Electronic descriptor to identify the activity of SACs for e-NRR and effect of BF3 as electrolyte ion.

Electrochemical nitrogen reduction reaction (e-NRR) is an eco-friendly alternative approach to generate ammonia under ambient conditions, with very low power supply. But, developing of an efficient catalyst by suppressing parallel hydrogen evolution reaction as well as avoiding the catalysts poisoning either by hydrogen or electrolyte ion is an open question. So, in order to screen the single atom catalysts (SACs) for the e-NRR, we proposed a descriptor-based approach using density functional theory (DFT) based calculations. We investigated total 24 different SACs of types TM-Pc, TM-N3C1, TM-N2C2, TM-NC3 and TM-N4, considering transition metal (TM). We have considered mainly BF3 ion to understand the role of electrolyte and extended the study for four more electrolyte ions, Cl, ClO4, SO4, OH. Herein, to predict catalytic activity for a given catalyst we have tested 16 different electronic parameters. Out of those, electronic parameter dxz↓ occupancy, identified as electronic descriptor, is showing an excellent linear correlation with catalytic activity (R2 = 0.86). Furthermore, the selectivity of e-NRR over HER is defined by using an energy parameter ∆G*H-∆G*NNH. Further, the electronic descriptor (dxz↓ occupancy) can be used to predict promising catalysts for e-NRR, thus reducing the efforts on designing future single atom catalysts (SACs).

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds.

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties.

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

A Data-driven Approach for Enhanced CO2 Capture with Ruthenium Complexes.

To attain carbon neutrality, significant efforts have been made to capture and use CO2. The homogeneous hydrogenation of CO2 catalyzed by transition metal complexes, particularly ruthenium complexes, has demonstrated significant advantages and is regarded as a viable approach for practical application. Insertion of CO2 into the Ru-H bond, producing the Ru-formate product, is the key step in the hydrogenation of CO2. In order to parameterize the catalytic activities in the CO2 insertion into the Ru-H bond, the concept of simplified mechanism-based approach with data-driven practice (SMADP) has been introduced in this paper. The results showed that the hydricity of the Ru-H complex (ΔGH-) might serve as a single active descriptor in the process of CO2 insertion, and that a novel Ru complex in CO2 catalysis may not be easily obtained by mere modification of the auxiliary ligand at the ruthenium metal site.

Design of Single-Atom Catalysts on C5N2 for Nitrogen Fixation at Ambient Conditions: A First-Principles Study.

Single atom catalysts (SACs) exhibit the flexible coordination structure of the active site and high utilization of active atoms, making them promising candidates for nitrogen reduction reaction (NRR) under ambient conditions. By the aid of first-principles calculations based on DFT, we have systematically explored the NRR catalytic behavior of thirteen 4d- and 5d-transition metal atoms anchored on 2D porous graphite carbon nitride C 5 ${_5 }$ N 2 ${_2 }$ . With high selectivity and outstanding activity, Zr, Nb, Mo, Ta, W and Re-doped C 5 ${_5 }$ N 2 ${_2 }$ are identified as potential nominees for NRR. Particularly, Mo@C 5 ${_5 }$ N 2 ${_2 }$ possesses an impressive low limiting potential of -0.39 V (corresponding to a very low temperature and atmospheric pressure), featuring the potential determining step involving *N-N transitions to *N-NH via the distal path. The catalytic performance of TM@C 5 ${_5 }$ N 2 ${_2 }$ can be well characterized by the adsorption strength of intermediate *N 2 ${_2 }$ H. Moreover, there exists a volcanic relationship between the catalytic property U L ${_{\rm{L}} }$ and the structure descriptor Ψ ${{{\Psi }}}$ , which validates the robustness and universality of Ψ ${{{\Psi }}}$ , combined with our previous study. This work sheds light on the design of SACs with eminent NRR performance.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru.

BACKGROUND: Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. METHODS: A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model. RESULTS: An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. CONCLUSIONS: Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Subtraction Descriptors in Machine Learning for Optimizing the Cocatalyst Effect of Cobalt Phosphate on Hematite Photoanodes.

In the quest for sustainable energy solutions, the optimization of the photoelectrochemical (PEC) performance of hematite photoanodes through cocatalysts represents a promising avenue. This study introduces a novel machine learning approach, leveraging subtraction descriptors, to isolate and quantify the specific effects of cobalt phosphate (Co-Pi) as a cocatalyst on hematite's PEC performance. By integrating data from various analytical techniques, including photoelectrochemical impedance spectroscopy and ultraviolet-visible spectroscopy, with advanced machine learning models, we successfully predicted the PEC performance enhancement attributed to Co-Pi. The Gaussian process regression (GPR) model emerged as the most effective, revealing the critical influence of the interfacial resistance, bulk resistance, and interfacial capacitance on the PEC performance. These findings underscore the potential of cocatalysts in improving charge separation and extending charge carrier lifetimes, thereby boosting the efficiency of photocatalytic reactions. This study not only advances our understanding of the cocatalyst effect in photocatalytic systems but also demonstrates the power of machine learning in modifying complex materials and guiding the development of optimized photocatalytic materials. The implications of this research extend beyond hematite photoanodes, offering a generalizable framework for enhancing the photoelectrochemical properties of a wide range of material modifications such as cocatalyst deposition, doping, and passivation.

NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors.

NanoConstruct is a state-of-the-art computational tool that enables a) the digital construction of ellipsoidal neutral energy minimized nanoparticles (NPs) in vacuum through its graphical user-friendly interface, and b) the calculation of NPs atomistic descriptors. It allows the user to select NP's shape and size by inserting its ellipsoidal axes and rotation angle while the NP material is selected by uploading its Crystallography Information File (CIF). To investigate the stability of materials not yet synthesised, NanoConstruct allows the substitution of the chemical elements of an already synthesized material with chemical elements that belong into the same group and neighbouring rows of the periodic table. The process is divided into three stages: 1) digital construction of the unit cell, 2) digital construction of NP using geometry rules and keeping its stoichiometry and 3) energy minimization of the geometrically constructed NP and calculation of its atomistic descriptors. In this study, NanoConstruct was applied for the investigation of the crystal growth of Zirconia (ZrO2) NPs when in the rutile form. The most stable configuration and the crystal growth route were identified, showing a preferential direction for the crystal growth of ZrO2 in its rutile form. NanoConstruct is freely available through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/riskgone/nanoconstruct/).

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line-Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model.

Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%.

Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling.

Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.

Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis.

BACKGROUND: Downy mildew is a plant disease that affects all cultivated European grapevine varieties. The disease is caused by the oomycete Plasmopara viticola. The current strategy to control this threat relies on repeated applications of fungicides. The most eco-friendly and sustainable alternative solution would be to use bred-resistant varieties. During breeding programs, some wild Vitis species have been used as resistance sources to introduce resistance loci in Vitis vinifera varieties. To ensure the durability of resistance, resistant varieties are built on combinations of these loci, some of which are unfortunately already overcome by virulent pathogen strains. The development of a high-throughput machine learning phenotyping method is now essential for identifying new resistance loci. RESULTS: Images of grapevine leaf discs infected with P. viticola were annotated with OIV 452-1 values, a standard scale, traditionally used by experts to assess resistance visually. This descriptor takes two variables into account the complete phenotype of the symptom: sporulation and necrosis. This annotated dataset was used to train neural networks. Various encoders were used to incorporate prior knowledge of the scale's ordinality. The best results were obtained with the Swin transformer encoder which achieved an accuracy of 81.7%. Finally, from a biological point of view, the model described the studied trait and identified differences between genotypes in agreement with human observers, with an accuracy of 97% but at a high-throughput 650% faster than that of humans. CONCLUSION: This work provides a fast, full pipeline for image processing, including machine learning, to describe the symptoms of grapevine leaf discs infected with P. viticola using the OIV 452-1, a two-symptom standard scale that considers sporulation and necrosis. If symptoms are frequently assessed by visual observation, which is time-consuming, low-throughput, tedious, and expert dependent, the method developed sweeps away all these constraints. This method could be extended to other pathosystems studied on leaf discs where disease symptoms are scored with ordinal scales.

FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.

The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4-9 s to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.

Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes.

The shape of a cell as defined by its membrane can be closely associated with its physiological state. For example, the irregular shapes of cancerous cells and elongated shapes of neuron cells often reflect specific functions, such as cell motility and cell communication. However, it remains unclear whether and which cell shape descriptors can characterize different cellular physiological states. In this study, 12 geometric shape descriptors for a three-dimensional (3D) object were collected from the previous literature and tested with a public dataset of ~400,000 independent 3D cell regions segmented based on fluorescent labeling of the cell membranes in Caenorhabditis elegans embryos. It is revealed that those shape descriptors can faithfully characterize cellular physiological states, including (1) cell division (cytokinesis), along with an abrupt increase in the elongation ratio; (2) a negative correlation of cell migration speed with cell sphericity; (3) cell lineage specification with symmetrically patterned cell shape changes; and (4) cell fate specification with differential gene expression and differential cell shapes. The descriptors established may be used to identify and predict the diverse physiological states in numerous cells, which could be used for not only studying developmental morphogenesis but also diagnosing human disease (e.g., the rapid detection of abnormal cells).

A General Descriptor for Single-Atom Catalysts with Axial Ligands.

Decoration of an axial coordination ligand (ACL) on the active metal site is a highly effective and versatile strategy to tune activity of single-atom catalysts (SACs). However, the regulation mechanism of ACLs on SACs is still incompletely known. Herein, we investigate diversified combinations of ACL-SACs, including all 3d-5d transition metals and ten prototype ACLs. We identify that ACLs can weaken the adsorption capability of the metal atom (M) by raising the bonding energy levels of the M-O bond while enhancing dispersity of the d orbital of M. Through examination of various local configurations and intrinsic parameters of ACL-SACs, a general structure descriptor σ is constructed to quantify the structure-activity relationship of ACL-SACs which solely based on a few key intrinsic features. Importantly, we also identified the axial ligand descriptor σACL, as a part of σ, which can serve as a potential descriptor to determine the rate-limiting steps (RLS) of ACL-SACs in experiment. And we predicted several ACL-SACs, namely, CrN4-, FeN4-, CoN4-, RuN4-, RhN4-, OsN4-, IrN4- and PtN4-ACLs, that entail markedly higher activities than the benchmark catalysts of Pt and IrO2 for oxygen reduction reaction and oxygen evolution reaction, respectively, thereby supporting that the general descriptor σ can provide a simple and cost-effective method to assess efficient electrocatalysts.

