Discovery of Alloy Catalysts Beyond Pd for Selective Hydrogenation of Reformate via First-principle Screening with Consideration of H-coverage

The highly selective hydrogenation to remove olefins is a significant refining approach for the reformate. Herein, a library of transition metal for reformate hydrogenation is tested experimentally to validate the predictive level of catalytic activity from our theoretical framework, which combines ab initio calculations and microkinetic modeling, with consideration of surface H-coverage effect on hydrogenation kinetics. The favorable H coverage of specific alloy surface under relevant hydrogenation condition, is found to be determined by its corresponding alloy composition. Besides, olefin hydrogenation rate is determined as a function of two descriptors, i.e. H coverage and binding energies of atomic hydrogen, paving the way to computationally screen on metal component in the periodic table. Evaluation of 172 bimetallic alloys based on the activity volcano map, as well as benzene hydrogenation rate, identifies prospective superior candidates and experimentally confirms that Zn3Ir1 outperforms pure Pd catalysts for the selective hydrogenation refining of reformate. The insights into H-coverage-related microkinetic modelling have enabled us to both theoretically understand experimental findings and identify novel catalysts, thus, bridging the gap between first-principle simulations and industrial applications. This work provides useful guidance for experimental catalyst design, which can be easily extended to other hydrogenation reaction.

Regulating Excess Electrons in Reducible Metal Oxides for Enhanced Oxygen Evolution Reaction Activity: A Mini-Review

Identifying a universal activity descriptor for metal oxides, akin to the d-band center for transition metals, remains a significant challenge in catalyst design, largely due to the intricate electronic structures of metal oxides. This review highlights a major advancement in formulating the number of excess electrons (NEE) as an activity descriptor for oxygen evolution reaction (OER) on reducible metal oxide surfaces. We elaborate on the quantitative relationship between NEE and the adsorption properties of OER intermediates, and unveil the decisive role of the octet rule on the OER performance of these oxides. This insight provides a robust theoretical basis for designing effective OER catalysts. Moreover, we discuss critical experimental evidence supporting this theory and summarize recent advances in employing NEE as a guiding principle for developing highly efficient OER catalysts experimentally.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2 ≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2 ≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Defect engineering of 1T'MX2(M= Mo, W andX= S, Se) transition metal dichalcogenide-based electrocatalyst for alkaline hydrogen evolution reaction

The alkaline electrolyzer (AEL) is a promising device for green hydrogen production. However, their energy conversion efficiency is currently limited by the low performance of the electrocatalysts for the hydrogen evolution reaction (HER). As such, the electrocatalyst design for the high-performance HER becomes essential for the advancement of AELs. In this work, we used both hydrogen (H) and hydroxyl (OH) adsorption Gibbs free energy changes as the descriptors to investigate the catalytic HER performance of 1T' transition metal dichalcogenides (TMDs) in an alkaline solution. Our results reveal that the pristine sulfides showed better alkaline HER performance than their selenide counterparts. However, the activities of all pristine 1T' TMDs are too low to dissociate water. To improve the performance of these materials, defect engineering techniques were used to design TMD-based electrocatalysts for effective HER activity. Our density functional theory results demonstrate that introducing single S/Se vacancy defects can improve the reactivities of TMD materials. Yet, the desorption of OH becomes the rate-determining step. Doping defective MoS2with late 3d transition metal (TM) atoms, especially Cu, Ni, and Co, can regulate the reactivity of active sites for optimal OH desorption. As a result, the TM-doped defective 1T' MoS2can significantly enhance the alkaline HER performance. These findings highlight the potential of defect engineering technologies for the design of TMD-based alkaline HER electrocatalysts.

