Structural Descriptors Research Articles

Representing and Quantifying Conformational Changes of Kinases and Phosphatases Using the TSR-Based Algorithm

Protein kinases and phosphatases are key signaling proteins and are important drug targets. An explosion in the number of publicly available 3D structures of proteins has been seen in recent years. Three-dimensional structures of kinase and phosphatase have not been systematically investigated. This is due to the difficulty of designing structure-based descriptors that are capable of quantifying conformational changes. We have developed a triangular spatial relationship (TSR)-based algorithm that enables a unique representation of a protein’s 3D structure using a vector of integers (keys). The main objective of this study is to provide structural insight into conformational changes. We also aim to link TSR-based structural descriptors to their functions. The 3D structures of 2527 kinases and 505 phosphatases are studied. This study results in several major findings as follows: (i) The clustering method yields functionally coherent clusters of kinase and phosphatase families and their superfamilies. (ii) Specific TSR keys are identified as structural signatures for different types of kinases and phosphatases. (iii) TSR keys can identify different conformations of the well-known DFG motif of kinases. (iv) A significant number of phosphatases have their own distinct DFG motifs. The TSR keys from kinases and phosphatases agree with each other. TSR keys are successfully used to represent and quantify conformational changes of CDK2 upon the binding of cyclin or phosphorylation. TSR keys are effective when used as features for unsupervised machine learning and for key searches. If discriminative TSR keys are identified, they can be mapped back to atomic details within the amino acids involved. In conclusion, this study presents an advanced computational methodology with significant advantages in not only representing and quantifying conformational changes of protein structures but also having the capability of directly linking protein structures to their functions.

Understanding melting behavior of aluminum clusters using machine learned deep neural network potential energy surfaces.

Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature abinitio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48-342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid-solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147-342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones.

Analysis of octane isomer properties via topological descriptors of line graphs

Currently, graph-based molecular structure descriptors are frequently used in QSAR (Quantitative Structure-Activity Relationship) and QSPR (Quantitative Structure-Property Relationship) modeling of the properties of chemical compounds. Octane isomers have an important role in the testing of such relationships. In this study, we compute some well-known topological descriptors of the molecular graphs of isomeric octanes and their line graphs, both indices and coindices. Then we analyze the correlation coefficients between the physicochemical properties of the octane isomers and some well-known topological indices and their coindices of the molecular graphs and the line molecular graphs of the octane isomers, to find the particular topological descriptors that have the best predictive values.

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections.

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed Cleavage High-Throughput Assay (CHiTA), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1600 previously reported putative twisters and ∼1000 new candidate twisters. The new candidates were identified computationally in ∼1000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

Prediction of Melting and Solid Phase Transitions Temperatures and Enthalpies for Triacylglycerols using Artificial Neural Networks

Triacylglycerols (TAG) are the main components of vegetable oils and any attempt to simulate vegetable oils processes will demand knowledge of their properties. However, experimental values are scarce, considering that several TAG in their pure forms are unavailable or too expensive for experimental measurements. On the other hand, correlating physical properties with TAG molecular structure is not simple. TAG is a molecule composed of 3 fatty acids (FA) esterified to a glycerol (GL) backbone, making properties dependent on carbon number (CN) of each FA, number of unsaturations (UN) of each FA, and position of the FA in the GL backbone. Few models are available in literature for prediction of TAG melting properties, with a special attention to melting temperature (Tfus) and enthalpy (ΔHfus) and solid-solid transition properties of the TAG polymorphic forms. Wesdorp's, Moorthy's et al. and Zeberg-Mikkelsen and Stenby's works present models based on the Group-Contribution theory nowadays used, despite some flaws, particularly considering the polymorphic transitions. Therefore, this work was aimed at evaluating Artificial Neural Network (ANN) models for prediction of TAG's Tfus and ΔHfus (β-form) as well as temperature and enthalpy transitions of molecule polymorphic forms (α and β’). Database was composed of temperature and enthalpy experimental data from literature. For each TAG, 7 input data were provided: total CN, as well as CN and UN at sn-1, 2 and 3 TAG position. The Multilayer Perceptron Feed Forward (MPL) model was used, and the topology was evaluated for number of hidden layers (HL), number of neurons (NN) and activation function at each hidden layer, and convergence algorithm. Number of HL and NN was screened by using a Central Composite Rotatable Design (CCRD). Models were further evaluated by Explainable Artificial Intelligence (XAI) and feature evaluation strategies. Architectures showed a significant higher accuracy for calculation and prediction of TAG's melting properties of the 3 polymorphic forms, with R2 higher to 0.91 for all databases when compared to literatures’ models (excepted for the prediction of the melting temperature of the β form, where Wesdorp's model presented a better predictive ability, despite great similarity). Good results were probably related to the well-defined physicochemical relationship between input (molecular structure descriptors) and output (melting properties), that could be described by XAI evaluation. This is an important advantage considering the improvement of the performance of process and products design including TAG molecules.