Joint unknown input observer for descriptor system based on interval observer.

In this paper, a novel joint unknown input observer (JUIO) is proposed for a class of descriptor systems. The unknown input (UI) to be estimated injects additively into both the state and output equations in a state space model. To the best of our knowledge, only a few contributions in existing work address this problem directly. To begin with, by introducing an auxiliary UI, the original system is transformed into a normal form in which the output is no longer affected by UI. In this way, the negative effect brought by the UI occurring in the output measurement is removed. An interval observer is developed to obtain upper and lower boundary estimates of the output of the reformulated system. After that, an algebraic relationship between the auxiliary UI and the states is established, and a UI reconstruction (UIR) method is developed. Based on the UIR, a JUIO comprising the UIR and a Luenberger-like state observer is developed to achieve asymptotic estimations of the UI and state simultaneously. Verifiable conditions for the existence of the proposed JUIO are given with respect to the original descriptor system. Finally, a simulation example is presented to verify the effectiveness of the proposed method.

Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach.

Umami peptides are known for enhancing the taste experience by binding to oral umami T1R1 and T1R3 receptors. Among them, small peptides (composed of 2-4 amino acids) constitute nearly 40% of reported umami peptides. Given the diversity in amino acids and peptide sequences, umami small peptides possess tremendous untapped potential. By investigating 168,400 small peptides, we screened candidates binding to T1R1/T1R3 through molecular docking and molecular dynamics simulations, explored bonding types, amino acid characteristics, preferred binding sites, etc. Utilizing three-dimensional molecular descriptors, bonding information, and a back-propagation neural network, we developed a predictive model with 90.3% accuracy, identifying 24,539 potential umami peptides. Clustering revealed three classes with distinct logP (-2.66 ± 1.02, -3.52 ± 0.93, -2.44 ± 1.23) and asphericity (0.28 ± 0.12, 0.26 ± 0.11, 0.25 ± 0.11), indicating significant differences in shape and hydrophobicity (P < 0.05) among potential umami peptides binding to T1R1/T1R3. Following clustering, nine representative peptides (CQ, DP, NN, CSQ, DMC, TGS, DATE, HANR, and STAN) were synthesized and confirmed to possess umami taste through sensory evaluations and electronic tongue analyses. In summary, this study provides insights into exploring small peptide interactions with umami receptors, advancing umami peptide prediction models.

Revisiting translator competence in the age of artificial intelligence: the case of legal and institutional translation.

Multi-componential models of translation competence are widely used in translator training as a yardstick for curricular and syllabus design. These models must be adapted to reflect professional trends, such as the impact of artificial intelligence, and machine translation in particular, on working methods. This paper describes the process of adapting a pioneering model of legal translation competence to the broader scope of institutional translation in light of recent trends, as verified by triangulating information from multiple interviews, analyses of translation volumes and job descriptors and other professional inputs. The resulting revised descriptor was validated through a survey of 474 translation professionals from 24 international organisations of diverse sizes and domain specialisations. The suitability of the descriptor was corroborated across the board, but variations were found in perceptions of the relevance of sub-competences to ensure translation quality. Profiles with a stronger specialisation in legal translation or more experience in institutional translation showed higher awareness of the relevance of all the sub-competences, especially the core language, strategic and thematic competences, and even more so for translating texts of a legal or administrative nature. The implications of these findings for training purposes in particular are discussed.

Rational design of bimetallic MBene for efficient electrocatalytic nitrogen reduction.

Electrocatalytic nitrogen reduction reaction (NRR) is one of the most promising approaches to achieving green and efficient NH3 production. However, the designs of efficient NRR catalysts with high activity and selectivity still are severely hampered by inherent linear scaling relations among the adsorption energies of NRR intermediates. Herein, the properties of ten M3B4 type MBenes have been initially investigated for efficient N2 activation and reduction to NH3via first-principles calculations. We highlight that Cr3B4 MBene possesses remarkable NRR activity with a record-low limiting potential (-0.13 V). Then, this work proposes descriptor-based design principles that can effectively evaluate the catalytic activity of MBenes, which have been further employed to design bimetallic M2M'B4 MBenes. As a result, 5 promising candidates including Ti2YB4, V2YB4, V2MoB4, Nb2YB4, and Nb2CrB4 with excellent NRR performance have been extracted from 20 bimetallic MBenes. Further analysis illuminates that constructing bimetallic MBenes can selectively tune the adsorption strength of NHNH2** and NH2NH2**, and break the linear scaling relations between their adsorption energies, rendering them ideal for NRR. This work not only pioneers the application of MBenes as efficient NRR catalysts but also proposes rational design principles for boosting their catalytic performance.

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals.

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.