Semi-empirical Research Articles

Crystallisation Dynamics in Large-Scale Extrusion Additive Manufacturing: An Analysis with and without Temperature Modification.

Large-Scale Material Extrusion (LS-MEX) is increasingly being used in small-scale production and prototyping due to its ability to create components in new temporal and spatial dimensions. However, the use of this manufacturing process poses microscopic and macroscopic challenges not encountered in previous small-scale production systems. These challenges arise primarily from the prolonged retention of heat in the material, which leads to insufficient strength in the extruded strands at the macrostructural level. As a result, the component can collapse, a phenomenon known as 'slumping'. Thermal energy also influences microstructural changes, such as crystallisation kinetics, which affect properties such as the strength and stiffness of the final product. The duration and dynamics of thermal energy are influenced by manufacturing parameters and the possible use of additional peripheral equipment, which affects component quality. In this study, the influence of thermal energy on structural processes through simulations of polyamide 6 with 40% carbon fibres (PA6 wt.%40 CF) is investigated. The results show that by adjusting the process parameters and using modification units, the thermal profile of the material can be accurately controlled, which allows the microstructural processes to be precisely controlled. This leads to the targeted modification of the macroscopic material properties. The focus of this work is on the combination of numerical simulations of the LS-MEX process with semi-empirical methods for the analysis of crystallisation processes. The application of the Nakamura model, which is used throughout similar investigations, allows a detailed description and prediction of the crystallisation kinetics during the manufacturing process. The study shows that the absolute degree of crystallisation can be determined with simplified assumptions using a combination of thermal simulations and semi-empirical approaches. It was found that the absolute degree of crystallisation increases from the outer interface of the strand to the print bed across the cross-section. This can be attributed to the specific thermal boundary conditions and the resulting temperature profiles at different points.

Cyclodextrin-based ternary supramolecular deep eutectic solvents for efficient extraction and analysis of trace quinolones and sulfonamides in wastewater by adjusting pH

BackgroundAntibiotics residues can accelerate the growth of drug-resistant bacteria and harm the ecological environment. Under the effect of enrichment and biomagnification, the emergence of drug-resistant pathogenic bacteria may eventually lead to humans being ineffective to drugs in the face of bacterial or fungal disease infections in the future. It is urgent to develop an efficient separation medium and analytical method for simultaneous extraction and determination of antibiotics in the water environment. ResultsThis work doped 2,6-Di-O-methyl-β-cyclodextrin, randomly methyl-β-cyclodextrin, 2-hydroxypropyl-β-cyclodextrin with thymol:fatty acid respectively to construct non-covalent interaction-dominated pH-responsive ternary supramolecular deep eutectic solvents (SUPRADESs), which can undergo a hydrophilic/hydrophobic transition with aqueous phase to achieve an efficient microextraction. Semi-empirical method illustrated that SUPRADESs have a wide range of hydrogen bond receptor sites. We developed a SUPRADES-based analytical method combined with liquid chromatography-triple quadrupole mass spectrometry for the extraction and determination of trace quinolones and sulfonamides in wastewater. The overall limits of detection of the method were 0.0021–0.0334 ng mL−1 and the limits of quantification were 0.0073–0.1114 ng mL−1. The linearity maintained good in the spiked level of 0.01–100 ng mL−1 (R2 > 0.99). The overall enrichment factors of the method were 157–201 with lower standard deviations (≤8.7). SignificanceThe method gave an extraction recovery of 70.1–115.3 % for 28 antibiotics in livestock farming wastewater samples from Zhejiang, China, at trace levels (minimum 0.5 ng mL−1). The results demonstrated that inducing the phase transition between SUPRADES and aqueous phase by adjusting pH for extraction is a novel and efficient pretreatment strategy. To our knowledge, this is the first application of cyclodextrin-based ternary SUPRADESs with pH-responsive reversible hydrophobicity-hydrophilicity transition behavior in wastewater analysis.

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment.

