Density Functional Theory Predictions Research Articles

Cooperative Anion-π and C-H-Cl Interactions in Multifunctional Naphthalene-Based Receptors for Chloride Recognition: Cage-Size Modulation Through Substitution Patterns.

This study introduces a novel approach for chloride recognition utilizing multifunctional naphthalene-based receptors. By strategically modifying the substitution patterns on tetrafluoropyridines, a series of new receptors with customized cavities and enhanced binding capabilities were developed. Density functional theory (DFT) calculations and experimental studies combining NMR spectroscopy and mass spectrometry confirmed the efficacy of these receptors in capturing chloride ions. The relative chloride affinity order determined experimentally is in agreement with DFT predictions. The synergistic effect of anion-π and C-H…Cl interactions, mediated by the TFP groups, played a crucial role in achieving high binding affinity. This work provides valuable insights for designing future anion receptors with improved performance.

Assessment of Thermoelectric Properties of Bi2Se3: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Methodology

AbstractThermoelectric properties in topological insulator Bi2Se3 are explored with multifaceted strategies, i.e., hybrid functional with strain and artificial intelligence methodology. The assessment with the experimental band gap values recognizes the limitations of conventional functional and the effectiveness of screened hybrid functionals. A thorough investigation into the impact of biaxial and uniaxial strain on thermoelectric parameters uncovers distinctive behaviors in n‐type and p‐type Bi2Se3, providing insights into optimal strain conditions for improved performance. Furthermore, the studies on the role of topologically non‐trivial surface states (TNSS) in thermoelectric properties reveal that TNSS significantly dominate electronic transport. Dual scattering time approximation elucidates the segregation of thermoelectric transport contributions from bulk and surface states, highlighting the importance of controlling the relaxation time ratio for enhanced thermoelectric performance. Additionally, the prediction of thermoelectric properties using Random Forest and Neural Networks models showcase impressive agreement with density functional theory predictions across varying temperatures, offering a powerful tool for understanding complex temperature‐dependent trends in thermoelectric properties. In summary, this interdisciplinary study presents a unique approach to advancing the understanding and optimization of thermoelectric properties in Bi2Se3. It provides a comprehensive framework for tailoring material behavior for diverse thermoelectric applications.

Experimental Validation of Density Functional Theory Predictions on Structural Water Impact in Vanadium Oxide Cathodes for Zinc-Ion Batteries.

This study combines experimental methods with density flooding theory (DFT) calculations to investigate the enhancement of the electrochemical performance of vanadium oxide cathodes for aqueous zinc ion batteries (AZIBs) through strategic water content management. DFT predictions indicated that a moderate presence of structural water optimizes electrical conductivity and facilitates zinc ion diffusion. These theoretical insights are empirically validated by synthesizing AlVO-1.6 H2O using a hydrothermal method, which exhibited superior electrochemical properties. This material demonstrated an impressive initial capacity of 316 mAh g-1 at 0.2 A g-1, with robust capacity retention after extended cycling. Remarkably, even at an elevated current density of 10 A g-1, it sustains a capacity of 161.6 mAh g-1, while maintaining a capacity retention of 97.6% over 2000 cycles. The results confirm that adjusting the structural water content in vanadium oxides significantly boosts their electrochemical capabilities, aligning experimental outcomes with computational forecasts and showcasing a novel approach for developing high-performance cathodes in energy storage technologies.

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments.

