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"How to evaluate de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple question has no clear answer. The absence of standardized guidelines challenges both the benchmarking of generative approaches and the selection of molecules for prospective studies. In this work, we take a fresh $- \textit{critical}$ and $\textit{constructive} -$ perspective on de novo design evaluation. We systematically investigate widely used evaluation metrics and expose key pitfalls ('traps') that were previously overlooked. In addition, we identify tools ('treasures') and strategies ('ways out') to navigate the complex 'jungle' of generative drug discovery, and strengthen the connections between the molecular and deep learning fields along the way. Our systematic and large-scale results are expected to provide a new lens for evaluating the de novo designs proposed by generative deep learning approaches.

Carbon biphenylene network (C-BPN), which is an ultra-thin material consisting of carbon atoms arranged in square-hexagonal-octagonal (4-6-8) periodic rings, has intriguing properties for nano-scale device design due to its unique crystal structure. Here, using the Landauer formalism in combination with first-principles calculations, we show that C-BPN is a highly stable thermoelectric material at elevated temperatures under mechanical strain, where its thermoelectric efficiency can be anisotropically engineered. Transport calculations reveal that C-BPN's transmission spectrum has significant degrees of directional anisotropy and it undergoes a metal-insulator transition under strain, which leads to an increase in its Seebeck coefficient. C-BPN's lattice thermal conductance can be selectively tuned up to 35% bidirectionally at room temperature by strain engineering. Enhancement in its power factor and the suppression of its lattice thermal conductance improves the p-type figure of merit up to 0.31 and 0.76 at 300 and 1000~K, respectively. Our findings reveal that C-BPN has high potency to be used in thermoelectric nano devices with selective anisotropic properties at elevated temperatures.

Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP learns from molecular string representations (e.g., Simplified Molecular Input Line Entry Systems [SMILES] and Self-Referencing Embedded Strings [SELFIES]) with methods akin to natural language processing. Despite their growing importance, training predictive CLP models is far from trivial, as it involves many 'bells and whistles'. Here, we analyze the key elements of CLP training, to provide guidelines for newcomers and experts alike. Our study spans three neural network architectures, two string representations, three embedding strategies, across ten bioactivity datasets, for both classification and regression purposes. This 'hitchhiker's guide' not only underscores the importance of certain methodological choices, but it also equips researchers with practical recommendations on ideal choices, e.g., in terms of neural network architectures, molecular representations, and hyperparameter optimization.

Question answering (QA) is the task of answering questions posed in natural language with free-form natural language answers extracted from a given passage. In the OpenQA variant, only a question text is given, and the system must retrieve relevant passages from an unstructured knowledge source and use them to provide answers, which is the case in the mainstream QA systems on the Web. QA systems currently are mostly limited to the English language due to the lack of large-scale labeled QA datasets in non-English languages. In this paper, we show that effective, low-cost OpenQA systems can be developed for low-resource contexts. The key ingredients are (1) weak supervision using machine-translated labeled datasets and (2) a relevant unstructured knowledge source in the target language context. Furthermore, we show that only a few hundred gold assessment examples are needed to reliably evaluate these systems. We apply our method to Turkish as a challenging case study, since English and Turkish are typologically very distinct and Turkish has limited resources for QA. We present SQuAD-TR, a machine translation of SQuAD2.0, and we build our OpenQA system by adapting ColBERT-QA and retraining it over Turkish resources and SQuAD-TR using two versions of Wikipedia dumps spanning two years. We obtain a performance improvement of 24-32% in the Exact Match (EM) score and 22-29% in the F1 score compared to the BM25-based and DPR-based baseline QA reader models. Our results show that SQuAD-TR makes OpenQA feasible for Turkish, which we hope encourages researchers to build OpenQA systems in other low-resource languages. We make all the code, models, and the dataset publicly available at https://github.com/boun-tabi/SQuAD-TR.