A Universal Method for Regulating Carbon Microcrystalline Structure for High-Capacity Sodium Storage: Binding Energy As Descriptor

Sodium-ion batteries (SIBs) are attracting worldwide attention due to their multiple merits including abundant reserve and safety. However, industrialization is challenged by the scarcity of high-performance carbon anodes with high specific capacities. Here, we report the metal-assisted microcrystalline structure regulation of carbon materials to achieve high-capacity sodium storage. Systematic investigations of in situ thermal-treatment X-ray diffraction and multiple spectroscopies uncover the regulation mechanism of constructing steric hindrance (C-O-C bonds) to restrain the aromatic polycondensation reaction. The carbon precursor of polycyclic aromatic hydrocarbon-type pitch contributes to a high carbon yield rate (40%) compared with those of resin and biomass precursors. The as-synthesized carbon materials deliver high capacities of up to 390 mAh g-1, surpassing many reported carbon anodes for SIBs. Through correlating specific capacity with ID/IG values in Raman spectra and theoretical calculation of carbon materials regulated by different metal elements (Mn, Nb, Ce, Cr, and V), we identify and propose the binding energy as the descriptor for characterizing the capability of regulating the carbon microcrystalline structure to promote sodium storage. This work provides a universal method for regulating the carbon structure, which may lead to the controlled design and fabrication of carbon materials for energy storage and conversion and beyond.

DFT-assisted low-dimensional carbon-based electrocatalysts design and mechanism study: a review

Low-dimensional carbon-based (LDC) materials have attracted extensive research attention in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relieve environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional "trial and error" strategies greatly slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus revealing the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The commonly used calculation methods and the basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge linking the structure and activity and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlated multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. Furthermore, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

R software for QSAR analysis in phytopharmacological studies

INTRODUCTION

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable.

OBJECTIVE

The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity.

RESULTS

The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model.

CONCLUSION

QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to treat Metabolic Syndrome

BACKGROUND

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism.

OBJECTIVE

To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome.

METHODS

In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor.

RESULTS

The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 µM and 0.0027 µM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed.

CONCLUSION

The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Rational Design Strategies for Nanozymes

Nanozymes constitute an emerging class of nanomaterials with enzyme-like characteristics. Over the past 15 years, more than 1200 nanozymes have been developed, and they have demonstrated promising potentials in broad applications. With the diversification and complexity of its applications, traditional empirical and trial-and-error design strategies no longer meet the requirements for efficient nanozyme design. Thanks to the rapid development of computational chemistry and artificial intelligence technologies, first-principles methods and machine-learning algorithms are gradually being adopted as a more efficient and easier means to assist nanozyme design. This review focuses on the potential elementary reaction mechanisms in the rational design of nanozymes, including peroxidase (POD)-, oxidase (OXD)-, catalase (CAT)-, superoxide dismutase (SOD)-, and hydrolase (HYL)-like nanozymes. The activity descriptors are introduced, with the aim of providing further guidelines for nanozyme active material screening. The computing- and data-driven approaches are thoroughly reviewed to give a proposal on how to proceed with the next-generation paradigm rational design. At the end of this review, personal perspectives on the prospects and challenges of the rational design of nanozymes are put forward, hoping to promote the further development of nanozymes toward superior application performance in the future.

Modulating the Catalytic Properties of Bimetallic Atomic Catalysts: Role of Dangling Bonds and Charging

Bimetallic atomic catalysts (BACs) exhibit great potential in CO₂ electroreduction. However, modulation and improvement of their catalytic performance are still challenging. To address these issues, an intrinsic descriptor ψ based on the valence properties of active centers was used. The role of the dangling bonds and charging in modulating the catalytic properties of BACs called M₁ M₂ -N₆ -G (M₁ =Ru and Fe) was studied. It was shown that linear relationships between the adsorption energy of the C-species are broken under the effect of the dangling bonds and that they are restored with charging. However, charging has minor effects on the adsorption of the O-species. These findings enable screening promising BACs for CH₃ OH production. This research provides effective schemes for modulating the properties of catalysts, which is beneficial to enriching high-performance catalysts for various reactions.