Harnessing high potential benzothiazole chalcones against dengue virus NS5 protein: A multi-faceted theoretical study through molecular docking, ADME, and DFT

Chalcones bearing tetralone, indanone and benzothiazole cores were synthesized successfully using a general Claisen-Schmidt condensation protocol. The prepared compounds were purified and structurally analyzed by 1H, 13C NMR, and FT-IR techniques. A multi-faceted theoretical approach, combining Density Functional Theory (DFT), molecular docking, and ADME predictions, was employed to evaluate their therapeutic potential. DFT calculations at the B3LYP/def2-TZVP level revealed key electronic properties, with TD3 compound demonstrating the highest chemical reactivity. Molecular Electrostatic Potential (MEP) and Reduced Density Gradient (RDG) analyses provided insights into the compounds' non-covalent interactions and charge distributions. Molecular docking studies against the NS5 protein (PDB: 6KR2) showed superior binding affinities for all three compounds compared to the control ligand SAH, with TD3 exhibiting the lowest binding energy (−8.41 kcal/mol) and theoretical inhibition constant (689.31 nM). ADME predictions indicated favorable drug-like properties with concerns regarding aqueous solubility and potential P-glycoprotein interactions. Toxicity evaluations highlighted challenges, particularly in hepatotoxicity and carcinogenicity. The study identified TD3 as a promising lead compound for Dengue Virus NS5 inhibition, while also emphasizing the need for targeted modifications to address toxicity concerns. This research not only contributes to anti-dengue drug discovery efforts but also provides a robust methodological framework for the theoretical evaluation of similar small compounds in future investigations.

The DFT prediction of comprehensive properties of antiperovskites Mg6CX4 (X = S, Se, Te) and effects of defects and surface adsorption

It is important to search potential materials to meet ever-increasing demand in various fields. In order to understand the new hexagonal antiperovskites material Mg6CX4 (X=S, Se, Te), their mechanical, electronic and optical properties were studied based on density functional theory (DFT). The structural stability has been confirmed by the elastic constant criteria, formation energy, phonon dispersion spectra and ab-initio molecular dynamics (AIMD) simulation performed at 300K. Besides, the Poisson's ratios over 0.26 demonstrate the ductility of the studied materials. They all have direct electronic band structures with bandgaps of 0.977eV for Mg6CS4, 0.809eV for Mg6CSe4 and 1.274eV for Mg6CTe4, respectively. The calculated melting temperatures are all around 1000K, which indicates the potential for high-temperature application. Therefore, Mg6CTe4 fits most for absorber in solar cells due to its proper bandgap and dispersive band edge. The antiperovskites exhibit high and broad light absorption in visible and near ultraviolet region with absorption coefficient greater than 104cm-1, which inidcates a broad applicable prospect in optoelectronic field. Mg6CTe4 is also a candidate for efficient solid-state refrigeration due to its lowest thermal conductivity as 0.0046W/(K•m). The defect analysis suggest that oxygen defects are the most favorable and have complex effects on bandgaps. The high bandgap sensitivity to surface adsorbates implies potential application in gas detector.

Smartphone-Assisted Nanozyme Colorimetric Sensor Array Combined "Image Segmentation-Feature Extraction" Deep Learning for Detecting Unsaturated Fatty Acids.

Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UFAs by combining a smartphone-assisted colorimetric sensor array (CSA) based on MnO2 nanozymes with "image segmentation-feature extraction" deep learning (ISFE-DL). Density functional theory predictions were validated by doping experiments using Ag, Pd, and Pt, which enhanced the catalytic activity of the MnO2 nanozymes. A CSA mimicking mammalian olfactory system was constructed with the principle that UFAs competitively inhibit the oxidization of the enzyme substrate, resulting in color changes in the nanozyme-ABTS substrate system. Through linear discriminant analysis coupled with the smartphone App "Quick Viewer" that utilizes multihole parallel acquisition technology, oleic acid (OA), linoleic acid (LA), α-linolenic acid (ALA), and their mixtures were clearly discriminated; various edible vegetable oils, different camellia oils (CAO), and adulterated CAOs were also successfully distinguished. Furthermore, the ISFE-DL method was combined in multicomponent quantitative analysis. The sensing elements of the CSA (3 × 4) were individually segmented for single-hole feature extraction containing information from 38,868 images of three UFAs, thereby allowing for the extraction of more features and augmenting sample size. After training with the MobileNetV3 small model, the determination coefficients of OA, LA, and ALA were 0.9969, 0.9668, and 0.7393, respectively. The model was embedded in the smartphone App "Intelligent Analysis Master" for one-click quantification. We provide an innovative approach for intelligent and efficient qualitative and quantitative analysis of UFAs and other compounds with similar characteristics.