High-throughput screening of halide solid-state electrolytes for all-solid-state Li-ion batteries through structural descriptor

All-solid-state Li-ion batteries (ASSLIBs) have attracted great attention due to their intrinsic safety and high potential energy density. To realize ASSLIBs, the development of solid-state electrolytes (SSEs) with favorable electrochemical stability and high Li-ion conductivity is of utmost significance. Compared to trial-and-error searches, theoretical computation-based methods are recognized as a powerful tool for accelerating the exploration of SSEs. However, the development of appropriate descriptors is often the first hurdle toward implementing accurate and meaningful screening process. Here we quantified the structural properties pertaining to low Li-ion migration barriers and performed a high-throughput screening of 3119 Li-containing halides obtained from the Materials Project (MP) database to search for promising SSEs. After excluding previously reported SSEs, three candidate materials, Li4ZrF8, Li3ErBr6, and Li2ZnI4 with crystal structure exhibiting 3D diffusion pathway were obtained. Subsequent theoretical calculations of these materials were performed to evaluate their thermodynamic stability, chemical/electrochemical stability, and ionic conductivity. The three halide materials appeared to be promising SSE candidates due to their robust thermodynamical stability against cathodes, and high ionic conductivity. This research provides a new insight and a systematic quantitative understanding of the relationship between the structural properties and Li-ion migration barriers, which can motivate future computational studies on new fast-ion conductors and accelerate the discovery of high ionic conductivity SSEs for the use in ASSLIBs.

Analysis of oral and inhalation toxicity of per- and polyfluoroalkylated organic compounds in rats and mice using multivariate QSAR

ABSTRACT Per- and polyfluoroalkylated organic compounds (PFAs) are versatile compounds extensively used in global industries. However, they are also persistent organic pollutants (POPs). This study aimed to develop new models for assessing oral and inhalation toxicity in rat and mice models. A set of 407 PFAs from the literature was divided into four groups based on the endpoints of interest. The models were constructed using only 2D structure descriptors derived from SMILES strings. The resulting models showed a strong statistical quality for all endpoints. They present an applicability domain (AD) that ensures good reliability, and provided meaningful interpretation, which are partially supported by existing literature. Consequently, these models are valuable for understanding how PFAs exert their toxic effect on mammals and for predicting the risk associated with these significant industrial chemical agents.

Research progress on the theoretical design of catalysts based on structure descriptors

Research progress on the theoretical design of catalysts based on structure descriptors

Advancing understanding of composition–structure–luminescent properties in laser glass through machine learnings

AbstractRare‐earth (RE)‐doped laser glasses meet urgent needs in national security and scientific fields, and their optimization has garnered extensive attention. However, the design of these laser glasses often relies excessively on trial‐and‐error experimentation, leading to significant costs and a lack of scientific guidance. Herein, we propose an integrated method that combines structural descriptors determined from molecular dynamics simulations, a self‐constructed luminescent database, and a machine learning algorithm to establish the composition–structure–luminescent property (CSLP) relationship. Using an Nd3+‐doped commercial silicate laser glass system as an example, the effectiveness of this approach has been demonstrated. The developed CSLP model enables highly accurate predictions of spectral properties, achieving a determination coefficient (R2) greater than 0.94, based on eight structural descriptors. The importance of different structural descriptors on spectral characteristics is ranked and thoroughly discussed, revealing an intrinsic relationship between the first and second coordination shells around RE ions and luminescent behaviors. Furthermore, the generic structural descriptors identified in the CSLP model can be extrapolated to other systems involving different network formers (e.g., silicate and phosphate) and modifier cations (e.g., Li, Na, K, Ba, and Ca). This capability facilitates the design of laser glasses tailored to specific targets, such as large emission cross‐sections, extended lifetimes, or reduced quenching effects.

Oxidation Potential of 2,6-Dimethyl-1,4-dihydropyridine Derivatives Estimated by Structure Descriptors

