Organic Superalkalis with Closed-Shell Structure and Aromaticity

Benzene (C6H6) and polycyclic hydrocarbons such as naphthalene (C10H8), anthracene (C14H10) and coronene (C24H12) are well known aromatic organic compounds. We study the substitution of Li replacing all H atoms in these hydrocarbons using density functional method. The vertical ionization energy (VIE) of such lithiated species, i.e., C6Li6, C10Li8, C14Li10 and C24Li12 ranges 4.24-4.50 eV, which is lower than the IE of Li atom. Thus, these species may behave as superalkalis, due to their lower IE than alkali metal. However, these lithiated species possess planar and closed-shell structure, unlike typical superalkalis. Furthermore, all Li-substituted species are aromatic although their degree of aromaticity is reduced as compared to corresponding hydrocarbon analogues. We have further explored the structure of C6Li6 as star-like, unlike its inorganic analogue B3N3Li6, which appears as fan-like structure. We have also demonstrated that the interaction of C6Li6 with a superhalogen (such as BF4) is similar to that of a typical superalkali (such as OLi3). This may further suggest that the proposed lithiated species may form a new class of closed-shell organic superalkalis with aromaticity

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training

Generative Adversarial Networks (GANs) have become a widely popular framework for generative modelling of high-dimensional datasets. However their training is well-known to be difficult. This work presents a rigorous statistical analysis of GANs providing straight-forward explanations for common training pathologies such as vanishing gradients. Furthermore, it proposes a new training objective, Kernel GANs, and demonstrates its practical effectiveness on large-scale real-world data sets. A key element in the analysis is the distinction between training with respect to the (unknown) data distribution, and its empirical counterpart. To overcome issues in GAN training, we pursue the idea of smoothing the Jensen-Shannon Divergence (JSD) by incorporating noise in the input distributions of the discriminator. As we show, this effectively leads to an empirical version of the JSD in which the true and the generator densities are replaced by kernel density estimates, which leads to Kernel GANs

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data

We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians. We derive a variational-inference-based training objective for gradient-based learning. Additionally, we employ an inducing point approximation which scales inference to large data sets. Furthermore, we develop hybrid Bayesian neural networks that combine standard deep learning components with the proposed model to enable learning for unstructured data. We provide empirical evidence that our model outperforms the competitor methods with respect to both training time and accuracy in classification experiments on 68 structured and two unstructured data sets. Finally, we highlight the key capability of our model in yielding prediction uncertainty for classification by demonstrating its effectiveness in the tasks of large-scale active learning and detection of adversarial images

Alkalized Borazine: A Simple Recipe to Design Superalkali Species

We propose a simple yet effective route to the design of superalkalis, by successive alkali metal substitution in borazine (B3N3H6). Using Li atoms, our density functional calculations demonstrate that the vertical ionization energy (VIE) of B3N3H6-xLix decreases with the increase in x for x equals 1-6. For x equals 4, the VIE of B3N3H6-xLix becomes lower than that of Li atom, thereby indicating their superalkali nature. More interestingly, all these species are planar such that NICSzz value at the rings center is reduced. These novel superalkalis are expected to stimulate further interests in this field

Designing New Electrolytes for Lithium Ion Batteries Using Superhalogen Anions

The electrolytes used in Lithium Ion Batteries (LIBs) such as LiBF4, LiPF6 etc. are Li-salts of some complex anions, BF4-, PF6- etc. The investigation shows that the vertical detachment energy (VDE) of these anions exceeds to that of halogen, and therefore they behave as superhalogen anions. Consequently, it might be possible to design new electrolytic salts using other superhalogen anions. We have explored this possibility using Li-salts of various superhalogen anions such as BO2-, AlH4-, TiH5- and VH6- as well as hyperhalogen anions, BH4-y(BH4)y-(y = 1 to 4). Our density functional calculations show that Li-salts of these complex anions possess similar characteristics as those of electrolytic salts in LIBs. Note that they all are halogen free and hence, non-toxic and safer than LiBF4, LiPF6 etc. In particular, LiB4H13 and LiB5H16 are two potential candidates for electrolytic salt due to their smaller Li-dissociation energy ({\Delta}E) than those of LiBF4, LiPF6 etc. We have also noticed that {\Delta}E of LiBH4-y(BH4)y varies inversely with the VDE of BH4-y(BH4)y- anions, which increases with the increase in y. These findings may guide experimentalists and future researchers to design and synthesize more efficient and environment friendly electrolytic salts for LIBs