Density Functional Theory Prediction of Laser Dyes-Cucurbit[7]uril Binding Affinities.

Among a variety of diverse host molecules distinguished by specific characteristics, the cucurbit[n]uril (CB) family stands out, being widely known for the attractive properties of its representatives along with their increasingly expanding area of applications. The presented herewith density functional theory (DFT)-based study is inspired by some recent studies exploring CBs as a key component in multifunctional hydrogels with applications in materials science, thus considering CB-assisted supramolecular polymeric hydrogels (CB-SPHs), a new class of 3D cross-linked polymer materials. The research systematically investigates the inclusion process between the most applied representative of the cavitand family CB[7] and a series of laser dye molecules as guests, as well as the possible encapsulation of a model side chain from the photoanisotropic polymer PAZO and its sodium-containing salt. The obtained results shed light on the most significant factors that play a key role in the recognition process, such as binding mode, charge, and dielectric constant of the solvent. The observed findings provide valuable insights at a molecular level for the design of dye-CB[7] systems in various environments, with potential applications in intriguing and prosperous fields like photonics and material science.

Density Functional Theory Prediction of Room-Temperature Two-Dimensional Ferromagnetic Materials for Applications as Spintronic Devices

Density Functional Theory Prediction of Room-Temperature Two-Dimensional Ferromagnetic Materials for Applications as Spintronic Devices

Ce-doping-induced defect effects boosting H2 generation

Defect engineering holds significant promise for addressing the low conductivity and limited adsorption of reactant molecules in hydrogen generation catalysts. Herein, theoretical predictions based on the d-band center and electron localization function (ELF) calculations indicate that the introduction of Ce induces charge rearrangement, enhancing charge enrichment on Pt atoms. Inspired by these theoretical predictions, a series of PtCeOx/CoNi-LDH catalysts were fabricated for efficient hydrogen generation. The experimental results indicate that introducing metal defects will induce lattice expansion in CeOx doping, leading to the elongation of Pt–O bonds, which effectively reduces the bond energy of Pt–O bonds. The reduction in Pt–O bond energy facilitates the capture and adsorption of reactant molecules, thus exhibiting outstanding catalytic activity with a NaBH4 hydrogen generation rate (HGR) of 8992 mL min–1 gcat.–1. Density functional theory (DFT) calculations show that the adsorption-free energy of the rate-determining step in the PtCeOx/CoNi-LDH catalyst is ca. –3.59 eV, which is lower than the reaction barrier of PtOx/CoNi-LDH (–3.25 eV). This indicates that the PtCeOx/CoNi-LDH catalyst more effectively facilitates the formation of transition state intermediates. Guided by DFT predictions, the successful fabrication of high-efficiency PtCeOx/CoNi-LDH catalysts establishes a strong foundation for the design and development of future hydrogen generation catalysts.

Prediction of ADMET, molecular docking, DFT, and QSPR of potential phytoconstituents from Ambrosia maritima L. targeting xanthine oxidase

This study aimed to evaluate the xanthine oxidase (XO) inhibitory activities of seven compounds identified in the potent antioxidant ethyl acetate fraction of Ambrosia maritima L. using various computational tools. Physicochemical properties and density functional theory (DFT) analyses were performed. Subsequently, XO molecular docking was performed to identify the most promising leads. The water solubility of the compounds varied among highly soluble, moderately soluble, and soluble compounds. Four compounds were predicted to have no mutagenic or tumorigenic effect. All compounds were found to have lower binding energies than the oxypurinol standard, indicating their potential as XO inhibitors. The predicted inhibitory interactions, physicochemical properties, and DFT results suggest that two of the compounds (Kaempferol-3-O-glucoside and escululin) are promising drugs or drug leads for the treatment of certain diseases related to increased levels of XO.

