Semi-empirical Research Articles

Semiempirical Methods for Molecular Systems in Strong Magnetic Fields.

A general scheme is presented to extend semiempirical methods to include the effects of arbitrary strength magnetic fields, while maintaining computational efficiency. The approach utilizes three main modifications; a London atomic orbital (LAO) basis set is introduced, field-dependent kinetic energy corrections are added to the model Hamiltonian, and spin-Zeeman interaction energy terms are included. The approach is applied to the widely available density-functional tight-binding method GFN1-xTB. Considering the basis set requirements for the kinetic energy corrections in a magnetic field leads to two variants: a single-basis approach GFN1-xTB-M0 and a dual-basis approach GFN1-xTB-M1. The LAO basis in the latter includes the appropriate nodal structure for an accurate representation of the kinetic energy corrections. The variants are assessed by benchmarking magnetizabilities and nuclear magnetic resonance shielding constants calculated using weak magnetic fields. Remarkably, the GFN1-xTB-M1 approach also exhibits excellent performance for strong fields, || ≤ 0.2B0 (B0 = 2.3505 × 105 T), recovering exotic features such as the para- to dia-magnetic transition in the BH molecule and the preferred electronic configuration, molecular conformation, and orientation of benzene. At stronger field strengths, || > 0.2B0, a degradation in the quality of the results is observed. The utility of GFN1-xTB-M1 is demonstrated by performing conformer searches in a range of field strengths for the cyclooctatetraene molecule, with GFN1-xTB-M1 capturing the transition from tub to planar conformations at high field, consistent with much more computationally demanding current-density functional theory calculations. Magnetically induced currents are also shown to be well described for the benzene and infinitene molecules, the latter demonstrating the flexibility and computational efficiency of the approach. The GFN1-xTB-M1 approach is a useful tool for the study of structure, conformation, and dynamics of large systems in magnetic fields at the semiempirical level as well as for preoptimization of molecular structure in ab initio calculations, enabling more efficient exploration of complex potential energy surfaces and reactivity in the presence of external fields.

Linearizing Battery Degradation for Health-Aware Vehicle Energy Management

The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.

Measurements and calculations of CO2-broadening and shift coefficients of water vapor transitions in the 5150–5550 cm−1 spectral region

The high-resolution H2O absorption spectra induced by CO2 pressure have been recorded using a Bruker IFS 125 HR FTIR spectrometer at room temperature in the 5150–5550 cm–1 spectral region. A multispectrum fitting procedure has been applied to these spectra to recover the spectral line parameters using the speed-dependent Voigt profile. The water vapor line broadening and shift coefficients for lines of two vibrational bands ν2+ν3 and ν1+ν2 were obtained. An uncertainty of the broadening coefficients for the unblended H2O absorption lines was estimated to be 2–3%. The calculations of line-broadening and line-shifting parameters were performed using the semi-empirical method which is based on the impact theory and enables a realistic prediction modified by introducing additional parameters obtained by using empirical data. The root mean square deviations of measured and calculated line widths and shifts were 0.0086 and 0.0053 cm−1 atm−1, respectively.

Ground Motion and Liquefaction Study at Opak River Estuary Bantul

In Mei 2006, an earthquake occurred, and liquefaction was induced in Bantul Regency in the form of lateral spreading and sand boiling. Based on Indonesia’s liquefaction susceptibility zone map 2019, the Bantul coast was identified as a high liquefaction hazard zone. There is a high risk of liquefaction in that location, and if liquefaction occurs, the effective stresses of the soil will be lost. This study aimed to determine ground motion-induced liquefaction potential in Opak river estuary in Bantul. The peak ground acceleration can be predicted by probabilistic seismic hazard analysis (PSHA) based on Indonesian spectra design application, deterministic seismic hazard analysis (DSHA) based on the mechanism of a recent earthquake in Bantul, and non-linear earthquake site response analysis (NERA) based on the 2004 Parkfield California earthquake, the 1986 Chalfant Valley California earthquake, and the 1987 Superstition Hill California earthquake. The value of acceleration from site-specific responses with DSHA and NERA is 0.558 g. Then, the maximum acceleration, seismic magnitude, N-SPT bore log soil, and other soil parameters are analyzed with a semi-empirical simplified method to estimate the safety factor of liquefaction potential using Settle3. The liquefaction safety factor was calculated by dividing the cyclic resistance ratio by the cyclic stress ratio; liquefaction occurs if the safety factor is less than 1. The result indicates that the Opak river estuary has a liquefaction potential beneath the surface from -1.5 to -16.5 m depth with various thick each boreholes. It implies that the bearing capacity design of the foundation system in this area and the liquefaction effect that will emerge when an earthquake occurs must be appropriately considered.