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

Sound-absorbing structures to reduce cabin and community noise of aircraft (Review)

The analysis of sound-absorbing structures is carried out taking into account their modern classification. The considered sound-absorbing structures can be used to reduce aircraft cabin and community noise levels. Depending on the spectrum of the main noise sources, the required type of sound-absorbing structure is selected, which is then adjusted to the problematic frequency range. Semiempirical and experimental methods are used to adjust sound-absorbing structures, and numerical methods are currently being actively developed.

Klein Tunneling in β12 Borophene.

Motivated by the recent observation of Klein tunneling in 8-Pmmn borophene, we delve into the phenomenon in β12 borophene by employing tight-binding approximation theory to establish a theoretical mode. The tight-binding model is a semi-empirical method for establishing the Hamiltonian based on atomic orbitals. A single cell of β12 borophene contains five atoms and multiple central bonds, so it creates the complexity of the tight-binding model Hamiltonian of β12 borophene. We investigate transmission across one potential barrier and two potential barriers by changing the width and height of barriers and the distance between two potential barriers. Regardless of the change in the barrier heights and widths, we find the interface to be perfectly transparent for normal incidence. For other angles of incidence, perfect transmission at certain angles can also be observed. Furthermore, perfect and all-angle transmission across a potential barrier takes place when the incident energy approaches the Dirac point. This is analogous to the "super", all-angle transmission reported for the dice lattice for Klein tunneling across a potential barrier. These findings highlight the significance of our theoretical model in understanding the complex dynamics of Klein tunneling in borophene structures.

Predicting Liquefaction Susceptibility in North-East Morocco: Comparative Analysis of Semi-empirical Methods and UBC3D-PLM Model

Predicting Liquefaction Susceptibility in North-East Morocco: Comparative Analysis of Semi-empirical Methods and UBC3D-PLM Model

Chemical and electrochemical nanofabrication of polyaminothiazole/graphene oxide composites

Poly(2-aminothiazole) is a little-studied conducting polymer with sulfur and nitrogen heteroatoms in the aromatic ring, which reveals prospects for application in various fields. We studied the conditions of chemical synthesis of poly(2-aminothiazole) and its composite with graphene oxide in an aqueous solution, as well as electrochemical deposition on electrode surfaces – Pt and SnO2. The structure of the polymer was investigated by SEM and FT-IR spectroscopy. A quantum-chemical calculation using the semi-empirical PM7 method was carried out. The electrochemical activity of polymer films was examined. Increasing effective parameters of charge transport for poly(2-aminothiazole)/graphene oxide films were detected.

Novel Compounds Design of Acertannin, Hamamelitannin, and Petunidin-3-Glucoside Typical Compounds of African Leaves (Vernonia amygdalina Del) as Antibacterial Based on QSAR and Molecular Docking

Antibacterial secondary metabolites such as tannins and their derivatives are found in the Vernonia amygdalina Del. Antibiotic resistance can develop due to overuse, reducing the efficacy of drugs to prevent and treat infections. This research aims to use the Quantitative Structure-Activity Relationship (QSAR) and the semi-empirical method Austin Model 1 (AM1) to design a modified novel compound from African leaves that has improved antibacterial activity. This research includes a descriptor calculation of QSAR using AM1 MOE on typical compounds from African leaves, and calculation results are chosen based on a multilinear regression statistical analysis. The model equation represents the three primary parameters of QSAR, which are electronic, hydrophobic, and steric parameters, which will be used to measure modified compounds. Molecular docking using Autodock Tools (The Scripps Research Institute, USA), and analysis of results of docking Autodock Tools using Discovery Studio 3.5 Client. The best QSAR model obtained is LogEC50 = (0.829 x LogP) - (1,302 x AM1_HOMO) - (0.339 x AM1_dipole) - (5,128 x mr) + (0.145 x vol) - (11,355). The results showed that EC50 prediction of modified hamamelitannin has the best activity with the lowest ΔGbind -9.0 kcal/mol and inhibition constant of 0.249 μM. In summary, the novel compound's design calculation has better antibacterial activity, as indicated by a lower EC50, than fosfomycin or compounds without modification. The modified hamamelitannin compound was found to have better antibacterial activity (prediction EC50 = 0.1933 μM) than the original (experimental EC50 = 145.50 μM).