Ni-Fe Oxides/TiO2 Heterojunction Anodes for Reactive Chlorine Generation and Mediated Water Treatment

Reactive chlorine-mediated electrochemical water treatment necessitates selective chlorine evolution reaction (ClER) versus parallel oxygen evolution reaction (OER) in mild pH (7-10), with minimal deployments of precious electrocatalysts. This study reports Ni_0.33Fe_0.67O_y/TiO₂ heterojunction anode prepared by a straightforward sol-gel coating with thermal decomposition at 425 °C. The ClER current efficiency (CE, 70%) and energy efficiency (2.3 mmol W h^-1) were comparable to benchmarking Ir₇Ta₃O_y/TiO₂ at 30 mA cm^-2 in 50 mM NaCl solutions with near-neutral pH. Correlations of ClER CE of variable Ni_xFe_1-xO_y/TiO₂ (x: 0.33, 0.8-1) with the flat-band potential and p-band center, as experimental descriptors for surface charge density, nominated the outer TiO₂ to be the active ClER center. The underlying Ni_0.33Fe_0.67O_y, characterized as biphasic NiFe₂O₄ and NiO, effectively lowered the O binding energy of TiO₂ by electronic interaction across the junction. The OER activity of Ni_0.33Fe_0.67O_y superior to the other Fe-doped Ni oxides suggested that the conductive OER intermediates generated on Ni_0.33Fe_0.67O_y could also facilitate the ClER as an ohmic contact. Stability tests and NH₄⁺ degradation in synthetic and real wastewater confirmed the feasibility of Ni_0.33Fe_0.67O_y/TiO₂ heterojunction anode for mediated water treatments in mild pH.

Molecular insights of nanozymes from design to catalytic mechanism

Emerging as cost-effective potential alternatives to natural enzymes, nanozymes have attracted increasing interest in broad fields. To exploit the in-depth potential of nanozymes, rational structural engineering and explicit catalytic mechanisms at the molecular scale are required. Recently, impressive progress has been made in mimicking the characteristics of natural enzymes by constructing metal active sites, binding pockets, scaffolds, and delicate allosteric regulation. Ingenious in-depth studies have been conducted with advances in structural characterization and theoretical calculations, unveiling the "black box" of nanozyme-catalytic mechanisms. This review introduces the state-of-art synthesis strategies by learning from the natural enzyme counterparts and summarizes the general overview of the nanozyme mechanism with a particular emphasis on the adsorbed intermediates and descriptors that predict the nanozyme activity The emerging activity assessment methodology that illustrates the relationship between electrochemical oxygen reduction and enzymatic oxygen reduction is discussed with up-to-date advances Future opportunities and challenges are presented in the end to spark more profound work and attract more researchers from various backgrounds to the flourishing field of nanozymes.

Standard Deviation Effect of Average Structure Descriptor on Grain Boundary Energy Prediction

The structural complexities of grain boundaries (GBs) result in their complicated property contributions to polycrystalline metals and alloys. In this study, we propose a GB structure descriptor by linearly combining the average two-point correlation function (PCF) and standard deviation of PCF via a weight parameter, to reveal the standard deviation effect of PCF on energy predictions of Cu, Al and Ni asymmetric tilt GBs (i.e., Σ3, Σ5, Σ9, Σ11, Σ13 and Σ17), using two machine learning (ML) methods; i.e., principal component analysis (PCA)-based linear regression and recurrent neural networks (RNN). It is found that the proposed structure descriptor is capable of improving GB energy prediction for both ML methods. This suggests the discriminatory power of average PCF for different GBs is lifted since the proposed descriptor contains the data dispersion information. Meanwhile, we also show that GB atom selection methods by which PCF is evaluated also affect predictions.

eg Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes

Functional nanomaterials offer an attractive strategy to mimic the catalysis of natural enzymes, which are collectively called nanozymes. Although the development of nanozymes shows a trend of diversification of materials with enzyme-like activity, most nanozymes have been discovered via trial-and-error methods, largely due to the lack of predictive descriptors. To fill this gap, this work identified e_g occupancy as an effective descriptor for spinel oxides with peroxidase-like activity and successfully predicted that the e_g value of spinel oxide nanozymes with the highest activity is close to 0.6. The LiCo₂O₄ with the highest activity, which is finally predicted, has achieved more than an order of magnitude improvement in activity. Density functional theory provides a rationale for the reaction path. This work contributes to the rational design of high performance nanozymes by using activity descriptors and provides a methodology to identify other descriptors for nanozymes.

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.