We study a massless real self-interacting scalar field $\varphi$ non-minimally coupled to Einstein gravity with torsion in (2+1) space-time dimensions in the presence of cosmological constant. The field equations with a self-interaction potential $V(\varphi)$ including $\varphi^{n}$ terms are derived by a variational principle. By numerically solving these field equations with the 4th Runge-Kutta method, the circularly symmetric rotating solutions for (2+1) dimensions Einstein gravity with torsion are obtained. Exact analytical solutions to the field equations are derived for the proposed metric in the absence of both torsion and angular momentum. We find that the self-interacting potential only exists for $n=6$. We also study the motion of massive and massless particles in (2+1) Einstein gravity with torsion coupled to a self-interacting scalar field. The effect of torsion on the behavior of the effective potentials of the particles is analyzed numerically., Comment: 17 pages, 25 figures

Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words (analogous to the words that make up sentences in human languages) and then apply advanced natural language processing techniques for tasks such as $\textit{de novo}$ drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. This study aims to investigate the chemical vocabularies generated by popular subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece, and Unigram, and identify key chemical words associated with protein-ligand binding. To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. Further, we conduct case studies on a number of protein families to analyze the impact of key chemical words on binding. Through our analysis, we find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our findings will help shed light on the chemistry captured by the chemical words, and by machine learning models for drug discovery at large., Comment: 16 pages, 11 figures, new computational analysis and extended case studies

Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases that may lead the model to learn dataset-specific patterns, instead of generalizable relationships. As a result, the prediction performance of models drops for previously unseen biomolecules, $\textit{i.e.}$ the prediction models cannot generalize to biomolecules outside of the dataset. The latest approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading prediction performance. Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models. DebiasedDTA reweights the training samples to mitigate the effect of dataset biases and is applicable to most DTA prediction models. The results suggest that models trained in the DebiasedDTA framework can achieve improved generalizability in predicting the interactions of the previously unseen biomolecules, as well as performance improvements on those previously seen. Extensive experiments with different biomolecule representations, model architectures, and datasets demonstrate that DebiasedDTA can upgrade DTA prediction models irrespective of the biomolecule representation, model architecture, and training dataset. Last but not least, we release DebiasedDTA as an open-source python library to enable other researchers to debias their own predictors and/or develop their own debiasing methods. We believe that this python library will corroborate and foster research to develop more generalizable DTA prediction models.

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/ ., Comment: EMNLP 2020 - COVID-19 Workshop

Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly., Comment: Accepted to EMNLP 2020

Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The S{\o}rensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine.

Quaternary CaO-MgO-Al2O3-SiO2 glasses are important constituents of the Earth's lower crust and mantle, and they also have important industrial applications such as in metallurgical processes, concrete production and emerging low-CO2 cement technologies. In particular, these applications rely heavily on the composition-structure-reactivity relationships for CMAS glasses, which are not yet well established. In this study, we developed a robust method that combines force-field molecular dynamics (MD) simulations and density functional theory (DFT) calculations with X-ray/neutron scattering experiments to resolve the atomic structure of a CMAS glass. The final structural representation generated using this method is not only thermodynamically favorable (according to DFT calculations) but also agrees with experiments (including X-ray/neutron scattering data as well as literature data). Detailed analysis of the final structure (including partial pair distribution functions, coordination number, oxygen environment) enabled existing discrepancies in the literature to be reconciled and has revealed new structural information on the CMAS glass, specifically, (i) the unambiguous assignment of medium-range atomic ordering, (ii) the preferential role of Ca atoms as charge compensators and Mg atoms as network modifiers, (iii) the proximity of Mg atoms to free oxygen sites, and (iv) clustering of Mg atoms. Overall, this new structural information will enhance our mechanistic understanding on CMAS glass dissolution behavior, including dissolution-related mechanisms occurring during the formation of low-CO2 cements.

Hybrid materials are crucial in photovoltaics where the overall efficiency of the heterostructure is closely related to the level of charge transfer at the interface. Here, using various metal / poly(3-hexylthiophene)(P3HT) heterostructure models, we reveal that the level of spontaneous charge transfer and electronic coupling at these interfaces depend on the conformational regularity of the organic polymer deposited on the metal substrate. Using ab-initio quantum chemical calculations based on density functional theory (DFT) and heterodyne vibrational sum frequency generation (HD-VSFG) measurements, we show that inducing regio-randomness into the organic polymer modifies the intensity of interfacial electronic states, level of hybridization, density of interfacial charge transfer and the electronic wave function of the material. We present the HD-VSFG responses of the metal/P3HT heterojunctions containing both regio-regular and regio-random P3HT structures and show that the amount of non-resonant signal is closely related to the level of the spontaneous charge transfer at the interface. Thus, by measuring the non-resonant response of the metal/P3HT heterojunctions, the level of spontaneous charge transfer at the interface can be determined.

Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similarity. We propose ChemBoost, a chemical language based approach for affinity prediction using SMILES syntax. We hypothesize that SMILES is a codified language and ligands are documents composed of chemical words. These documents can be used to learn chemical word vectors that represent words in similar contexts with similar vectors. In ChemBoost, the ligands are represented via chemical word embeddings, while the proteins are represented through sequence-based features and/or chemical words of their ligands. Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence. We used eXtreme Gradient Boosting to predict protein-ligand affinities in KIBA and BindingDB data sets. ChemBoost was able to predict drug-target binding affinity as well as or better than state-of-the-art machine learning systems. When powered with ligand-centric representations, ChemBoost was more robust to the changes in protein sequence similarity and successfully captured the interactions between a protein and a ligand, even if the protein has low sequence similarity to the known targets of the ligand., Comment: Molecular Informatics

CO$_2$ emissions originating from the construction industry have a significant impact on global warming where the production of ordinary Portland cement clinker is responsible for approximately 8\% of all human-made CO$_2$. Alkali doped calcium-silicate-hydrate (C-S-H) is a critical silicate material since the use of blended cements and alkali-activated materials in construction industry can substantially reduce human-made CO$_2$ emissions. However, the effect of alkali doping (Na and K) on the long-term stability and associated durability of C-S-H remains an open question. Here, using first principles quantum chemistry calculations on the model crystalline phase clinotobermorite, we show that there is a strong interplay between the thermodynamic stability of alkali doped C-S-H and the symmetry of the alkali atoms in the structure. Our results reveal that a symmetrical distribution of alkali atoms leads to a higher stability value such that stable structures with moderate alkali concentrations can be obtained provided that the alkali atoms are allowed to settle into a symmetrical distribution. We investigate the associated structural mechanisms by calculating the migration barriers of alkali atoms within the material, the electronic charge distribution in the material and the variation of basal spacing by using both computational methods and X-ray diffraction analysis.

Based on first-principles density functional calculations, we show that SnS and SnSe layers can form mechanically rigid heterostructures with the constituent puckered or buckled monolayers. Due to the strong interlayer coupling, the electronic wavefunctions of the conduction and valence band edges are delocalized across the heterostructure. The resultant bandgap of the heterostructures reside in the infrared region. With strain engineering, the heterostructure bandgap undergoes transition from indirect to direct in the puckered phase. Our results show that there is a direct correlation between the electronic wavefunction and the mechanical rigidity of the layered heterostructure.

Conventional drying of colloidal materials and gels (including cement) can lead to detrimental effects due to the buildup of internal stresses as water evaporates from the nano/microscopic pores. However, the underlying nanoscopic alterations in these gel materials that are, in part, responsible for macroscopically-measured strain values, especially at low relative humidity, remain a topic of open debate in the literature. In this study, sodium-based calcium-alumino-silicate-hydrate (C-(N)-A-S-H) gel, the major binding phase of silicate-activated blast furnace slag (one type of low-CO$_2$ cement), is investigated from a drying perspective, since it is known to suffer extensively from drying-induced microcracking. By employing in situ synchrotron X-ray total scattering measurements and pair distribution function (PDF) analysis we show that the significant contributing factor to the strain development in this material at extremely low relative humidity (0%) is the local atomic structural rearrangement of the C-(N)-A-S-H gel, including collapse of interlayer spacing and slight disintegration of the gel. Moreover, analysis of the medium range (1.0 - 2.2 nm) ordering in the PDF data reveals that the PDF-derived strain values are in much closer agreement (same order of magnitude) with the macroscopically measured strain data, compared to previous results based on reciprocal space X-ray diffraction data. From a mitigation standpoint, we show that small amounts of ZrO$_2$ nanoparticles are able to actively reinforce the structure of silicate-activated slag during drying, preventing atomic level strains from developing. Mechanistically, these nanoparticles induce growth of a silica-rich gel during drying, which, via density functional theory calculations, we show is attributed to the high surface reactivity of tetragonal ZrO$_2$.