An effective multi-time series model of RC column backbone curve identification

Accurate identification of the backbone curves of reinforced concrete (RC) columns is key to engineering design and strengthening renovation. In view of the problems of high cost, long time, low accuracy, large dispersion of calculation results and discontinuous stiffness changes of the existing backbone curve identification methods, such as experimental method, finite element simulation method and semi-theoretical and semi-empirical method, it is proposed to transform the backbone curve identification problem into a multi-time series prediction problem. By introducing the attention mechanism and combining it with the bidirectional long short-term memory (BiLSTM), the backbone curve identification model (BC-ABiLSTM) is established considering the relationship between the front and back points of the backbone curves. Compared with the models for backbone curves with BiLSTM (BC-BiLSTM), long short-term memory (BC-LSTM), multilayer perceptron (BC-MLP), and the existing identification methods, the performance of the BC-ABiLSTM model is better, and the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 of the BC-ABiLSTM model on the testing set are 12.492 kN, 10.595 %, 20.838 kN and 0.9924, respectively, which provides a new method for accurate, efficient and cost-effective prediction of RC column backbone curve points under various cyclic loading levels.

Multi-electron transfer mechanism and band structures of CNTs in MOF-derived CNTs/Co hybrids for enhanced electromagnetic wave absorption property

Metal-organic framework (MOF)-derived magnetic metal/carbon nanocomposites have received widespread attention for electromagnetic wave (EMW) absorption. The present study introduces a novel approach to MOF composite material design by constructing composites modified with carbon nanotubes (CNTs) and magnetic Co nanoparticles. The carbon confinement effect in CNT/Co not only regulates the electronic structure of Co-C bonds, but also maintains the redox cycle of Co0/2→Co3+→Co0/2+, improving electronic activity and exhibiting synergistic integration characteristics of efficient electromagnetic wave absorption. Particularly, the maximum reflection loss (RLmin) of CNTs/Co-900 reached −52.7 dB at 4.67 GHz in the case of an absorber thickness of 1.6 mm. The excellent microwave absorption performance may be attributed to the interwoven structure formed by novel ZIF-67, CNTs, and Co metal nanoparticles and the double loss of magnetic Co and dielectric CNTs. In addition, the band gap of CNTs was calculated to be Eg = 0.27 eV by the semi-empirical tight-binding method with DFTB+. It is confirmed that CNTs have high electron migration ability, which is beneficial to conductive loss. It is confirmed that CNTs have high electron migration ability, which is beneficial to conductive loss. This study provides a novel strategy for the development of metal-organic framework-based magnetic particles/carbon nanocomposites for electromagnetic wave absorption.

Vertical transport velocity of fine particles of aluminum smelter emissions

In this study, the average values of vertical velocity of particles emitted from an aluminum smelter in the surface layer of the atmosphere were estimated using a semi-empirical method. The method is based on regression analysis of the horizontal profile of pollutants measured along the selected direction using moss bioindicators. The selection of epiphytic mosses Sanionia uncinata was carried out in 2013 in the zone of influence of a metallurgical industry enterprise in the city of Kandalaksha, Murmansk region. The concentrations of As, Si, Ni, Zn, Ti, Cd, Na, Pb, Co, K, Ba, Ca, Mg, Mn, Sr, Fe, Al, V, Cr, Cu were determined using atomic emission spectrometry. The conducted assessments showed that the average particle velocity toward the Earth’s surface, when considering large spatial and temporal scales, is tens of times higher than gravitational settling velocities.

