Theoretical Properties Research Articles

Soft-sphere continuum solvation models for nonaqueous solvents.

Solvation effects profoundly influence the characteristics and behavior of chemical systems in liquid solutions. The interaction between solute and solvent molecules intricately impacts solubility, reactivity, stability, and various chemical processes. Continuum solvation models gained prominence in quantum chemistry by implicitly capturing these interactions and enabling efficient investigations of diverse chemical systems in solution. In comparison, continuum solvation models in condensed matter simulation are very recent. Among these, the self-consistent continuum solvation (SCCS) and the soft-sphere continuum solvation models (SSCS) have been among the first to be successfully parameterized and extended to model periodic systems in aqueous solutions and electrolytes. As most continuum approaches, these models depend on a number of parameters that are linked to experimental or theoretical properties of the solvent, or that can be tuned based on reference data. Here, we present a systematic parameterization of the SSCS model for over 100 nonaqueous solvents. We validate the model's efficacy across diverse solvent environments by leveraging experimental solvation-free energies and partition coefficients from comprehensive databases. The average root means square error over all the solvents was calculated as 0.85 kcal/mol which is below the chemical accuracy (1 kcal/mol). Similarly to what has been reported by Hille et al. (J. Chem. Phys. 2019, 150, 041710.) for the SCCS model, a single-parameter model accurately reproduces experimental solvation energies, showcasing the transferability and predictive power of these continuum approaches. Our findings underscore the potential for a unified approach to predict solvation properties, paving the way for enhanced computational studies across various chemical environments.

Mechanism of Tunable Band Gap of Halide Cubic Perovskite CsPbBr3−xIx

Perovskites are organic-inorganic compounds with a crystal structure that revolutionize many optoelectronic applications, especially solar cells. The CsPbBr3−xIx, a perovskite, has garnered significant attention due to its tunable band gap and excellent photovoltaic properties. In this theoretical study, the structural, electronic, and optical properties of CsPbBr3−xIx are investigated through density functional theory calculations. The calculations reveal that the substitution of Br with I leads to a significant reduction in the band gap of CsPbBr3−xIx, resulting in improved light absorption properties. The obtained data show that the coexistence of Br and I ions in the structure creates an energy level similar to the shallow energy levels caused by doping at the R symmetry point in the band structure.

Biphenylite as Anode Materials for Alkali Metal Ion Batteries with Self-Enhanced Storage Mechanism.

In this work, we theoretically investigate the feasibility of biphenylite, the van der Waals layered bulk structure from experimental biphenylene network monolayers, as an anode material for alkali metal ions. The results indicate that the theoretical properties of Li, Na, and K in biphenylite are generally beyond those in graphite. Li-biphenylite exhibits a high specific capacity of 744 mAh·g-1, with a corresponding voltage range of 0.90-0.36 V, low diffusion barrier (<0.30 eV), and small volume change (∼9.9%), far exceeding those of Li-graphite. Moreover, a novel self-enhanced storage mechanism is observed and unveiled, in which the heavy intercalation of Li atoms (i.e., electron doping) induces puckered distortion of the nonhoneycomb carbon frameworks to enhance the intercalation ability and capacity of Li ion via a chemical activation of carbon frameworks. Possessing excellent anode performance beyond graphite, biphenylite is a promising "all-around" anode material candidate for alkali metal ion batteries, especially for lithium ion batteries.

Optimization of the corrosion inhibition performance of novel oxadiazole thione-based Schiff base for mild steel in HCl media using Doehlert experimental design

This study aimed to evaluate the effectiveness of 3-((dicyclohexylamino) methyl)-5-(4-((3, 4-dimethoxybenzylidene) amino) phenyl)-1, 3, 4-oxadiazole-2(3H)-thione (DMOT) in inhibiting the deterioration of mild steel in a 1 M HCl solution. Weight loss and electrochemical measurements were used to assess the inhibition performance and synthesis of DMOT. The characterization of DMOT was conducted through NMR and FTIR measurements, while SEM and AFM were used in the surface analysis. DFT calculations were used to investigate the chemical and theoretical properties of DMOT. The results of electrochemical measurements showed that the inhibition efficiency increased as the concentration of the inhibitor increased, with an optimal concentration reaching an inhibition efficiency of 99.17%. Polarization curves revealed that DMOT molecules behaved as a mixed-type inhibitor, with adsorption following the isotherm of Langmuir. The adsorption of DMOT on the corroded metal's surface was verified by SEM and AFM investigations. Using quantum chemical calculations, the quantum chemical properties of the DMOT were evaluated.