Greenhouse gas emissions originating from fossil fuel combustion contribute significantly to global warming, and therefore the design of novel materials that efficiently capture CO$_2$ can play a crucial role in solving this challenge. Here, we show that reducing the dimensionality of bulk crystalline portlandite results in a stable monolayer material, named portlandene, that is highly effective at capturing CO$_2$. Based on theoretical analysis comprised of ab-initio quantum mechanical calculations and force-field molecular dynamics simulations, we show that this single-layer phase is robust and maintains its stability even at high temperatures. The chemical activity of portlandene is seen to further increase upon defect engineering of its surface using vacancy sites. Defect-containing portlandene is capable of separating CO and CO$_2$ from a syngas (CO/CO$_2$/H$_2$) stream, yet is inert to water vapor. This selective behavior and the associated mechanisms have been elucidated by examining the electronic structure, local charge distribution and bonding orbitals of portlandene. Additionally, unlike conventional CO$_2$ capturing technologies, the regeneration process of portlandene does not require high temperature heat treatment since it can release the captured CO$_2$ by application of a mild external electric field, making portlandene an ideal CO$_2$ capturing material for both pre- and post-combustion processes.

We investigate excitons in stacked transition metal dichalcogenide (TMDC) layers under perpendicularly applied electric field, herein MoSe$_2$/WSe$_2$ van der Waals heterostructures. Band structures are obtained with density functional theory calculations, along with the electron and hole wave functions in conduction and valence bands, respectively. Although the type-II nature of the heterostructure leads to fully charge separated inter-layer excitons, charge carriers distribution among the layers is shown to be easily tunable by external field. Our results show that moderate values of electric field produce more evenly distributed wave functions along the heterostructure, thus enhancing both the inter-layer exciton binding energy and, most notably, its oscillator strength., Comment: 5 pages, 4 figures

We couple a conformal scalar field in (2+1) dimensions to Einstein gravity with torsion. The field equations are obtained by a variational principle. We could not solve the Einstein and Cartan equations analytically. These equations are solved numerically with 4th order Runge-Kutta method. From the numerical solution, we make an ansatz for the rotation parameter in the proposed metric, which gives an analytical solution for the scalar field for asymptotic regions., Comment: 12 pages, 7 figures

Alkali-activated materials and related alternative cementitious systems are sustainable material technologies that have the potential to substantially lower CO$_2$ emissions associated with the construction industry. However, the impact of augmenting the chemical composition of the material on the main binder phase, calcium-silicate-hydrate gel, is far from understood, particularly since this binder phase is disordered at the nanoscale. Here, we reveal the presence of a charge balancing mechanism at the molecular level, which leads to stable structures when alkalis (i.e., Na or K) are incorporated into a calcium-silicate-hydrate gel, as modeled using crystalline 14{\AA} tobermorite. These alkali containing charge balanced structures possess superior mechanical properties compared to their charge unbalanced counterparts. Our results, which are based on first-principles simulations using density functional theory, include the impact of charge balancing on the optimized geometries of the new model phases, formation energies, local bonding environments, bulk moduli and diffusion barriers of the alkali atoms within the crystals.

Black phosphorus is an infrared layered material. Its bandgap complements other widely studied two-dimensional materials: zero-gap graphene and visible/near-infrared gap transition metal dichalcogenides. Though highly desirable, a comprehensive infrared characterization is still lacking. Here we report a systematic infrared study of mechanically exfoliated few-layer black phosphorus, with thickness ranging from 2 to 15 layers and photon energy spanning from 0.25 to 1.36 eV. Each few-layer black phosphorus exhibits a thickness-dependent unique infrared spectrum with a series of absorption resonances, which reveals the underlying electronic structure evolution and serves as its IR fingerprints. Surprisingly, unexpected absorption features, which are associated with the forbidden optical transitions, have been observed. Furthermore, we unambiguously demonstrate that controllable uniaxial strain can be used as a convenient and effective approach to tune the electronic structure of few-layer black phosphorus. Our study paves the way for black phosphorus applications in infrared photonics and optoelectronics., Comment: updated version, add new data for strain engineering of few-layer BP electronic structures. Paper has been published in Nature Communications