COMPUTER SIMULATION OF THE STRUCTURE OF SOME AZO-DERIVATIVES OF β-DIKETONES BY THE METHODS OF QUANTUM CHEMISTRY

The electronic and spatial structures of newly synthesized derivatives of β-diketones were studied based on the results of quantum chemical calculations. The properties of β-diketones with substituted phenyldiazonium chloride, namely 8 tautomers aryl derivatives of β-diketones-(E)-3-(2-(1-(dimethylamino)-1,3-dioxobutan-2-ylidene)hydrazinyl)-2-hydroxy-5-nitrobenzene sulfonic acid (H3L1) have been studied using the PM3 semi-empirical quantum chemistry method and the density functional theory (DFT) method with the B3LYP hybrid functional and 6-31+G(d,p) basis set. Energy and electronic characteristics of 8 tautomers are investigated. The effect of intramolecular hydrogen bonding on the structure of the investigated compounds has been studied. The formation of hydrogen bonds was confirmed by Natural Bond Orbital (NBO) analysis. Theoretical studies using the PM3 and DFT methods confirm the practical conclusions about the superiority of the hydrazone form in real samples.

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

The recycling of lithium-ion batteries (LIBs) from electric vehicles (EVs) for augmenting the capacity of battery energy storage systems (BESS) presents a sustainable approach to leverage investment in LIBs, mitigating economic losses. This study highlights the critical role of accurate capacity estimation and remaining-useful-life (RUL) prediction in determining optimal replacement and augmentation schedules. We introduce a semi-empirical (SE) model that integrates experimental aging data with an adaptive method and an electrochemical model to capture the degradation trends of LIBs accurately. The adoption of the Moore-Penrose pseudoinverse (Pinv) method, suited for the SE model's multi-parameter, single-output nature, significantly improves capacity estimation accuracy, achieving a maximum error below 0.2 Ah. This improved SE (ISE) model accounts for capacity trends and addresses non-stationary phenomena, including noise and signal distortion during parameter identification and capacity recovery phases.To address these challenges, we propose using discrete wavelet transform (DWT) for the decomposition of non-stationary signals, adjusting the window size to signal characteristics for enhanced noise reduction. The application of DWT to denoise α and β parameters, characterized by their non-constant periodicity and noise interference, facilitates the development of DWT combined with the ISE (DWT-ISE) aging model for early RUL prediction. Experimental validation reveals that our DWT-based approach minimizes root-mean square error (RMSE) and mean absolute error (MAE) for early RUL prediction, demonstrating the model's superiority at 300 cycles for identification and achieving the lowest prediction error at 400 cycles. Furthermore, comparison results of other models (Particle filter, and long short-term memory) reveal that our DWT-ISE aging model exhibits the best RMSE and MAE on limited data. Specifically, the RMSE and MAE values for RUL prediction, underscored as 0.032 % and 0.027 % respectively at 300 cycles, are confirmed as the highest evaluation metrics.

Punching Shear of FRP-RC Slab–Column Connections: A Comprehensive Database

Several design standards have been developed in the last two decades to estimate the punching capacity of two-way reinforced concrete (RC) slabs reinforced with fiber-reinforced polymer (FRP) reinforcement. FRP-RC design standards include the recently published ACI 440.11-22, CSA/S806-12, and JSCE-2007. These models are either based on empirical data or semi-empirical methods and calibrated using different databases. Additionally, these standards do not have provisions for connections with shear reinforcement. Therefore, a reliable worldwide database for developing and assessing the applicability of such provisions with test results is vital. This study presents a worldwide and up-to-date database for punching shear of FRP-RC slabs. The database includes 197 tested connections, comprising interior and edge connections, with and without shear reinforcement, and a wide range of materials and cross-sectional properties. The database was used to evaluate the accuracy of the mentioned standards in predicting the punching shear capacity. For connections without shear reinforcement, it was determined that the three design standards yielded similar performance with different conservatism levels. ACI 440.11-22 yielded the most conservative results, with average Vexp/Vpred ratios of 2.04 compared to 1.28 and 1.3 for other models. For connection with shear reinforcement, specimens with Evf> 100 GPa resulted in Vexp/Vpred ratios less than 1.0 for ACI and CSA standards.

Local Energy Decomposition Analysis of London Dispersion Effects: From Simple Model Dimers to Complex Biomolecular Assemblies.