CO2-Activation and Enhanced Capture by C6Li6: A Density Functional Approach

The capture and storage of CO2, a major component of greenhouse gases, are crucial steps that can positively impact the global carbon balance. The capture of CO2 has been difficult due to its extremely high stability. In this study, we propose a simple and yet effective approach for capture and storage of CO2 by C6Li6. C6Li6 possesses a planar star-like structure, whose ionization energy is lower than that of Li atom and hence, it behaves as a superalkali. Superalkalis are unusual species possessing lower ionization energies than alkali atoms. We have systematically studied the interaction of successive CO2 molecules with C6Li6 using long-range dispersion corrected density functional {\omega}B97xD/6-311+G(d) calculations. We notice that these interactions lead to stable C6Li6-nCO2 complexes (n = 1-6) in which the structure of CO2 moieties is bent appreciably (122-125deg) due to electron transfer from C6Li6, whose planarity is distorted only slightly (less than or equal to 7 deg). This clearly suggests that the CO2 molecules can successfully be activated and captured by C6Li6. We have also analyzed bond-lengths and bond-angle of CO2, their charges and adsorption energy as a function of the number of adsorbed CO2 (n). It has been also noticed that the bond-length of CO2 in C6Li6-nCO2 complexes increases monotonically whereas adsorption energy decreases, ranging 3.18-2.79 eV per CO2 with the increase in n. These findings not only establish the potential of C6Li6 for capture and storage of CO2 molecules but also provide new insights into CO2-activation, capture, and storage by systems having low ionization energies

Possible Physical Realizations of the Tolman VII solution

The Tolman VII solution (an exact static spherically symmetric perfect fluid solution) to the Einstein equations is reexamined, and a closed form equation of state (EOS) is deduced for the first time. This EOS allows further analysis leading to a viable model for compact stars to be obtained. Explicit application of causality conditions places further constraints on the model, and recent measurements of masses and radii of neutron stars prove to be within the predictions of the model. It is found that the adiabatic index ${\gamma} \geq 2$, and self-bound crust solutions are not excluded. The solution is also shown to obey known stability criteria. It is argued that this solution provides realistic limits on models of compact stars.Comment: Accepted and published in PR

Reversals in infinite-Prandtl-number Rayleigh-B\'enard convection

Using direct numerical simulations, we study the statistical properties of reversals in two-dimensional Rayleigh-B\'enard convection for infinite Prandtl number. We find that the large-scale circulation reverses irregularly, with the waiting time between two consecutive genuine reversals exhibiting a Poisson distribution on long time scales, while the interval between successive crossings on short time scales shows a power law distribution. We observe that the vertical velocities near the sidewall and at the center show different statistical properties. The velocity near the sidewall shows a longer autocorrelation and $1/f^2$ power spectrum for a wide range of frequencies, compared to shorter autocorrelation and a narrower scaling range for the velocity at the center. The probability distribution of the velocity near the sidewall is bimodal, indicating a reversing velocity field. We also find that the dominant Fourier modes capture the dynamics at the sidewall and at the center very well. Moreover, we show a signature of weak intermittency in the fluctuations of velocity near the sidewall by computing temporal structure functions.Comment: 12 pages, 17 figure

Flow reversals in turbulent convection with free-slip walls

We perform numerical simulations of turbulent convection for infinite Prandtl number with free-slip walls, and study the dynamics of flow reversals. We show interesting correlations between the flow reversals and the nonlinear interactions among the large-scale flow structures represented by the modes $(1,1), (2,1), (3,1)$ and some others. After a flow reversal, the odd modes, e.g. $(1,1), (3,1)$, switch sign, but the even modes, e.g. $(2,2)$, retain their sign. The mixed modes $(1,2)$ and $(2,1)$ fluctuate around zero. Using the properties of the modes and their interactions, we show that they form a Klein four-group $Z_2 \times Z_2$. We also show that for the free-slip boundary condition, the corner rolls and vortex reconnection are absent during a flow reversal, in contrast to active role played by them in flow reversals for the no-slip boundary condition. We argue that the flow reversals with the no-slip and free-slip boundary conditions are different because they are induced by nonlinearities $({\bf u} \cdot \nabla) {\bf u}$ and $({\bf u} \cdot \nabla) \theta$ respectively.Comment: 25 pages, 13 figures, accepted in Physics of Fluid

Open-World Visual Recognition Using Knowledge Graphs

In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meaningful predictions for such inputs, we propose a two-step approach that utilizes information from knowledge graphs. First, a knowledge-graph representation is learned to embed a large set of entities into a semantic space. Second, an image representation is learned to embed images into the same space. Under this setup, we are able to predict structured properties in the form of relationship triples for any open-world image. This is true even when a set of labels has been omitted from the training protocols of both the knowledge graph and image embeddings. Furthermore, we append this learning framework with appropriate smoothness constraints and show how prior knowledge can be incorporated into the model. Both these improvements combined increase performance for visual recognition by a factor of six compared to our baseline. Finally, we propose a new, extended dataset which we use for experiments