Photothermal-enhanced magnetic Cd0.9Zn0.1S/CoB Schottky heterojunction toward photocatalytic hydrogen evolution

The construction of heterojunctions effectively suppresses the undesired recombination of photogenerated charge carriers. This work presents the rational design and preparation of a novel Cd0.9Zn0.1S/CoB (CZS/CoB) Schottky heterojunction tailored for photothermal-assisted photocatalytic hydrogen evolution with the aid of the predictions of density functional theory (DFT). Under visible light irradiation, the hydrogen evolution rate of CZS/CoB reaches 2.56 times that of pure CZS, due to the formed Schottky heterojunction and photothermal properties, effectively inhibiting electron-hole recombination and boosting the photocatalytic reaction. Notably, with the help of the excellent magnetic properties of CoB, the as-synthesized CZS/CoB exhibits excellent magnetic separation property, which is crucial for practical application. The charge transfer pathways in the Schottky heterojunction were confirmed by experimental results and verified by DFT calculation results. This study presents a feasible strategy for the design and preparation of magnetic recyclable Schottky Heterojunction photocatalyst for photocatalytic hydrogen evolution.

A S-scheme 0D/2D heterojunction formed by decorating Cd0.5Zn0.5S nanoparticles on Cu2MoS4 plates for efficient photocatalytic hydrogen generation

Rationally designing a Step-scheme (S-scheme) hetero-structured photocatalyst with high redox capability and separation efficiency of photoinduced carriers is considered as an attractive approach to address the shortcomings of single component and traditional hetero-structured photocatalyst. Herein, enlightened by the predictions of density functional theory (DFT), a unique S-scheme heterojunction photocatalyst was successfully fabricated by incorporating zero-dimensional (0D) Cd0.5Zn0.5S (CZS) on two-dimensional (2D) lamellar Cu2MoS4 (CMS) plates via a facile coprecipitation method, which substantially enhances the photocatalytic activity for hydrogen (H2) generation. CZS nanoparticles deposited on the surface of CMS nanoplates play a crucial role in creating highly intimate heterojunction interfaces and abundant exposed reaction sites, laying the foundation for improved photocatalytic performance. Furthermore, the significant difference in Fermi levels and band structures between CZS and CMS result in the formation of the internal electric field (IEF) and energy band bending at the interface of the heterojunction. This S-scheme charge transfer path enables 0D/2D CZS/CMS heterojunction possesses an exceptional capacity to maintain the robust redox potential and boosts the separation efficiency of light-induced carriers, as verified by DFT calculations and energy band structure analyses. As expected, the optimal heterojunction exhibits a satisfactory H2 generation rate of 13.1 mmol·g-1·h−1, which is nearly 7.7-fold higher than that of individual CZS. This study is expected to inspire the design of morphology-controlled hetero-structured photocatalyst with S-scheme charge transfer route for photocatalytic H2 generation.

Graphene/MoS2-assisted alum sludge electrode induces selective oxidation for organophosphorus pesticides degradation: Co-oxidation and detoxification mechanism

Designing an electrode that can generate abundant free radicals and 1O2, which can effectively degrade and detoxify organophosphorus pesticides (OPPs) through a co-oxidation pathway, is important. In this study, we prepared a electrode GO/MoS2@AS by supporting MoS2 on alum sludge (AS) under graphene oxide (GO) nanoconfinement. The results show that the dominant role of 1O2 at the cathode and •OHads at the anode for degradation, in addition to the involvement of 1O2 in the cathodic degradation mechanism, can be attributed to the abundant precursor •O2– and H2O2. Furthermore, calculations using density functional theory and toxicity prediction of products show that the energy (∆E) requirements of •OHfree to break the C–O bond of the pyridine ring and phosphate group are higher than that required for 1O2, and this non-radical oxidation plays a key role in detoxification. In contrast, accelerating ring opening and oxidation processes are attributed to radical oxidation. Above all, the cathodic detoxification is more effective than anodic detoxification. Three prevalent OPPs, chlorpyrifos, glyphosate, and trichlorfon, were degraded in the GO/MoS2@AS system by over 90 %, with mineralization rates of 76.66 %, 85.46 %, and 82.18 %, respectively. This study provides insights into the co-oxidation degradation and detoxification mechanism mediated by 1O2 and •OHfree.