Descriptor and Scaling Relations for Ion Mobility in Crystalline Solids

Ion mobility is a critical performance parameter not only in electrochemical energy storage and conversion but also in other electrochemical devices. On the basis of first-principles electronic structure calculations, we have derived a descriptor for the ion mobility in battery electrodes and solid electrolytes. This descriptor is entirely composed of observables that are easily accessible: ionic radii, oxidation states, and the Pauling electronegativities of the involved species. Within a particular class of materials, the migration barriers are connected to this descriptor through linear scaling relations upon the variation of either the cation chemistry of the charge carriers or the anion chemistry of the host lattice. The validity of these scaling relations indicates that a purely ionic view falls short of capturing all factors influencing ion mobility in solids. The identification of these scaling relations has the potential to significantly accelerate the discovery of materials with desired mobility properties.

Screening of Transition-Metal Single-Atom Catalysts Anchored on Covalent-Organic Frameworks for Efficient Nitrogen Fixation

Two-dimensional (2D) covalent-organic frameworks (COFs) offer abundant hollow sites for stably anchoring transition-metal (TM) atoms to promote single-atom catalysis (SACs), which is expected to overcome the poor stability of SACs on conventional substrate materials. Using first-principles calculations within density-functional theory, a number of TM atoms embedded on a 2D COF Pc-TFPN (TMPc-TFPN) as SACs for ammonia synthesis under ambient conditions are investigated. Through a "five-step" screening strategy, WPc-TFPN is highlighted from 26 TMPc-TFPNs as the best SACs for nitrogen reduction reaction (NRR) with a low limiting potential of -0.19 V. Meanwhile, multiple-level descriptors are developed to uncover the origins of NRR activity, among which a simple descriptor φ that involves the electronegativity and number of d electrons of TM atoms shows volcano plot trends of limiting potential of NRR. This work provides a rational strategy for fast screening SACs for the electrochemical N₂ fixation using 2D COFs containing TM-N₄ units as host materials, which could also be applied to other electrochemical reactions.

Palindromic Vectors, Symmetropy and Symmentropy as Symmetry Descriptors of Binary Data

Today, the palindromic analysis of biological sequences, based exclusively on the study of "mirror" symmetry properties, is almost unavoidable. However, other types of symmetry, such as those present in friezes, could allow us to analyze binary sequences from another point of view. New tools, such as symmetropy and symmentropy, based on new types of palindromes allow us to discriminate binarized 1/f noise sequences better than Lempel-Ziv complexity. These new palindromes with new types of symmetry also allow for better discrimination of binarized DNA sequences. A relative error of 6% of symmetropy is obtained from the HUMHBB and YEAST1 DNA sequences. A factor of 4 between the slopes obtained from the linear fits of the local symmentropies for the two DNA sequences shows the discriminative capacity of the local symmentropy. Moreover, it is highlighted that a certain number of these new palindromes of sizes greater than 30 bits are more discriminating than those of smaller sizes assimilated to those from an independent and identically distributed random variable.

Two-Dimensional Conductive Metal-Organic Frameworks as Highly Efficient Electrocatalysts for Lithium-Sulfur Batteries

Lithium-sulfur batteries (LiSBs) which are expected to fulfill the increasing demands of high-density energy storage have been under intensive investigation. However, the development of LiSBs is facing many obstacles, such as the poor electronic conductivity of sulfur, shuttling effects of lithium polysulfides (LiPSs), sluggish Li₂S decomposition, and low discharging/charging efficiency. Suitable electrocatalysts that can solve the above problems are promising in the development of LiSBs. Herein, 13 two-dimensional (2D) metal-organic frameworks (MOFs) of nitrogen-, sulfur-, and oxygen-coordinated transition-metal (TM) atoms (Co, Ni, Cu, and Zn) are selected and constructed to reveal the structure-activity relationship of 2D MOFs in terms of the electrocatalytic performance. Among all the 2D MOFs investigated, Cu₃(HITP)₂, Zn₃(HITP)₂, and Cu₃(C₁₈H₉O₃N₃)₂ offer moderate binding strength to LiPSs, which effectively suppresses Li₂S_n dissolution and shuttling. Cu₃(HITP)₂ exhibits good electrical conductivity, a low Gibbs free energy barrier, effective electrocatalytic ability for Li₂S decomposition, and a high sulfur loading amount. A descriptor φ is proposed to correlate the binding energies of the 2D MOFs to the coordination environment and the electronegativity of the TM atoms in the LiPSs via an implicit volcano plot. These findings are helpful for understanding the electrocatalytic effect of 2D MOFs in LiSBs and represent a promising approach for the development of future LiSBs.