We present a comprehensive study of the band alignments of two-dimensional (2D) semiconducting materials and highlight the possibilities of forming momentum-matched type I, II and III heterojunctions; an enticing possibility being atomic heterostructures where the constituent monolayers have band edges at the zone center. Our study, which includes the Group IV and III-V compound monolayer materials, Group V elemental monolayer materials, transition metal dichalcogenides (TMD) and transition metal trichalcogenides (TMT) reveals that almost half of these materials have conduction and/or valence band edges residing at the zone center. Using first-principles density functional calculations, we present the type of the heterojunction for 903 different possible combination of these 2D materials which establishes a periodic table of heterojunctions.

We propose a model for planar nanoscale dielectric capacitor consisting of a single layer, insulating hexagonal boron nitride (BN) stripe placed between two metallic graphene stripes, all forming commensurately a single atomic plane. First-principles density functional calculations on these nanoscale capacitors for different levels of charging and different widths of graphene - BN stripes mark high gravimetric capacitance values, which are comparable to those of supercapacitors made from other carbon based materials. Present nanocapacitor model allows the fabrication of series, parallel and mixed combinations which offer potential applications in 2D flexible nanoelectronics, energy storage and heat-pressure sensing systems., Comment: Published version in PRB

Silicene and germanene derivatives constructed from periodic dumbbell units play a crucial role in multilayers of these honeycomb structures. Using first-principles calculations based on density functional theory, here we investigate the dumbbell formation mechanisms and energetics of Group IV atoms adsorbed on graphene, silicene, germanene and stanene monolayer honeycomb structures. The stabilities of the binding structures are further confirmed by performing ab-initio molecular dynamics calculations at elevated temperatures, except for stanene which is subject to structural instability upon the adsorption of adatoms. Depending on the row number of the adatoms and substrates we find three types of binding structures, which lead to significant changes in the electronic, magnetic, and optical properties of substrates. In particular, Si, Ge and Sn adatoms adsorbed on silicene and germanene form dumbbell structures. Furthermore, dumbbell structures occur not only on single layer, monatomic honeycomb structures, but also on their compounds like SiC and SiGe. We show that the energy barrier to the migration of a dumbbell structure is low due to the concerted action of atoms. This renders dumbbells rather mobile on substrates to construct new single and multilayer Si and Ge phases., Comment: Accepted for publication in J. Phys. Chem. C

Germanene, a graphene like single layer structure of Ge, has been shown to be stable and recently grown on Pt and Au substrates. We show that a Ge adatom adsorbed to germanene pushes down the host Ge atom underneath and forms a dumbbell structure. This exothermic process occurs spontaneously. The attractive dumbbell-dumbbell interaction favors high coverage of dumbbells. This letter heralds stable new phases of germanene, which are constructed from periodically repeating coverage of dumbbell structures and display diversity of electronic and magnetic properties., Comment: Published in JPCL http://pubs.acs.org/doi/abs/10.1021/jz500977v

The growth of the $\sqrt{3} \times \sqrt{3}$ reconstructed silicene on Ag substrate has been frequently observed in experiments while its atomic structure and formation mechanism is poorly understood. Here by first-principles calculations we show that $\sqrt{3} \times \sqrt{3}$ reconstructed silicene is constituted by dumbbell units of Si atoms arranged in a honeycomb pattern. Our model shows excellent agreement with the experimentally reported lattice constant and STM image. We propose a new mechanism for explaining the spontaneous and consequential formation of $\sqrt{3} \times \sqrt{3}$ structures from $3 \times 3$ structures on Ag substrate. We show that the $\sqrt{3} \times \sqrt{3}$ reconstruction is mainly determined by the interaction between Si atoms and have weak influence from Ag substrate. The proposed mechanism opens the path to understanding of multilayer silicon.