Surface-Symmetry-Driven Dzyaloshinskii-Moriya Interaction and Canted Ferrimagnetism in Collinear Magnetoelectric Antiferromagnet Cr_{2}O_{3}.

Antiferromagnets are normally thought of as materials with compensated magnetic sublattices. This adds to their technological advantages but complicates readout of the antiferromagnetic state. We demonstrate theoretically the existence of a Dzyaloshinskii-Moriya interaction (DMI), which is determined by the magnetic symmetry classes of Cr_{2}O_{3} surfaces with an in-plane magnetic easy axis. The DMI explains a previously predicted out-of-plane magnetization at the nominally compensated surfaces of chromia, leading to a surface-localized canted ferrimagnetism. This is in agreement with magnetotransport measurements and with density functional theory predictions that further allow us to quantify the strength of DMI. The temperature dependence of the transversal resistance for these planes shows distinct behavior in comparison with that of the Cr_{2}O_{3} c plane, which we attribute to the influence of DMI. Our Letter provides a framework to analyze surface-driven phenomena in antiferromagnets, and motivates the use of nominally compensated chromia surfaces for antiferromagnetic spintronics and magnonics.

Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been shown to reversibly store hydrogen at room temperature, but the design of HEAs with appropriate thermodynamics is still challenging. To explore HEAs with appropriate hydride formation enthalpy, this study employs machine learning (ML), in particular, Gaussian process regression (GPR) using four different kernels by training with 420 datum points collected from literature and curated here. The developed ML models are used to predict the formation enthalpy of hydrides for the TixZr2-xCrMnFeNi (x = 0.5, 1.0 and 1.5) system, which is not in the training set. The predicted values by ML are consistent with data from experiments and density functional theory (DFT). The present study thus introduces ML as a rapid and reliable approach for the design of HEAs with hydride formation enthalpies of −25 to −39 kJ/mol for hydrogen storage at room temperature.

Strategically designing and fabricating nitrogen and sulfur Co-doped g-C3N4 for accelerating photocatalytic H2 evolution

Doping engineering is an effective strategy for graphitic carbon nitride (g-C3N4) to improve its photocatalytic hydrogen evolution reaction (HER) performance. In this work, a novel nitrogen and sulfur co-doped g-C3N4 (N, S-g-C3N4) is elaborately designed on the basis of theoretical predictions of first-principle density functional theory (DFT). The calculated Gibbs free energy of adsorbed hydrogen (ΔGH*) for N, S-g-C3N4 at the N-doping active sites is extremely close to zero (0.01 eV). Inspired by the theoretical predictions, the N, S-g-C3N4 is successfully fabricated through ammonia-rich pyrolysis synthesis strategy, in which ammonia is in-situ obtained by pyrolyzing melamine. Subsequent characterizations indicate that the N, S-g-C3N4 possesses high specific surface area, outstanding light utilization, good hydrophilicity, and efficient carrier transfer efficiency. Consequently, the N, S-g-C3N4 displays an extremely high H2 evolution rate of 8269.9 µmol g−1 h−1, achieves an apparent quantum efficiency (AQE) of 3.24%, and also possesses outsatnding durability. Theoretical calculations further demonstrate that N and S dopants can not only introduce doping energy level to reduce the band gap, but also induce charge redistribution to facilitate hydrogen adsorption, thus promoting the photocatalytic HER process. Moreover, femtosecond transient absorption (fs-TA) spectroscopy further corroborates the efficient photogenerated carrier transport of N, S-g-C3N4. This research highlights a promising and reliable strategy to achieve superior photocatalytic activity, and exhibits significant guidance for precise designing high-efficiency photocatalysts.