2D-QSAR, Docking, Molecular Dynamics Simulations with the MM/GBSA Approaches against Graves' Disease and PTPN22

Graves' disease (GD) is an autoimmune condition that frequently causes hyperthyroidism and thyrotoxicosis. Protein tyrosine phosphatase, non-receptor type 22 (lymphoid) isoform 1 (PTPN22), is a promising therapeutic candidate for treating GD, rheumatoid arthritis, type 1 diabetes, and other autoimmune disorders. In this dataset, 31 molecular compounds and two standard drugs were optimized using the semi-empirical PM7 theory method via MOPAC v22.0.4 to reveal the key influencing factors contributing to their grave's disease inhibition activity and selectivity. Using QSARIN software, the acquired properties/descriptors were used to create a quantitative structural activities relationship (QSAR) model, and the similarities between the observed and predicted pIC50 values were examined. A molecular docking simulation study also uncovers non-covalent interactions between the investigated compounds and the receptors. The observed ligand-protein interactions with GD proteins (PDB ID 2XPG and 4QT5) and PTPN22 (PDB ID 3BRH) were investigated. The pharmacokinetics (ADMET) properties were also investigated. Finally, molecular dynamics (MD) simulation and MM/GBSA studies that demonstrated stable trajectory and molecular properties with a consistent interaction profile were used to validate the stability of the compounds in the complex with PTPN22.

A New Semi-Empirical Method of Quantifying Electrolyzer Degradation

Electrolyzers are a key component in transitioning the world’s reliance on fossil fuels for energy. Accordingly, a substantial influence on electrolyzer costs is operational efficiency. The means of quantifying this efficiency is necessary for sustaining the continuous improvement of this technology.Polarization curves are a critical tool in understanding the behavior and mechanisms of electrochemical devices such as electrolyzers and fuel cells. In this work, a semi-empirical voltage-breakdown method is used to model and identify a series of resistance levels and voltage losses. This model presents the modes of change in electrolyzer performance. Polarization curves are taken at several temperature and time intervals throughout steady state operation. The unique benefit of this method is that no potentiostat is required and that key parameters, such as the Tafel slope and ohmic resistance, are compared to more direct measurements.Presented here, is a user-friendly technique of evaluating the degradation of an electrolyzer over the course of its lifetime. More specifically, it provides insight on the durability of cell components such as membrane, gas diffusion layers, and catalyst layers.

Electrostatic Field for Positive Lightning Impulse Breakdown Voltage in Sphere-to-Plane Air Gaps Using Machine Learning

Breakdown (BD) voltage is significant in high-voltage power electric machines. Currently, BD voltages are mainly predicted by the semi-empirical formula in strongly inhomogeneous electric fields. However, the equation could not be applied for electrodes with weakly inhomogeneous electric fields. In this paper, positive lightning impulse BD voltages are predicted in various sphere-to-plane air gaps using forms of machine learning such as support vector regression (SVR), Bayesian regression (BR) and multilayer perceptron (MLP). Unlike previous studies, a method is also proposed by introducing streamer propagation characteristics as new features and by removing electric field gradients as unnecessary features to find out how to reduce the feature dimension. The streamer propagation characteristics are suggested to reflect the possibility of a discharge process between electrodes. Predicted voltages from machine learning algorithms are compared with the experimental results and calculated voltages from the semi-empirical formula. Firstly, the predictions from each model agreed well with the datasets. New features were observed to be applied for machine learning algorithms and to be as important as known electrostatic features before discharge. Secondly, predicted BD voltages were more accurate than calculated voltages from the semi-empirical equation in strongly inhomogeneous electric fields. Predictions from each model also agreed well with the experimental results in weakly inhomogeneous electric fields. The prediction accuracy of SVR was better than those of BR and MLP. Machine learning algorithms were also shown to be applied for electrodes with a wide range of inhomogeneities, unlike a semi-empirical method. We expect that the suggested features and machine learning algorithms can be used for accurately calculating BD voltages.