Silica or SiO$_2$, the main constituent of earth's rocks has several 3D complex crystalline and amorphous phases, but it does not have a graphite like layered structure in 3D. Our theoretical analysis and numerical calculations from the first-principles predict a single-layer honeycomb like allotrope, h$\alpha$-silica, which can be viewed to be derived from the oxidation of silicene and it has intriguing atomic structure with re-entrant bond angles in hexagons. It is a wide band gap semiconductor, which attains remarkable electromechanical properties showing geometrical changes under external electric field. In particular, it is an auxetic metamaterial with negative Poisson's ratio and has a high piezoelectric coefficient. While it can form stable bilayer and multilayer structures, its nanoribbons can show metallic or semiconducting behavior depending on their chirality. Coverage of dangling Si orbitals by foreign adatoms can attribute new functionalities to h$\alpha$-silica. In particular, Si$_2$O$_5$, where Si atoms are saturated by oxygen atoms from top and bottom sides alternatingly can undergo a structural transformation to make silicatene, another stable, single layer structure of silica., Comment: Accepted for publication in Physical Review Letters

The interaction of silicene with Si, C, H, O, Ti atoms along with H$_2$, H$_2$O and O$_2$ molecules are investigated and the induced functionalities thereof are analyzed using first principles density functional theory. Si adatom initially adsorbed at the top site of silicene pushes down the Si atom underneath to form a dumbbell like structure with 3+1 coordination. This prediction is important for silicene research and reveal new physical phenomena related with the formation of multilayer Si, which is apparently the precursor state for missing layered structure of silicon. We found that dumbbell structure attributes coverage dependent electronic and magnetic properties to nonmagnetic bare silicene. Even more interesting is that silicene with dumbbells is energetically more favorable than the pristine silicene: The more dense the dumbbell coverage, the stronger is the cohesion. Incidentally, these structures appear to be intermediate between between silicene and silicon. Carbon adatom, which is initially adsorbed to the bridge position, substitutes one Si atom, if it overcomes a small energy barrier. Oxygen molecule can dissociate on silicene surface, whereby constituent oxygen atoms oxidize silicene by forming strong bonds. By varying the concentration and decoration of carbon, hydrogen and oxygen atoms one can tune the band gap of silicene. Through the adsorption of hydrogen or titanium adatom, silicene acquires spin polarized state. A half metallic ferromagnetic behavior is attained at specific uniform coverage of Ti adatom, which may function as a spin valve., Comment: Accepted for publication in The Journal of Physical Chemistry http://pubs.acs.org/doi/abs/10.1021/jp408647t

Self healing mechanisms of vacancy defects in graphene and silicene are studied using first principles calculations. We investigated host adatom adsorption, diffusion, vacancy formation and revealed atomistic mechanisms in the healing of single, double and triple vacancies of single layer graphene and silicene. Silicon adatom, which is adsorbed to silicene at the top site forms a dumbbell like structure by pushing one Si atom underneath. The asymmetric reconstruction of the single vacancy in graphene is induced by the magnetization through the rebonding of two dangling bonds and acquiring a significant magnetic moment through remaining unsaturated dangling bond. In silicene, three two-fold coordinated atoms surrounding the single vacancy become four-fold coordinated and nonmagnetic through rebonding. The energy gained through new bond formation becomes the driving force for the reconstruction. Under the external supply of host atoms, while the vacancy defects of graphene heal perfectly, Stone-Wales defect can form in the course of healing of silicene vacancy. The electronic and magnetic properties of suspended, single layer graphene and silicene are modified by reconstructed vacancy defects., Comment: Published in PRB: http://prb.aps.org/abstract/PRB/v88/i4/e045440

We investigate a nanoscale dielectric capacitor model consisting of two-dimensional, hexagonal h-BN layers placed between two commensurate and metallic graphene layers using self-consistent field density functional theory. The separation of equal amounts of electric charge of different sign in different graphene layers is achieved by applying electric field perpendicular to the layers. The stored charge, energy, and the electric potential difference generated between the metallic layers are calculated from the first-principles for the relaxed structures. Predicted high-capacitance values exhibit the characteristics of supercapacitors. The capacitive behavior of the present nanoscale model is compared with that of the classical Helmholtz model, which reveals crucial quantum size effects at small separations, which in turn recede as the separation between metallic planes increases., Comment: Published version in The Journal of Physical Chemistry: http://pubs.acs.org/doi/abs/10.1021/jp403706e