Linear relationships, expressing the electrochemical properties of molecules as functions of structure, give insight into the associated electrochemical process and are a tool for prediction. Many biological activities rely on water-based dissociation, making electrochemical properties a bridge between structure and activity. Motivated by a previous study, a replica is made here on a different dataset in order to validate/invalidate the previously reported results. There are several methods for obtaining structure-based descriptors. Some of the methods have been devised to account for molecular topology, some to account for molecular geometry, and others to account for both. Two methods are involved here to derive structure-based descriptors and further obtain structure–property relationships (FMPI and ChPE). In order to express structure descriptors, both FMPI and ChPE express first the topology of the molecule, using the heavy atoms identity matrix and the heavy atoms adjacency matrix, both square symmetric matrices in the belief that symmetry is one major factor of molecular stability. A set of 2,6-dimethyl-1,4-dihydropyridine derivatives with oxidation peak potentials and coulometrically determined number of electrons experimental data is subjected to the search for structure–activity relationships. Even if the 2,6-dimethyl-1,4-dihydropyridine is a symmetric compound (of Cs point group), their derivatives are generally not symmetric (9 out of 24 are asymmetric). The dataset is subjected to descriptive and inferential statistics in order to filter out the most relevant structure–activity relationship. The geometry is built using three levels of theory (one from molecular mechanics and two others from density functionals, of which one accounts for the interaction with water as solvent). One challenge of picking one out of two reported measured values is dealt with by calculating the likelihood associated with the two choices. Relevant structure–activity models are extracted and discussed. The use of in vivo (in water, SM8 model) models in geometry optimization (from MMFF94 and B3LYP and to M06 + Water SM8) results in a precision gain, but this is, in most of the cases, not statistically significant, and this can be considered a negative result.

Determining the chemical ordering in nanoalloys by considering atomic coordination types.

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.

Important structural parameter for curvature effect of TM-N4 embeded C70 fullerenes as electrocatalysts for CO2 reduction interpreted with machine learning and first-principles calculations

In this work, TM-N4 sites embedded on curved C70 Fullerenes (TMN4(n)@C64, n = 1–5 which denote the doping positions and TM = Sc-Zn) were designed as efficient catalysts for electrochemical CO2 reduction reaction (ECO2RR). With interpretable machine learning (ML) and first-principles calculations, the performance of TM-N4 sites as possible electrocatalysts for ECO2RR under the curvature effect were revealed. After the screen procedure of catalyst stability, CO2 activation and selectivity of 50 designed systems, VN4(3), CrN4(3), MnN4(2), FeN4(2, 3, 5)@C64 were identified as high performance catalysts for ECO2RR to CH4, with limiting potential of -0.41 V, -0.26 V, -0.28 V, -0.35 V, -0.25 V and -0.17 V respectively, outperform the TM-N4 catalysts on the flat surface. A key structural descriptor K which can reflect the curvature effect of TM-N4 sites on the curved surface was identified with ML models. The gradient boosting regression (GBR) and sure independence screening and sparsifying operator (SISSO) algorithm reveal that the structural parameter K have strong association with the CO2 adsorption and significant influence on the selectivity of catalysts. Interestingly, the structural parameter K can influence more the binding of O-bonded intermediates than C-bonded intermediates. With the GBR algorithm, an effective model for the prediction of the limiting potential for C1 products were obtained. This study clarified the curvature effect of TM-N4 supported on the curved surface, and provides valuable guidance for the designing of TM-N4 single atom catalysts for ECO2RR on curved surface.

Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E th, maximum sputtering energy E max and maximum sputtering yield SY max. In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors (R 2 evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R 2 of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning

The pervasive presence of microplastics (MPs) in aquatic systems facilitates the transmission of antibiotic resistance genes (ARGs), thereby posing risks to ecosystems and human well-being. However, owing to variations in environmental backgrounds and the limited scope of research subjects, studies on ARGs in MPs lack unified conclusions, particularly regarding whether different types of MPs selectively promote ARG enrichment. Analysing large-scale datasets can better encompass broad spatiotemporal scales and diverse samples, facilitating a more extensive exploration of the complex ecological relationships between MPs and ARGs. The present study integrated existing metagenomic datasets to conduct a comprehensive risk assessment and comparative analysis of resistance groups across various MPs. In addition, we endeavoured to elucidate potential associations between ARGs and bacterial taxa, as well as MP structural features, using machine learning (ML) methods. The findings of our research highlight the pivotal role of MP type in shaping plastispheres, accounting for 9.56 % of the biotic variation (Adonis index) and explaining 18.59 % of the ARG variance. Compared to conventional MPs, biodegradable MPs, such as polyhydroxyalkanoates (PHA) and polylactic acid (PLA), exhibit lower species uniformity and diversity but pose a higher risk of ARG occurrence. These ML approaches effectively forecasted ARG abundance by using the bacterial taxa and molecular structure descriptors (MDs) of MPs (average R2tra = 0.882, R2test = 0.759). Feature analysis showed that MDs associated with lipophilicity, solubility, toxicity, and surface potential significantly influenced the relative abundance of ARGs in the plastispheres. The interpretable multiple linear regression (MLR) model, particularly notable, elucidated a linear relationship between bacterial genera and ARGs, offering promise for identifying potential ARG hosts. This study offers novel insights into ARG dynamics and ecological risks within aquatic plastispheres, highlighting the importance of comprehensive MP monitoring initiatives.