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation

The neutrino sector offers one of the most sensitive probes of new physics beyond the Standard Model of Particle Physics (SM). The mechanism of neutrino mass generation is still unknown. The observed suppression of neutrino masses hints at a large scale, conceivably of the order of the scale of a rand unified theory (GUT), which is a unique feature of neutrinos that is not shared by the charged fermions. The origin of neutrino masses and mixing is part of the outstanding puzzle of fermion masses and mixings, which is not explained ab initio in the SM. Flavor model building for both quark and lepton sectors is important in order to gain a better understanding of the origin of the structure of mass hierarchy and flavor mixing, which constitute the dominant fraction of the SM parameters. Recent activities in neutrino flavor model building based on non-Abelian discrete flavor symmetries and modular flavor symmetries have been shown to be a promising direction to explore. The emerging models provide a framework that has a significantly reduced number of undetermined parameters in the flavor sector. In addition, such a framework affords a novel origin of CP violation from group theory due to the intimate connection between physical CP transformation and group theoretical properties of non-Abelian discrete groups. Model building based on non-Abelian discrete flavor symmetries and their modular variants enables the particle physics community to interpret the current and anticipated upcoming data from neutrino experiments. Non-Abelian discrete flavor symmetries and their modular variants can result from compactification of a higher-dimensional theory. Pursuit of flavor model building based on such frameworks thus also provides the connection to possible UV completions: in particular, to string theory. We emphasize the importance of constructing models in which the uncertainties of theoretical predictions are smaller than, or at most compatible with, the error bars of measurements in neutrino experiments. While there exist proof-of-principle versions of bottom-up models in which the theoretical uncertainties are under control, it is remarkable that the key ingredients of such constructions were discovered first in top-down model building. We outline how a successful unification of bottom-up and top-down ideas and techniques may guide us towards a new era of precision flavor model building in which future experimental results can give us crucial insights into the UV completion of the SM.

Extropy estimation of Weibull distribution under upper records

Recently, extropy has gained interest by the academic researchers. This work explores the features of parametric and non-parametric estimators based on upper record values under the a two-parameter Weibull distribution. We apply the Markov Chain Monte Carlo (MCMC) method to provide a Bayesian estimator. A considerable number of theoretical properties of the procedures are determined.

Carbon Nanotube-Directed 7GPa Heterocyclic Aramid Fiber and Its Application in Artificial Muscles.

Poly(p-phenylene-benzimidazole-terephthalamide) (PBIA) fibers with excellent mechanical properties are widely used in fields that require impact-resistant materials such as ballistic protection and aerospace. The introduction of heterocycles in polymer chains increases their flexibility and makes it easier to optimize the fiber structure. However, the inadequate orientation of polymer chains is one of the main reasons for the large difference between the measured and theoretical mechanical properties of PBIA fibers. Herein, carbon nanotubes (CNTs) are selected as an orientation seed. Their structural features allow CNTs to orient during the spinning process, which can induce an orderly arrangement of polymers and improve the orientation of the fiber microstructure. To ensure the complete 1D topology of long CNTs (≈10µm), PBIA is used as an efficient dispersant to overcome dispersion challenges. The p-CNT/PBIA fibers (10µm single-walled carbon nanotube 0.025wt%) exhibit an increase of 22% in tensile strength and 23% in elongation, with a maximum tensile strength of 7.01±0.31GPa and a reinforcement efficiency of 893.6. The artificial muscle fabricated using CNT/PBIA fibers exhibits a 34.8% contraction and a 25% lifting of a 2kg dumbbell, providing a promising paradigm for high-performance organic fibers as high-load smart actuators.