The crucial role of Y109 and R162 as catalytic residues of nanoKAZ: insights from molecular docking, molecular dynamics simulation, and quantum chemical investigations.

nanoKAZ is a compact luciferase that exhibits intense blue light emission when it catalyzes the substrate Furimazine (FMZ) as a luciferin, making it an excellent candidate as a reporter protein. However, the specific catalytic residues and mechanism of nanoKAZ have not been revealed. Recently, the structure of nanoKAZ was determined, and it was observed that the luminescent properties changed when FMZ analogs with naphthalene replacing benzene were used. It is speculated that the substituted naphthalene may influence the interaction between the catalytic residues and luciferins, thereby affecting the energy of the emitted light signal. Therefore, the primary objective of this study is to analyze and compare the molecular recognition between nanoKAZ and FMZ along with its four activity-altered naphthalene analogs, with aiming to identify the catalytic residues. Molecular docking was employed to construct all nanoKAZ-luciferin models, followed by a 500ns molecular dynamics simulation. The simulation trajectory was subjected to MM/PBSA analysis to identify crucial residues that contribute significantly to luciferin binding. In the result, two polar residues Y109, and R162 were identified as active residues as their notable contributions to the binding energy. Subsequently, an oxygen molecule was introduced into the local region of the nanoKAZ-FMZ complex and followed with quantum chemical calculations (semiempirical and DFT methods were used) to investigate the catalysis details. The results illustrated the involvement of Y109 and R162 in the oxygenation of FMZ, leading to the formation of dioxetanone, which has been suggested as an important intermediate in the oxidation process among various luciferins sharing the same functional group as FMZ.

A semiempirical method optimized for modeling proteins

ContextIn recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.MethodsTwo alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Photocatalytic splitting of water on g-C3N4-based single-atom Pt catalysts with stable “sandwich” structure: a combined first principles and semi-empirical investigation

Solar-to-hydrogen energy conversion is a promising strategy to solve environmental pollution and energy crisis by utilizing photocatalytic water splitting. In this work, a “sandwich” structure of g-C3N4-based single-atom platinum (Pt) catalyst for photocatalytic water splitting is proposed and investigated using a combined first principles and semi-empirical study method. The calculation results indicate that, without any co-catalyst, the photogenerated holes in the valence band of BL-g-C3N4 cannot oxidize H2O to O2, and its oxygen evolution reaction (OER) performance is not better than that of the pristine monolayer g-C3N4. Significantly, the photogenerated holes in the valence band of the “sandwich”-structured photocatalyst g-C3N4–Pt1–g-C3N4 can oxidize H2O to O2 without any co-catalyst. That is, the OER performance of g-C3N4–Pt1–g-C3N4 is better than that of the pristine g-C3N4 and the pristine bilayer-g-C3N4 (BL-g-C3N4). However, it can be found that the introduction of single Pt atom confinement in the BL-g-C3N4 cannot effectively reduce hydrogen evolution reaction (HER) energy barrier or improve the hydrogen evolution kinetics of BL-g-C3N4. In other words, the introduction of the confined single Pt atom in the BL-g-C3N4 not only fails to improve HER performance of BL-g-C3N4, but deteriorates HER catalytic performance of BL-g-C3N4. These findings provide some theoretical insights for engineers to prepare photocatalysts with higher activity and stability.

Machine learning prediction of wave characteristics: Comparison between semi-empirical approaches and DT model

Prediction of wave characteristics plays a crucial role in design and performance assessment of various coastal projects. The computational complexity and time-consuming procedures have limited the applications of numerical models for wave predictions. This study develops a new model for wave prediction using the capabilities of the M5p Decision Tree (DT) algorithm. The wind speed was employed as the model input parameter, and satellite measured altimeter data was used to train and test the developed models. The proposed DT-based model is compared with the existing semi-empirical wave prediction methods recommended by Coastal Engineering Manual (CEM). For a comprehensive assessment of the model's performance, four scenarios with different input data and modelling approaches are investigated. It was shown that the locally calibrated CEM formula can provide the best performance amongst the modified semi-empirical formulations. Comparison results show that M5p DT models' prediction are far more accurate and closely match the satellite measured altimeter data than the semi-empirical models. Furthermore, the ‘Short-term’ M5p tree model is shown to have the best predictive results, and the obtained results highlight that the proposed M5p model can provide a robust alternative for wave prediction across large spatial and temporal scales.

