navigate the averaged network using their individual preferences for word associations. We have investigated the statistics of word repetitions in this guided association task. We find that the considered models mimic some of the statistical properties, viz the probability of word repetitions, the distance between repetitions and the distribution of association chain lengths, of the experiment, with the ACT-R model showing a particularly good fit to the experimental data for the more intricate properties as, for instance, the ratio of repetitions per length of association chains.
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