ConspectusLondon dispersion (LD) forces are ubiquitous in chemistry, playing a pivotal role in a wide range of chemical processes. For example, they influence the structure of molecular crystals, the selectivity of organocatalytic transformations, and the formation of biomolecular assemblies. Harnessing these forces for chemical applications requires consistent quantification of the LD energy across a broad and diverse spectrum of chemical scenarios. Despite the great progress made in recent years in the development of experimental strategies for LD quantification, quantum chemical methods remain one of the most useful tools in the hand of chemists for the study of these weak interactions. Unfortunately, the accurate quantification of LD effects in complex systems poses many challenges for electronic structure theories. One of the problems stems from the fact that LD forces originate from long-range electronic dynamic correlation, and hence, their rigorous description requires the use of complex, highly correlated wave function-based methods. These methods typically feature a steep scaling with the system size, limiting their applicability to small model systems. Another core challenge lies in disentangling short-range from long-range dynamic correlation, which from a rigorous quantum mechanical perspective is not possible.In this Account, we describe our research endeavors in the development of broadly applicable computational methods for LD quantification in molecular chemistry as well as challenging applications of these schemes in various domains of chemical research. Our strategy lies in the use of local correlation theories to reduce the computational cost associated with complex electronic structure methods while providing at the same time a simple means of decomposition of dynamic correlation into its long-range and short-range components. In particular, the local energy decomposition (LED) scheme at the domain-based local pair natural orbital coupled cluster (DLPNO-CCSD(T)) level has emerged as a powerful tool in our research, offering a clear-cut quantitative definition of the LD energy that remains valid across a plethora of different chemical scenarios. Typical applications of this scheme are examined, encompassing protein-ligand interactions and reactivity studies involving many fragments and complex electronic structures. In addition, our research also involves the development of novel cost-effective methodologies, which exploit the LED definition of the LD energy, for LD energy quantification that are, in principle, applicable to systems with thousands of atoms. The Hartree-Fock plus London Dispersion (HFLD) scheme, correcting the HF interaction energy using an approximate CCSD(T)-based LD energy, is a useful, parameter-free electronic structure method for the study of LD effects in systems with hundreds of molecular fragments. However, the usefulness of the LED scheme reaches beyond providing an interpretation of the calculated DLPNO-CCSD(T) or DLPNO-MP2 interaction energies. For example, the dispersion energies obtained from the LED can be fruitfully used in order to parametrize semiempirical dispersion models. We will demonstrate this in the context of an emerging semiempirical method, namely, the Natural Orbital Tied Constructed Hamiltonian (NOTCH) method. NOTCH incorporates LED-derived LD energies and shows promising accuracy at a minimum amount of empiricism. Thus, it holds substantial promise for large and complex systems.

Atomistic origins of compound semiconductor synthesis with computational neuromorphic engineering

We propose the usage of multi-element bulk materials to mimic neural dynamics instead of atomically thin materials via the modeling of group II–IV compound semiconductor growth using vacancy defects and dopants by creating and annihilating one another like a complex artificial neural network, where each atom itself is the device in analogy to crossbar memory arrays, where each node is a device. We quantify the effects of atomistic variations in the electronic structure of an alloy semiconductor using a hybrid method composed of a semiempirical tight-binding method, density functional theory, Boltzmann transport theory, and a transfer-matrix method. We find that the artificial neural network resembles the neural transmission dynamics and, by proposing resistive switching in small areas with low energy consumption, we can increase the integration density similar to the human brain.

Electronic and structural properties of Möbius boron-nitride and carbon nanobelts