First Principle Studies of Electronic Properties, Global Reactivity Descriptors, and Molecular Docking of Olivacine Drug

AbstractOlivacine, a semisynthetic isomer of ellipticine, belongs to the family of natural alkaloids; which possess analgesic, antibacterial, and antipyretic properties. It is a model anticancer drug acting as topoisomerase II inhibitor. The mechanism of action and antineoplastic properties of olivacine are ascribed to its intercalative binding into DNA helices. The present paper reports DFT investigation of the molecular structure, electronic properties, and global reactivity descriptors of the drug. Frontier orbitals (HOMO and LUMO) and MEP surface of the olivacine molecule have been examined. Furthermore, inhibition activity and binding sites of olivacine with kinase protein (PDB Id: 3OG7) have been explored by molecular docking technique. Results have been used to elucidate physico‐chemical aspects and preferred binding patterns of the olivacine drug.

Machine-learned coarse-grained potentials for particles with anisotropic shapes and interactions

Computational investigations of biological and soft-matter systems governed by strongly anisotropic interactions typically require resource-demanding methods such as atomistic simulations. However, these techniques frequently prove to be prohibitively expensive for accessing the long-time and large-length scales inherent to such systems. Conversely, coarse-grained models offer a computationally efficient alternative. Nonetheless, models of this type have seldom been developed to accurately represent anisotropic or directional interactions. In this work, we introduce a straightforward bottom-up, data-driven approach for constructing single-site coarse-grained potentials suitable for particles with arbitrary shapes and highly directional interactions. Our method for constructing these coarse-grained potentials relies on particle-centered descriptors of local structure that effectively encode dependencies on rotational degrees of freedom in the interactions. By using these descriptors as regressors in a linear model and employing a simple feature selection scheme, we construct single-site coarse-grained potentials for particles with anisotropic interactions, including surface-patterned particles and colloidal superballs in the presence of non-adsorbing polymers. We validate the efficacy of our models by accurately capturing the intricacies of the potential-energy surfaces from the underlying fine-grained models. Additionally, we demonstrate that this simple approach can accurately represent the contact function (shape) of non-spherical particles, which may be leveraged to construct continuous potentials suitable for large-scale simulations.

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

High-throughput screening of efficient graphdiyne supported transition metal single atom toward water electrolysis and oxygen reduction

Efficient single-atom electrocatalysts show great potential for application in the field of renewable energy. In this work, dispersion-corrected density functional theory (DFT) calculations based on high-throughput screening were used to identify effective graphdiyne (GDY) supported single-atom electrocatalysts (SAECs) for water electrolysis and the oxygen reduction reaction (ORR). The solvation effect on the electrocatalytic performance of the catalysts was carefully discussed. A general design principle was established to evaluate the activities of TM@GDY SAECs for ORR and oxygen evolution reaction (OER). ΔGOH* could serve as the sole descriptor for the onset potential of OER and ORR. Additionally, a universal structural descriptor φ, which is strongly related to ΔGOH*, was identified. This descriptor only includes intrinsic features such as the number of electrons in d orbitals and elemental electronegativity. Consequently, a structure-activity volcano was plotted. Combined with the electronic properties calculations, the mechanism behind the relationship between ΔGOH* and descriptor φ was deeply analyzed. Among the investigated candidates, TM@GDY (TM = Ni, Pt, Pd, Cu, Co, Rh, and Ag) are potential bifunctional electrocatalysts for OER and ORR. Three non-noble metal-based SAECs (Ni@GDY, Co@GDY, and Cu@GDY) and two noble metal-based SAECs (Rh@GDY and Ag@GDY) are potential trifunctional electrocatalysts for OER, ORR, and the hydrogen evolution reaction.

Synthesis, Spectroscopic, SC-XRD/DFT and Nonlinear Optical Response of New Halogenated Schiff Base Ligand and its Square Planar Complexes

This study details the synthesis and characterization analysis of a of new halogenated Schiff base ligand (E)-4-((5‑bromo-2-hydroxybenzyldene) amino) butanoic acid (HL) and its Square Planar trans-L2 Cu(II) complex. Characterization was performed using a suite of spectroscopic techniques, including 1H and 13C NMR, FT-IR, Raman, and UV–Vis spectroscopy, with thorough explanations and discussions of the spectral data. The structure of ligand and its complex CuL2 were elucidated through single-crystal X-ray crystallography. Additionally, the study extended to the computational domain, where the structural optimization, reactivity descriptors, and linear optical properties, as well as the static and dynamic first and second hyperpolarizabilities of HL, Cr(II)L2, Mn(II)L2, Co(II)L2 and Cu(II)L2 were investigated using DFT and TD-DFT calculation. Our results indicate that the second hyperpolarizability escalates as the atomic number of the transition metal increases. Moreover, it is inferred that the ligand examined holds the potential to be a superior material for second-order nonlinear optical applications.