A green ultrasound-promoted synthesis, experimental, theoretical, and nonlinear optical properties studies of benzylidenehydrazono thiazolidin-4-one derivative

A green ultrasound-promoted synthesis, experimental, theoretical, and nonlinear optical properties studies of benzylidenehydrazono thiazolidin-4-one derivative

First Principle Study of Structural, Electronic, Optical Properties of Co-Doped ZnO

In this theoretical study, the electronic, structural, and optical properties of copper-doped zinc oxide (CZO) were investigated using the full-potential linearized enhanced plane wave method (FP-LAPW) based on the density functional theory (DFT). The Tran–Blaha modified Becke–Johnson exchange potential approximation (TB-mBJ) was employed to enhance the accuracy of the electronic structure description. The introduction of copper atoms as donors in the ZnO resulted in a reduction in the material’s band gap from 2.82 eV to 2.72 eV, indicating enhanced conductivity. This reduction was attributed to the Co-3d intra-band transitions, primarily in the spin-down configuration, leading to increased optical absorption in the visible range. The Fermi level of the pure ZnO shifted towards the conduction band, indicating metal-like characteristics in the CZO. Additionally, the CZO nanowires displayed a significant blue shift in their optical properties, suggesting a change in the energy band structure. These findings not only contribute to a deeper understanding of the CZO’s fundamental properties but also open avenues for its potential applications in optoelectronic and photonic devices, where tailored electronic and optical characteristics are crucial. This study underscores the significance of computational techniques in predicting and understanding the behavior of doped semiconductors, offering valuable insights for the design and development of novel materials for advanced electronic applications.

Space-Confined Synthesis of Monolayer Graphdiyne in MXene Interlayer.

Graphdiyne (GDY) is an artificial carbon allotrope that is conceptually similar to graphene but composed of sp- and sp2 -hybridized carbon atoms. Monolayer GDY (ML-GDY) is predicted to be an ideal 2D semiconductor material with a wide range of applications. However, its synthesis has posed a significant challenge, leading to difficulties in experimentally validating theoretical properties. Here, it is reported that in situ acetylenic homocoupling of hexaethynylbenzene within the sub-nanometer interlayer space of MXene can effectively prevent out-of-plane growth or vertical stacking of the material, resulting in ML-GDY with in-plane periodicity. The subsequent exfoliation process successfully yields free-standing GDY monolayers with micrometer-scale lateral dimensions. The fabrication of field-effect transistor on free-standing ML-GDY makes the first measurement of its electronic properties possible. The measured electrical conductivity (5.1 × 103 S m-1 ) and carrier mobility (231.4 cm2 V-1 s-1 ) at room temperature are remarkably higher than those of the previously reported multilayer GDY materials. The space-confined synthesis using layered crystals as templates provides a new strategy for preparing 2D materials with precisely controlled layer numbers and long-range structural order.

Learning‐based model predictive control under value iteration with finite approximation errors

AbstractThis paper proposes a novel learning‐based model predictive control (LMPC) scheme for discrete‐time nonlinear systems. It overcomes the challenge of manually designing the terminal conditions for traditional MPC and enhances the control performance. The scheme employs the value iteration (VI) in reinforcement learning (RL), and autonomously designs the terminal cost by iteratively performing value function learning and policy update under known dynamics and constraints. In contrast to the existing schemes that combine RL with MPC, the proposed scheme explicitly considers the approximation errors in each iteration. Further, a rigorous theoretical analysis is provided, including the convergence of VI, the stability and performance of the closed‐loop system. In addition, the influences of the prediction horizon and the initial terminal cost on performance are also investigated. Simulation results of a linear system verify the theoretical properties of the LMPC and show that it achieves (near‐)optimal performance. Moreover, its unique superiority over traditional MPC is fully demonstrated in a nonholonomic vehicle regulation example.

Mapping material-property space of cellular metamaterials under uncertainty

Cellular mechanical metamaterials (CMMs) are assemblies of periodic representative volume elements which can be engineered to exhibit unique mechanical properties. Recent advances in additive manufacturing (AM) have enabled us to fabricate sophisticated architected materials with high precision. To increase and diversify applications in both science and engineering practice, the rapid development of fabrication technologies necessities a novel and effective algorithm to outline a theoretical property space for material design problems, allowing designers to acknowledge the limitations of material performance, and to make informed trade-off between target objectives and competing industrial requirements. Inspired by biological evolution, the proposed work addresses a methodology for intuitively mapping material-property spaces of CMMs by using the genetic algorithm as a sampling algorithm, consisting selection of objective properties and stochastic search of property points. It approximates the property space more faithfully and comprehensively than traditional mapping approaches. Considering the manufacturing defects of the AM, uncertainties in properties are quantified via the deep learning method. Variations of properties induced by the defects are illustrated as stochastic boundaries of the property space.