Blue and black phosphorene on metal substrates: a density functional theory study

We have performed density functional theory calculations to study blue phosphorene and black phosphorene on metal substrates. The substrates considered are the (111) and (110) surfaces of Al, Cu, Ag, Ir, Pd, Pt and Au and the (0001) and (101¯0) surfaces of Zr and Sc. The formation energyE_Fis negative (energetically favorable) for all 36 combinations of overlayer and substrate. By comparing values of ΔΩ, the change in free energy per unit area, as well as the overlayer-substrate binding energyE_b, we identify that Ag(111), Al(110), Cu(111), Cu(110) and possibly Au(110) may be especially suitable substrates for the synthesis and subsequent exfoliation of blue phosphorene, and the Ag(110) and Al(111) substrates for the synthesis of black phosphorene. However, these conclusions are drawn assuming the source of P atoms is bulk phosphorus, and can alter upon changing synthesis conditions (chemical potential of phosphorus). Thus, when the source of phosphorus atoms is P₄, blue phosphorene is favored only over Pt(111). We find that for all combinations of overlayer and substrate, the charge transfer per bond can be captured by the universal descriptorD=Δχ/ΔR, where ΔχandΔRare, respectively, the differences in electronegativity and atomic size between phosphorus and the substrate metal.

Universal-Descriptors-Guided Design of Single Atom Catalysts toward Oxidation of Li2 S in Lithium-Sulfur Batteries

The sulfur redox kinetics critically matters to superior lithium-sulfur (Li-S) batteries, for which single atom catalysts (SACs) take effect on promoting Li2 S redox process and mitigating the shuttle behavior of lithium polysulfide (LiPs). However, conventional trial-and-error strategy significantly slows down the development of SACs in Li-S batteries. Here, the Li2 S oxidation processes over MN4 @G catalysts are fully explored and energy barrier of Li2 S decomposition (Eb ) is identified to correlate strongly with three parameters of energy difference between initial and final states of Li2 S decomposition, reaction energy of Li2 S oxidation and LiS bond strength. These three parameters can serve as efficient descriptors by which two excellent SACs of MoN4 @G and WN4 @G are screened which give rise to Eb values of 0.58 and 0.55 eV, respectively, outperforming other analogues in adsorbing LiPs and accelerating the redox kinetics of Li2 S. This method can be extended to a wider range of SACs by coupling MN4 moiety with heterostructures and heteroatoms beyond N where WN4 @G/TiS2 heterointerface is predicted to exhibit enhanced catalytic performance for Li2 S decomposition with Eb of 0.40 eV. This work will help accelerate the process of designing a wider range of efficient catalysts in Li-S batteries and even beyond, e.g. alkali-ion-Chalcogen batteries.

Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.

Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics

Descriptors play a central role in constructing composition-structure-property relationships to guide materials design. We propose a material descriptor, δτ, for the composition dependence of the Curie temperature (T_c) on single doping elements in BaTiO₃ ferroelectrics, which is then generalized to a linear combination of multiple dopants in the solid solutions. The descriptor δτ depends linearly on the Curie temperature and also serves to separate the ferroelectric phase from the relaxor phase. We compare δτ to other commonly used descriptors such as the tolerance factor, electronegativity, and ionic displacement. By using regression analysis on our assembled experimental data, we show how it outperforms other descriptors. We use the trained machine-learned models to predict compositions in our search space with the largest ferroelectric, dielectric, and piezoelectric properties, namely, d₃₃, electrostrain, and recoverable energy storage density. We experimentally verify our predictions for T_c and classification into ferroelectrics and relaxors by synthesizing and characterizing six solid solutions in BaTiO₃ ferroelectrics. Our definition of δτ can shed light on the design of knowledge-based descriptors in other systems such as Pb-based and Bi-based solid solutions.