Using first-principles Density Functional Theory calculations, we showed that electronic and magnetic properties of bare and Ti adatom adsorbed single-layer silicene and germanene, which are charged or exerted by a perpendicular electric field are modified to attain new functionalities. In particular, when exerted by a perpendicular electric field, the symmetry between the planes of buckled atoms is broken to open a gap at the Dirac points. The occupation of 3d-orbitals of adsorbed Ti atom changes with charging or applied electric field to induce significant changes of magnetic moment. We predict that neutral silicene uniformly covered by Ti atoms becomes a half-metal at a specific value of coverage and hence allows the transport of electrons in one spin direction, but blocks the opposite direction. These calculated properties, however exhibit a dependence on the size of the vacuum spacing between periodically repeating silicene and germanene layers, if they are treated using plane wave basis set within periodic boundary condition. We clarified the cause of this spurious dependence and show that it can be eliminated by the use of local orbital basis set., Comment: Accepted for Journal of Physics: Condensed Matter

A single graphene layer placed between two parallel Ni(111) surfaces screens the strong attractive force and results in a significant reduction of adhesion and sliding friction. When two graphene layers are inserted, each graphene is attached to one of the metal surfaces with a significant binding and reduces the adhesion further. In the sliding motion of these surfaces the transition from stick-slip to continuous sliding is attained, whereby non-equilibrium phonon generation through sudden processes is suppressed. The adhesion and corrugation strength continues to decrease upon insertion of the third graphene layer and eventually saturates at a constant value with increasing number of graphene layers. In the absence of Ni surfaces, the corrugation strength of multilayered graphene is relatively higher and practically independent of the number of layers. Present first-principles calculations reveal the superlubricant feature of graphene layers placed between pseudomorphic Ni(111) surfaces, which is achieved through the coupling of Ni-3d and graphene-$\pi$ orbitals. The effect of graphene layers inserted between a pair of parallel Cu(111) and Al(111) surfaces are also discussed. The treatment of sliding friction under the constant loading force, by taking into account the deformations corresponding to any relative positions of sliding slabs, is the unique feature of our study., Comment: Accepted paper for Physical Review B

We predict the stabilities of \alpha-graphynes and their boron nitride analogues(\alpha-BNyne), which are considered as competitors of graphene and two-dimensional hexagonal BN. Based on first-principles plane wave method, we investigated the stability and structural transformations of these materials at different sizes using phonon dispersion calculations and ab-initio finite temperature, molecular dynamics simulations. Depending on the number of additional atoms in the edges between the corner atoms of the hexagons, n, both \alpha-graphyne(n) and \alpha-BNyne(n) are stable for even n, but unstable for odd n. \alpha-graphyne(3) undergoes a structural transformation, where the symmetry of hexagons is broken. We present the structure optimized cohesive energies, electronic, magnetic and mechanical properties of stable structures. Our calculations reveal the existence of Dirac cones in the electronic structures of \alpha-graphynes of all sizes, where the Fermi velocities decrease with increasing n. The electronic and magnetic properties of these structures are modified by hydrogenation. A single hydrogen vacancy renders a magnetic moment of one Bohr magneton. We finally present the properties of the bilayer \alpha-graphyne and \alpha-BNyne structures. We expect that these layered materials can function as frameworks in various chemical and electronic applications., Comment: Published version in The Journal of Physical Chemistry