Optimizing the Local Charge of Graphene via Iron Doping to Promote the Adsorption of Formaldehyde Molecules—A Density Functional Theory Study

Formaldehyde is a colorless, pungent, and highly volatile toxic gas known for its detrimental effects on the brain, respiratory, and nervous systems. The adsorption method emerges as an effective approach for detecting and mitigating formaldehyde gas, with the adsorption material serving as its core component. Graphene, a two-dimensional nanomaterial with remarkable properties, exhibits enhanced adsorption capabilities when subjected to metal doping, which alters its local geometric and charge characteristics. In this investigation, theoretical first-principles density functional technology was employed to optimize the efficiency of Fe-doped graphene in formaldehyde adsorption. The calculated adsorption bond length and energy were used to determine the type of adsorption. Then, the calculated Bader charge, density of states (partial density of states), and differential valence charge density distribution were used to analyze the electron transfer process before and after adsorption. Finally, the theoretical optical properties analysis result was applied to analyze the potential of Fe-doped graphene for formaldehyde detection. The findings indicated that Fe-doped graphene constitutes a viable and stable doping structure, accompanied by a notable shift in valence charge distribution around the doped iron atom. This altered charge distribution facilitated the chemical adsorption process, leading to reduced adsorption spacing and increased adsorption energy. Throughout the chemical adsorption process, there was evident charge transfer between carbon (formaldehyde) and iron atoms, as well as between oxygen (formaldehyde) and iron atoms. The formation of adsorption bonds primarily involved the p-orbital electrons of carbon and oxygen atoms, along with the p- and d-orbital electrons of iron atoms. Ultimately, the Fe-doped graphene material exhibited promising applications in the realm of formaldehyde molecular detection, marked by significant theoretical disparities in optical properties before and after the adsorption process.

Preface

It is a matter of great pleasure for the organizers to bring out the Conference proceedings of the 6th National Conference on Advanced Materials and Radiation Physics (AMRP-2023) in Journal of Physics: Conference Series. The 6th National Conference on Advanced Materials and Radiation Physics (AMRP-2023) was held during May 18-19, 2023 through HYBRID Mode at Sant Longowal Institute of Engineering and Technology, Longowal, Punjab (INDIA). The AMRP-2023 conference is an important event that brings academics and researchers from all over India to share their professional experiences, expand their professional knowledge and receive updates on the latest advances in the field of advanced materials and radiation Physics.Materials science is an interdisciplinary field which deals with the discovery and design of new materials involving the study of materials through the materials paradigm (synthesis, structure, properties and performance). The subject covers a broad spectrum of materials science research including functional materials, synthesis and processing, theoretical analyses, characterization and properties of materials. Emphasis is also placed on the interdisciplinary nature of materials science and issues at the forefront of the field, such as energy and environmental issues, as well as medical and bioengineering applications are also discussed.The Radiation Physics is highly multidisciplinary in nature and is now the backbone of the nuclear medical diagnostics. The radiation physics research covers many areas of radiation imaging techniques, fundamental studies of semiconductor radiation detectors, detector materials, radiation interaction with matter and medical physics which aims to improve knowledge and methods in the field of radiation physics and radiation dosimetry, in order to develop new radiation measurement instruments and methods for applications in research, industry and medicine.In this conference more than 150 researchers from reputed universities/institutes have shown interest in hybrid mode participation. The conference included one key note address, four plenary talks, six invited talks, and presentation of more than 130 contributed papers via online & offline mode on different aspects of the conference theme. We extend our sincere gratitude to all the authors who submitted their papers to the AMRP conference. We also like to express our great appreciation to the Keynote, plenary and invited speakers.We are thankful to Institute administration for extending all support and motivation to organize this conference. On behalf of the Conference Organizing Committee, we would like to thank the referees for their efficient and thoughtful actions. We are grateful to the members of the Organising Committee for their efforts in making and shaping the program for AMRP 2023. Particularly, we acknowledge the publishing support from the members of Journal of Physics: Conference Series. We hope that the future AMRP will be as successful and stimulating, as indicated with the contributions presented in this volume.Organising Committee AMRP-2023List of Organising Committee is available in this Pdf.