Structure of Graphene Grown on Cu(111): X-Ray Standing Wave Measurement and Density Functional Theory Prediction.

We report the quantitative adsorption structure of pristine graphene on Cu(111) determined using the normal incidence x-ray standing wave technique. The experiments constitute an important benchmark reference for the development of density functional theory approximations able to capture long-range dispersion interactions. Electronic structure calculations based on many-body dispersion-inclusive density functional theory are able to accurately predict the absolute measure and variation of adsorption height when the coexistence of multiple moiré superstructures is considered. This provides a structural model consistent with scanning probe microscopy results.

First-Principles and Experimental Investigation of ABO4 Zircons as Calcium Intercalation Cathodes.

Identifying next-generation batteries with multivalent ions, such as Ca2+ is an active area of research to meet the increasing demand for large-scale, renewable energy storage solutions. Despite the promise of higher energy densities with multivalent batteries, one of their main challenges is addressing the sluggish kinetics in cathodes that arise from stronger electrostatic interactions between the multivalent ion and host lattice. In this paper, zircons are theoretically and experimentally evaluated as Ca cathodes. A migration barrier as low as 113 meV is computationally found in YVO4, which is the lowest Ca2+ barrier reported to date. Low barriers are confirmed across 18 zircon compositions, which are related to the low coordination change and reduced interstitial site preference of Ca2+ along the diffusion pathway. Among the four materials (BiVO4, YVO4, EuCrO4, and YCrO4) that were synthesized, characterized, and electrochemically cycled, the highest initial capacity of 81 mA h/g and the most reversible capacity of 65 mA h/g were achieved in YVO4 and BiVO4, respectively. Despite the facile migration of multivalent ions in zircons, density functional theory predictions of the unstable, discharged structures at higher Ca2+ concentrations (Cax>0.25ABO4), the low dimensionality of the migration pathway, and the defect analysis of the B site atom can rationalize the limited intercalation observed upon electrochemical cycling.

Investigation of cathinone analogs targeting human dopamine transporter using molecular modeling

In a step towards understanding the structure–property relationship among Synthetic Cathinones (SCs), a combined methodology based on Density Functional Theory (DFT), Administration, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, docking and molecular dynamics simulations have been applied to correlate physicochemical descriptors of various SCs to their biological activity. The results from DFT and molecular docking studies correlate well with each other explaining the biological activity trends of the studied SCs. Quantum mechanical descriptors viz. polarizability, electron affinity, ionization potential, chemical hardness, electronegativity, molecular electrostatic potential, and ion interaction studies unravel the distinguishingly reactive nature of Group D (pyrrolidine substituted) and Group E (methylenedioxy and pyrrolidine substituted) compounds. According to ADMET analysis, Group D and Group E molecules have a higher probability of permeating through the blood–brain barrier. Molecular docking results indicate that Phe76, Ala77, Asp79, Val152, Tyr156, Phe320, and Phe326 constitute the binding pocket residues of hDAT in which the most active ligands MDPV, MDPBP, and MDPPP are bound. Finally, to validate the derived quantum chemical descriptors and docking results, Molecular Dynamics (MD) simulations are performed with homology-modelled hDAT (human dopamine transporter). The MD simulation results revealed that the majority of SCs remain stable within the hDAT protein’s active sites via non-bonded interactions after 100 ns long simulations. The findings from DFT, ADMET analysis, molecular docking, and molecular dynamics simulation studies complement each other suggesting that pyrrolidine-substituted SCs (Group D and E), specifically, MPBP and PVN are proven potent SCs along with MDPV, validating various experimental observations. Communicated by Ramaswamy H. Sarma