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.

ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins

Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal-Suite, a web server comprising a set of machine learning-based methods for studying proteins. The main module of ProtDCal-Suite is the ProtDCal software. ProtDCal translates the structural information of proteins into numerical descriptors that serve as input to machine-learning techniques. The ProtDCal-Suite server also incorporates a post-processing optional stage that allows ranking and filtering the obtained descriptors by computing their Shannon entropy values across the input set of proteins. ProtDCal's codification was used in the development of models for the prediction of specific protein properties. Thus, the other modules of ProtDCal-Suite are protein analysis tools implemented using ProtDCal's descriptors. Among them are PPI-Detect, for predicting the interaction likelihood of protein-protein and protein-peptide pairs, Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures, and Pred-NGlyco, for predicting N-glycosylation sites. ProtDCal-Suite is freely accessible at https://protdcal.zmb.uni-due.de.

Descriptors for High Throughput in Structural Materials Development

The development of novel structural materials with increasing mechanical requirements is a very resource-intense process if conventional methods are used. While there are high-throughput methods for the development of functional materials, this is not the case for structural materials. Their mechanical properties are determined by their microstructure, so that increased sample volumes are needed. Furthermore, new short-time characterization techniques are required for individual samples which do not necessarily measure the desired material properties, but descriptors which can later be mapped on material properties. While universal micro-hardness testing is being commonly used, it is limited in its capability to measure sample volumes which contain a characteristic microstructure. We propose to use alternative and fast deformation techniques for spherical micro-samples in combination with classical characterization techniques such as XRD, DSC or micro magnetic methods, which deliver descriptors for the microstructural state.

Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR

QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).

Finding Constellations in Chemical Space Through Core Analysis

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

K-Index: A Descriptor, Predictor, and Correlator of Complex Nanomorphology to Other Material Properties

Morphology is a qualitative property of nanostructured matter and is articulated by visual inspection of micrographs. For deterministic procedures that relate nanomorphology to synthetic conditions, it is necessary to express nano- and microstructures numerically. Selecting polyurea aerogels as a model system with demonstrated potential for rich nanomorphology and guided by a statistical design-of-experiments model, we prepared a large array of materials (208) with identical chemical composition but quite different nanostructures. By reflecting on SEM imaging, it was realized that our first preverbal impression about a nanostructure is related to its openness and texture; the former is quantified by porosity ( Π), and the latter is oftentimes related to hydrophobicity, which, in turn, is quantified by the contact angle (θ) of water droplets resting on the material. Herewith, the θ-to-Π ratio is referred to as the K-index, and it was noticed that all polyurea samples of this study could be put in eight K-index groups with separate nanomorphologies ranging from caterpillar-like assemblies of nanoparticles, to thin nanofibers, to cocoon-like structures, to large bald microspheres. A first validation of the K-index as a morphology descriptor was based on compressing samples to different strains: it was observed that as the porosity decreases, the water-contact angle decreases proportionally, and thereby the K-index remains constant. The predictive power of the K-index was demonstrated with 20 polyurea aerogels prepared in 8 binary solvent systems. Subsequently, several material properties were correlated to nanomorphology through the K-index and that, in turn, provided insight about the root cause of the diversity of the nanostructure in polyurea aerogels. Finally, using response surface methodology, K-indexes and other material properties of practical interest were correlated to the monomer, water, and catalyst concentrations as well as the three Hansen solubility parameters of the sol. That enabled the synthesis of materials with up to six prescribed properties at a time, including nanomorphology, bulk density, BET surface area, elastic modulus, ultimate compressive strength, and thermal conductivity.