Nucleation and growth mechanisms of short chains of carbon atoms on single-layer, hexagonal boron nitride (h-BN), and short BN chains on graphene are investigated using first-principles plane wave calculations. Our analysis starts with the adsorption of a single carbon ad-atom and examines its migrations. Once a C$_2$ nucleates on h-BN, the insertion of each additional carbon at its close proximity causes a short segment of carbon atomic chain to grow by one atom at at a time in a quaint way: The existing chain leaves its initial position and subsequently is attached from its bottom end to the top of the carbon ad-atom. The electronic, magnetic and structural properties of these chains vertically adsorbed to h-BN depend on the number of carbon atoms in the chain, such that they exhibit an even-odd disparity. An individual carbon chain can also modify the electronic structure with localized states in the wide band gap of h-BN. As a reverse situation we examined the growth of short BN atomic chains on graphene, which attribute diverse properties depending on whether B or N is the atom bound to the substrate. These results together with ab-initio molecular dynamics simulations of the growth process reveal the interesting self-assembly behavior of the grown chains. Furthermore, we find that these atomic chains enhance the chemical activity of h-BN and graphene sheets by creating active sites for the bonding of various ad-atoms and can act as pillars between two and multiple sheets of these honeycomb structures leaving wider spacing between them to achieve high capacity storage of specific molecules., Comment: Accepted for Physical Review B

We propose a model for nanoscale supercapacitor consisting of two-dimensional insulating BN layers placed between two commensurate and metallic graphene layers. First-principles Density Functional calculations of structure optimized total energy and self-consistent field potential performed on these nanoscale capacitors for different levels of charging and different number of BN layers mark the values of capacitance per unit mass, which are larger than those measured values for the supercapacitors made from other carbon based materials. Our theoretical study also compares results obtained for the present nanoscale capacitor with those of classical Helmholtz model and reveals crucial differences. Our model allows the fabrication of series/parallel mixed combinations consisting of epitaxially grown, sequential and multiple graphene/BN sheets., Comment: Preliminary results

The growth process of single layer graphene with and without substrate is investigated using ab initio, finite temperature molecular dynamic calculations within density functional theory. An understanding of the epitaxial graphene growth mechanisms in the atomic level is provided by exploring the transient stages which occur at the growing edges of graphene. These stages are formation and collapse of large carbon rings together with the formation and healing of Stone-Wales like pentagon-heptagon defects. The activation barriers for the healing of these growth induced defects on various substrates are calculated using the climbing image nudge elastic band method and compared with that of the Stone-Wales defect. It is found that the healing of pentagon-heptagon defects occurring near the edge in the course of growth is much easier than that of Stone-Wales defect. The role of the substrate in the epitaxial growth and in the healing of defects are also investigated in detail, along with the effects of using carbon dimers as the building blocks of graphene growth., Comment: Published version

The Blume-Emery-Griffiths spin glass is studied by renormalization-group theory in d=3. The boundary between the ferromagnetic and paramagnetic phases has first-order and two types of second-order segments. This topology includes an inverted tricritical point, first-order transitions replacing second-order transitions as temperature is lowered. The phase diagrams show disconnected spin-glass regions, spin-glass and paramagnetic reentrances, and complete reentrance, where the spin-glass phase replaces the ferromagnet as temperature is lowered for all chemical potentials., Comment: Added discussion, references, and figure insets. 5 pages, 6 figures. Published version

The calculations of the elastic scattering properties of ultracold-francium isotopes are reported in the detail. A parametric model potential for the triplet molecular states of the Fr$_{2}$ is represented, and the scattering lengths at and the effective range $r_{e}$ are calculated using WKB and Numerov methods for Fr-Fr in the triplet state. The convergence of these scattering properties as the depending on a $K_{0}$ parameter and core radius is also investigated using two method as Quantum Defect Theory and analytical formula of symskovisky., Comment: 6 pages, 6 tables and 2 figures

The calculation of the elastic scattering properties of mixtures composed by rubidium and potassium atoms is reported and compared with experimental results in detail. The improved potentials for both molecular states the singlet and the triplet of the RbK are presented, and the scattering lengths a_{t} and the effective range r_{e} are calculated with the help of this potential using WKB and Numerov methods for Rb-K in the triplet and singlet state. In addition, the convergence of these scattering properties as the depending on a K_{0} parameter is investigated using Quantum Defect Theory. The evaporative cooling other results that include the cross section and the spin-charge cross section, the rate coefficient as a function of the energy and the ultra-low temperature are also presented in this study., Comment: 6 tables and 4 figures