For the development of nanofilters and nanosensors, we wish to know the impact of size on their geometric, electronic, and thermal stabilities. Using the semiempirical tight binding method as implemented in the xTB program, we characterized Möbius boron-nitride and carbon-based nanobelts with different sizes and compared them to each other and to normal nanobelts. The calculated properties include the infrared spectra, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the energy gap, the chemical potential, and the molecular hardness. The agreement between the peak positions from theoretical infrared spectra compared with experimental ones for all systems validates the methodology that we used. Our findings show that for the boron-nitride-based nanobelts, the calculated properties have an opposite monotonic relationship with the size of the systems, whereas for the carbon-based nanobelts, the properties show the same monotonic relationship for both types of nanobelts. Also, the torsion presented on the Möbius nanobelts, in the case of boron-nitride, induced an inhomogeneous surface distribution for the HOMO orbitals. High-temperature molecular dynamics also allowed us to contrast carbon-based systems with boron-nitride systems at various temperatures. In all cases, the properties vary with the increase in size of the nanobelts, indicating that it is possible to choose the desired values by changing the size and type of the systems. This work has many implications for future studies, for example our results show that carbon-based nanobelts did not break as we increased the temperature, whereas boron-nitride nanobelts had a rupture temperature that varied with their size; this is a meaningful result that can be tested when the use of more accurate simulation methods become practical for such systems in the future.

Modeling Zinc Complexes Using Neural Networks.

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

The lunar 40Ar/36Ar antiquity indicator in the presence of a lunar paleomagnetosphere

The ratio of 40Ar/36Ar trapped within lunar grains, commonly known as the lunar antiquity indicator, is an important semi-empirical method for dating the time at which lunar samples were exposed to the solar wind. The behavior of the antiquity indicator is governed by the relative implantation fluxes of solar wind-derived 36Ar ions and indigenously sourced lunar exospheric 40Ar ions. Previous explanations for the behavior of the antiquity indicator have assumed constancy in both the solar wind ion precipitation and exospheric ion recycling fluxes; however, the presence of a lunar paleomagnetosphere likely invalidates these assumptions. Furthermore, most astrophysical models of stellar evolution suggest that the solar wind flux should have been significantly higher in the past, which would also affect the behavior of the antiquity indicator. Here, we use numerical simulations to explore the behavior of solar wind 36Ar ions and lunar exospheric 40Ar ions in the presence of lunar paleomagnetic fields of varying strengths. We find that paleomagnetic fields suppress the solar wind 36Ar flux by up to an order-of-magnitude while slightly enhancing the recycling flux of lunar exospheric 40Ar ions. We also find that at an epoch of ∼2 Gya, the suppression of solar wind 36Ar access to the lunar surface by a lunar paleomagnetosphere is−somewhat fortuitously−nearly equally balanced by the expected increase in the upstream solar wind flux. These counterbalancing effects suggest that the lunar paleomagnetosphere played a critical role in preserving the correlation between the antiquity indicator and the radioactive decay profile of indigenous lunar 40K. Thus, a key implication of these findings is that the accuracy of the 40Ar/36Ar indicator for any lunar sample may be strongly influenced by the poorly constrained history of the lunar magnetic field.

Design, synthesis, explorations of DFT, PM6, molecular docking with SAR-CoV-2 Mpro and anticancer properties of novel indolo[2,3-c]isoquinoline analogues

A novel series of indolo[2,3-c]isoquinoline derivatives, coupled with pyrazole and pyrazolo[3,4-d]pyrimidinone rings bearing substituted Cl, OCH3, CH3, and H groups II, II(a-d), were successfully synthesized, exhibiting significant biological and pharmacological relevance. Computational chemistry using the semiempirical method PM6 via MOPAC software was employed to investigate their thermochemical properties. Compound IIIb displayed a lower heat formation (ΔHf) value of 68.89873 kcal/mole, indicating enhanced stability, whereas compound IId exhibited a higher ΔHf value of 151.32004 kcal/mole, suggesting lesser stability. Density functional theory (DFT) at B3LYP/def2-TZVP def2/J was utilized for analyzing electronic structures and geometric optimization. The HOMO-LUMO energy gap (ΔEg) followed an ascending order in the pyrimidinone>pyrazole series, with IIIb possessing the lowest energy gap (ΔEg = 2.994 eV) and IIc having the highest (ΔEg = 3.433 eV). Compound IIa displayed the highest electrophilicity index (χ = 3.540 eV) and exhibited potent anti-cancer activity, as observed in derivatives II and III(a-d). Additionally, compounds IIa, IIb, and IIc demonstrated promising interactions with amino acids of the SARS-CoV-2 main protease (PDB:7e18).