Catalytic Chemistry Predicted by a Charge Polarization Descriptor: Synergistic O2 Activation and CO Oxidation by Au-Cu Bimetallic Clusters on TiO2(101)

The versatile properties of bimetallic nanoparticles greatly expand the range of catalyzed chemical reactions. We demonstrate that surface chemistry can be understood and predicted using a simple adsorbate-surface interaction descriptor that relates charge polarization to chemical reactivity. Our density functional theory studies of O₂ activation and CO oxidation catalyzed by Au₇-Cu₁ bimetallic nanoparticles supported on the TiO₂(101) surface demonstrate that the generated oxidized Cu atom (CuO _x) can efficiently inhibit the aggregation of the active Cu sites. Moreover, because of the strong dipole-dipole interaction between the surface and the adsorbate on the oxidized Cu site, the adsorption of CO + O₂/CO + O can be significantly enhanced, which can decrease the CO oxidation barriers and further improve catalytic performance. The product of the two electric dipole moments provides a parameter that allows us to predict the key catalytic properties for different adsorption sites and reaction pathways. The reported findings provide important insights into the mechanism of chemical reactivity of metallic clusters and generate a valuable principle for catalyst design.

A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules

In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN), Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern (LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. The Homogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC) are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and Random Forest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed method in terms of accuracy which gives best result than the competing methods.

Structural and Electronic Descriptors of Catalytic Activity of Graphene-Based Materials: First-Principles Theoretical Analysis

Characteristic features of the d-band in electronic structure of transition metals are quite effective as descriptors of their catalytic activity toward oxygen reduction reaction (ORR). With the promise of graphene-based materials to replace precious metal catalysts, descriptors of their chemical activity are much needed. Here, a site-specific electronic descriptor is proposed based on the p_z (π) orbital occupancy and its contribution to electronic states at the Fermi level. Simple structural descriptors are identified, and a linear predictive model is developed to precisely estimate adsorption free energies of OH (ΔG_OH ) at various sites of doped graphene, and it is demonstrated through prediction of the most optimal site for catalysis of ORR. These structural descriptors, essentially the number of ortho, meta, and para sites of N/B-doped graphene sheet, can be extended to other doped sp² hybridized systems, and greatly reduce the computational effort in estimating ΔG_OH and site-specific catalytic activity.

Cell wall permeability assisted virtual screening to identify potential direct InhA inhibitors of Mycobacterium tuberculosis and their biological evaluation

The arising cases of isoniazid-resistance have motivated research interests toward new class of molecules known as direct InhA inhibitors. Here, a combine approach of shape-based pharmacophore and descriptor-based 2D QSAR was used to identify the potential direct InhA inhibitors. The approach is duly assisted with in vitro testing and molecular dynamics simulations. A combination of empirical parameters was derived to use as a filter for cell wall permeability while 2D QSAR was used as another filter to predict the biological activity. Both filters were applied to prioritize the molecules for biological evaluation against anti-TB activity. It led to 6 potential molecules which showed > 90% inhibition of H37Rv strain of Mycobacterium tuberculosis in BACTEC assay. Further, MMGBSA binding free energy of identified molecules was compared with available highly potent molecule, 5-hexyl-2-(2-methylphenoxy) phenol (IC₅₀ = 5nM) using molecular dynamics simulations. It showed two molecules with comparatively higher affinity toward InhA as compared to potent molecule. It indicated the candidature of identified molecules to be further considered in anti-TB drug development pipeline.

Cross-Spectral Local Descriptors via Quadruplet Network

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.

Descriptors of Malignant Non-mass Enhancement of Breast MRI: Their Correlation to the Presence of Invasion

RATIONALE AND OBJECTIVES

This study aims to investigate the clinical significance of malignant non-mass enhancement (NME) descriptors in breast magnetic resonance images by assessing their correlation to the presence of invasion or lymph node metastasis.

MATERIALS AND METHODS

Three radiologists independently reviewed magnetic resonance images with malignant NMEs between January 2008 and December 2009. Distribution was assessed first, and then each of four internal enhancement patterns-clumped, clustered ring, branching, and hypointense area-was evaluated dichotomously (yes or no). Because clustered rings and hypointense areas were thought to be major structural elements of heterogeneous NMEs, they were also evaluated by integrating them into one collective descriptor we called the "heterogeneous structures." Chi-square test, Fisher exact test, or Student t test was used to analyze differences of variables by each reviewer. Positive predictive values (PPVs) of descriptors in predicting presence of invasion or lymph node metastasis were calculated. P < 0.05 was considered significant.