Hierarchical Optimization-Derived Learning.

In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks. Although having achieved relatively satisfying practical performance, there still exist fundamental issues in existing ODL methods. In particular, current ODL methods tend to consider model constructing and learning as two separate phases, and thus fail to formulate their underlying coupling and depending relationship. In this work, we first establish a new framework, named Hierarchical ODL (HODL), to simultaneously investigate the intrinsic behaviors of optimization-derived model construction and its corresponding learning process. Then we rigorously prove the joint convergence of these two sub-tasks, from the perspectives of both approximation quality and stationary analysis. To our best knowledge, this is the first theoretical guarantee for these two coupled ODL components: optimization and learning. We further demonstrate the flexibility of our framework by applying HODL to challenging learning tasks, which have not been properly addressed by existing ODL methods. Finally, we conduct extensive experiments on both synthetic data and real applications in vision and other learning tasks to verify the theoretical properties and practical performance of HODL in various application scenarios.

Asymmetric Dependence in Hydrological Extremes

AbstractExtremal dependence describes the strength of correlation between the largest observations of two variables. It is usually measured with symmetric dependence coefficients that do not depend on the order of the variables. In many cases, there is a natural asymmetry between extreme observations that cannot be captured by such coefficients. An example for such asymmetry is large discharges at an upstream and a downstream stations on a river network: an extreme discharge at the upstream station will directly influence the discharge at the downstream station, but not vice versa. Simple measures for asymmetric dependence in extreme events have not yet been investigated. We propose the asymmetric tail Kendall's τ as a measure for extremal dependence that is sensitive to asymmetric behavior in the largest observations. It essentially computes the classical Kendall's τ but conditioned on the extreme observations of one of the two variables. We show theoretical properties of this new coefficient and derive a formula to compute it for existing copula models. We further study its effectiveness and connections to causality in simulation experiments. We apply our methodology to a case study on river networks in the United Kingdom to illustrate the importance of measuring asymmetric extremal dependence in hydrology. Our results show that there is important structural information in the asymmetry that would have been missed by a symmetric measure. Our methodology is an easy but effective tool that can be applied in exploratory analysis for understanding the connections among variables and to detect possible asymmetric dependencies.

Scheduling Most Valuable Committees for the Sharded Blockchain

In a sharded blockchain, transactions are processed by a number of parallel committees. Thus, the transaction throughput can be largely boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Moreover, the heterogeneous processing capabilities of different committees also result in imbalanced consensus latency. Such imbalanced two-phase latency brings a large cumulative age to the transactions pending in transaction pool. Consequently, the blockchain throughput can be significantly degraded. We believe that a good committee-scheduling strategy can reduce the cumulative age of transactions, and thus benefit the throughput. However, we have not yet found a committee-scheduling mechanism that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transactions’ throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. We then rigorously analyze three theoretical properties of the proposed algorithm, including the theoretical convergence time, the probability of committees’ failure due to Sybil attacks, as well as the performance perturbation brought by committees’ offline events. Finally, we evaluate the proposed algorithm using the dataset of real-world blockchain transactions. The simulation results demonstrate that the proposed SE algorithm outperforms other baselines in terms of system utility, the valuable degree of yielded solutions, latency, and throughput performance.

GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.

Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.

Doubly multiplicative error models with long- and short-run components

We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating long-run, respectively, short-run features in the data. Three such models are considered, the Spline-MEM which fits a spline to the slow-moving pattern of volatility, the Component-MEM, which uses daily data for both components, and the MEM-MIDAS, which exploits the logic of MIxed-DAta Sampling (MIDAS) methods. The parameters are estimated by the Generalized Method of Moments (GMM), for which we establish the theoretical properties and the equivalence with the Quasi Maximum Likelihood (QML) estimator under a Gamma assumption. The empirical application involves the S&P 500, NASDAQ, FTSE 100, DAX, Nikkei and Hang Seng indices: irrespective of the market, the DMEM’s generally outperform the HARand other relevant GARCH-type models.

ROCCO: a robust method for detection of open chromatin via convex optimization.

Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively. We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology. Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a stand-alone binary utility using pip/PyPI.