EVTOL Tilt-Wing Aircraft Design under Uncertainty Using a Multidisciplinary Possibilistic Approach

Recent development in Electric Vertical Take-off and Landing (eVTOL) aircraft makes it a popular design approach for urban air mobility (UAM). When designing these configurations, due to the uncertainty present in semi-empirical estimations, often used for aerodynamic characteristics during the conceptual design phase, results can only be trusted to approximately 80% accuracy. Accordingly, an optimized aircraft using semi-empirical estimations and deterministic multi-disciplinary design optimization (MDO) approaches can be at risk of not being certifiable in the detailed design phase of the life cycle. The focus of this study was to implement a robust and efficient possibility-based design optimization (PBDO) method for the MDO of an eVTOL tilt-wing aircraft in the conceptual design phase, using existing conventional designs as an initial configuration. As implemented, the optimization framework utilizes a deterministic gradient-based optimizer, run sequentially with a possibility assessment algorithm, to select an optimal design. To achieve this, the uncertainties which arise from multi-fidelity calculations, such as semi-empirical methods, are considered and used to modify the final design such that its viability is guaranteed in the detailed design phase. With respect to various requirements, including trim, stability, and control behaviors, the optimized eVTOL tilt-wing aircraft design offers the preferred results which ensure that airworthiness criteria are met whilst complying with predefined constraints. The proposed approach may be used to revise currently available light aircraft and develop eVTOL versions from the original light aircraft. The resulting aircraft is not only an optimized layout but one where the stability of the eVTOL tilt-wing aircraft has been guaranteed.

Structural deterioration of calcium aluminosilicate hydrate by sulfate attack: Atomic processes of Al-Si chain breaking

Calcium-aluminosilicate-hydrates (C-A-S-H) is the major hydration product of sustainable concrete that incorporates industrial waste as a partial substitute for cement. Although C-A-S-H is considered a durable material, it’s susceptible to sulfate attack. To better understand this phenomenon, the atomic process of sulfate attack on C-A-S-H was studied using semi-empirical quantum chemical methods. The results showed that sulfate attacks can be broken down into several subprocesses: adsorption, vibration, and breaking. The underlying causes of these processes were revealed through electronic structures, in which the weak AlO bond and strong electronegativity are the main factors leading to Al-Si breaking. The Atom in Molecular theory revealed that the weak electron localization of the [AlO4] tetrahedron leads to a lower water-assisted bond dissociated energy for breaking the AlO bond (18.3KJ/mol) than the SiO bond (109.4KJ/mol). This study provides valuable insights into the sulfate attack on C-A-S-H and its implications for improving the durability of sustainable concrete.

Semi-empirical determination of radiative parameters for Co II

The values of the radiative parameters for single ionised cobalt were determined using a semi-empirical method. The eigenvector amplitudes determined in our previously published research were adopted. In most cases, the calculated values of oscillator strengths and radiative lifetimes agree well with the experimental data. The results were also compared with the available theoretical calculations.

Pynta─An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics.

Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.

Exploring energy landscapes for solid-state systems with variable cells at the extended tight-binding level.

The design of novel materials requires a theoretical understanding of dynamical processes in the solid state, including polymorphic transitions and associated pathways. The organization of the potential energy landscape plays a crucial role in such processes, which may involve changes in the periodic boundaries. This study reports the implementation of a general framework for periodic condensed matter systems in our energy landscape analysis software, allowing for variation in both the unit cell and atomic positions. This implementation provides access to basin-hopping global optimization, the doubly nudged elastic band procedure for identifying transition state candidates, the missing connection approach for multi-step pathways, and general tools for the construction and analysis of kinetic transition networks. The computational efficacy of the procedures is explored using the state-of-the-art semiempirical method GFN1-xTB for the first time in this solid-state context. We investigate the effectiveness of this level of theory by characterizing the potential energy and enthalpy landscapes of several systems, including silicon, CdSe, ZnS, and NaCl, and discuss further technical challenges, such as translational permutation of the cell. Despite the expected limitations of the semiempirical method, we find that the resulting energy landscapes provide useful insight into solid-state simulations, which will facilitate detailed analysis of processes such as defect and ion migration, including refinement at higher levels of theory.