RESULTS

We included 131 malignant NMEs (76 in situ and 55 invasive) in 129 patients (two bilateral). All three observers' results showed clustered rings (PPVs 54.5%, 54.5%, 50.0%) (P = 0.0005, 0.038, 0.029) and hypointense areas (PPVs 63.6%, 61.5%, 73.9%) (P = 0.004, 0.024, 0.0006) to be significantly associated with invasion. When clustered rings and hypointense areas were integrated into heterogeneous structures, they were significantly associated with invasion (PPVs 54.3%, 53.3%, 51.8%) (P = 0.0003, 0.016, 0.003).

CONCLUSIONS

The NME descriptors clustered rings, hypoechoic areas, and heterogeneous structures, assessed collectively, were associated with invasive breast cancer.

Defect-Tolerant Monolayer Transition Metal Dichalcogenides

Localized electronic states formed inside the band gap of a semiconductor due to crystal defects can be detrimental to the material's optoelectronic properties. Semiconductors with a lower tendency to form defect induced deep gap states are termed defect-tolerant. Here we provide a systematic first-principles investigation of defect tolerance in 29 monolayer transition metal dichalcogenides (TMDs) of interest for nanoscale optoelectronics. We find that the TMDs based on group VI and X metals form deep gap states upon creation of a chalcogen (S, Se, Te) vacancy, while the TMDs based on group IV metals form only shallow defect levels and are thus predicted to be defect-tolerant. Interestingly, all the defect sensitive TMDs have valence and conduction bands with a very similar orbital composition. This indicates a bonding/antibonding nature of the gap, which in turn suggests that dangling bonds will fall inside the gap. These ideas are made quantitative by introducing a descriptor that measures the degree of similarity of the conduction and valence band manifolds. Finally, the study is generalized to nonpolar nanoribbons of the TMDs where we find that only the defect sensitive materials form edge states within the band gap.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.

Assigning Main Orientation to an EOH Descriptor on Multispectral Images

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

Why Pt Survives but Pd Suffers From SOx Poisoning?

Pd is more prone to sulfation compared to Pt. Given the chemical similarity between Pt and Pd, the radical divide in their tendencies for sulfation remains a puzzle. We explain this intriguing difference using an extensive first-principles thermodynamics analysis and computed bulk and surface phase diagrams. In practically relevant temperatures and O2 and SO3 partial pressures, we find that Pt and Pd show significantly different tendencies for oxidation and sulfation. PdO formation is favored even at low oxygen chemical potential; however, PtO2 formation is not favorable in catalytically relevant conditions. Similarly, PdSO4, and adsorbed SO3 and oxygen species on clean and oxidized surfaces are highly favored, whereas PtSO4 formation does not occur at typical temperature and pressure conditions. Finally, several descriptors are identified that correlate to heightened sulfation tendencies, such as the critical O chemical potential for bulk oxide and surface oxide formation, chemical potentials O and SO3 for bulk sulfate formation, and SO3 binding strength on metal surface-oxide layers, which can be used to explore promising sulfur resistant catalysts.

Investigation of solvolysis kinetics of new synthesized fluocinolone acetonide C-21 esters--an in vitro model for prodrug activation

In this study the solvolysis of newly synthesized fluocinolone acetonide C-21 esters was analysed in comparison with fluocinonide during a 24-hour period of time. The solvolysis was performed in an ethanol-water (90:10 v/v) mixture using the excess of NaHCO₃. The solvolytic mixtures of each investigated ester have been assayed by a RP-HPLC method using isocratic elution with methanol-water (75:25 v/v); flow rate 1 mL/min; detection at 238 nm; temperature 25 °C. Solvolytic rate constants were calculated from the obtained data. Geometry optimizations and charges calculations were carried out by Gaussian W03 software. A good correlation (R = 0.9924) was obtained between solvolytic rate constants and the polarity of the C-O2 bond of those esters. The established relation between solvolytic rate constant (K) and lipophilicity (cLogP) with experimental anti-inflammatory activity could be indicative for topical corticosteroid prodrug activation.