Shedding light into the biological activity of aminopterin, via molecular structural, docking, and molecular dynamics analyses

In this study, the structural and anticancer properties of aminopterin, as well as its antiviral characteristics, were elucidated. The preferred conformations of the title molecule were investigated with semiempirical AM1 method, and the obtained the lowest energy conformer was then optimized by using density functional (DFT/B3LYP) method with 6-311++G(d,p) as basis set. The vibrational frequencies of the optimized structure were calculated by the same level of theory and were compared with the experimental values. The vibrational assignments were performed based on the computed potential energy distribution (PED) of the vibrational modes. The molecular electrostatic potential (MEP) and frontier molecular orbitals (HOMO, LUMO) analyses were carried out for the optimized structure and the chemical reactivity has been scrutinized. To enlighten the biological activity of aminopterin as anticancer and anti-COVID-19 agents, aminopterin was docked into DNA, αIIBβ3 and α5β1integrins, human dihydrofolate reductase, main protease (Mpro) of SARS-CoV-2 and SARS-CoV-2/ACE2 complex receptor. The binding mechanisms of aminopterin with the receptors were clarified. The molecular docking results revealed the strong interaction of the aminopterin with DNA (−8.2 kcal/mol), αIIBβ3 and α5β1 integrins (−9.0 and −10.8 kcal/mol, respectively), human dihydrofolate reductase (−9.7 kcal/mol), Mpro of SARS-CoV-2 (−6.7 kcal/mol), and SARS-CoV-2/ACE2 complex receptor (−8.1 kcal/mol). Moreover, after molecular docking calculations, top-scoring ligand-receptor complexes of the aminopterin with SARS-CoV-2 enzymes (6M03 and 6M0J) were subjected to 50 ns all-atom MD simulations to investigate the ligand-receptor interactions in more detail, and to determine the binding free energies accurately. The predicted results indicate that the aminopterin may significantly inhibit SARS-CoV-2 infection. Thus, in this study, as both anticancer and anti-COVID-19 agents, the versatility of the biological activity of aminopterin was shown. Communicated by Ramaswamy H. Sarma

Probabilistic landslide tsunami modeling of the 2018 Palu Bay event

On September 28, 2018, a Mw 7.5 earthquake triggered near Central Sulawesi generated a highly destructive tsunami within Paul Bay (Indonesia). Field surveys and various studies conducted after the event showed that, as a result of the earthquake, several large submarine landslides were triggered along the shores of the bay, that significantly contributed to tsunami generation. The estimated geometry and other parameters for these slides, however, were affected by a large uncertainty. Here, we present a probabilistic tsunami hazard analysis of this event, based on Monte Carlo simulations using a linear Mild Slope Equation (MSE) model combined with a Green’s function approach, that allow efficiently simulating a large number of stochastic landslide tsunami generation and propagation scenarios within Palu Bay, for each of the identified landslides. In the MSE model, a space and time-dependent source term is used to represent the seafloor motion associated with each landslide scenario. In the Green’s function approach, a large database of elementary solutions and their tsunami elevation at a large number of coastal save points is pre-computed for tsunamis generated by a unit seafloor acceleration specified over a small area. Then, given an actual submarine landslide scenario, with a specific acceleration function, the tsunami elevation at the save points is simply and efficiently computed as a weighed linear superposition of the elementary solutions. Tsunami runup is finally obtained using a semi-empirical method, based on results computed at save points for each landslide scenario. The model is applied to the 2018 Palu Bay tsunami event, allowing to investigate how the uncertainty in landslide parameters affects tsunami hazard. A comparison with observed runups is made, which shows that these fall within the range of uncertainty of the simulated probabilistic runups.

Semiempirical Method for Determining the Velocities of Phases During Fluidization and Settling in a Self-Similar Region

Semiempirical Method for Determining the Velocities of Phases During Fluidization and Settling in a Self-Similar Region

Time-dependent dielectric density functional theory predictions for excited-state molecular properties using neural networks guided by lower-level quantum chemistry

This paper discusses a neural network approach for the prediction of ground- and excited-state molecular properties. The time-dependent dielectric density functional theory was employed to handle the excited state of coumarin-152 dye. We demonstrated that lower-level quantum chemistry techniques, such as the semiempirical method and the Hartree–Fock approximations, can be used to improve the predictive performance of neural network models. The semiempirical quantum chemistry method provided excellent feature vectors (descriptors) on excited-state molecular properties in terms of computational cost and performance. In addition, a vector-based descriptor to improve molecular-force predictions is discussed.