Charge Model Research Articles

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains.

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Ionic liquids intercalation in titanium carbide MXenes: A first-principles investigation.

Herein, we present a density functional theory with dispersion correction (DFT-D) calculations that focus on the intercalation of ionic liquids (ILs) electrolytes into the two-dimensional (2D) Ti3C2Tx MXenes. These ILs include the cation 1-ethyl-3-methylimidazolium (Emim+), accompanied by three distinct anions: bis(trifluoromethylsulfonyl)imide (TFSA-), (fluorosulfonyl)imide (FSA-) and fluorosulfonyl(trifluoromethanesulfonyl)imide (FTFSA-). By altering the surface termination elements, we explore the intricate geometries of IL intercalation in neutral, negative, and positive pore systems. Accurate estimation of charge transfer is achieved through five population analysis models, such as Hirshfeld, Hirshfeld-I, DDEC6 (density derived electrostatic and chemical), Bader, and VDD (voronoi deformation density) charges. In this work, we recommend the DDEC6 and Hirshfeld-I charge models, as they offer moderate values and exhibit reasonable trends. The investigation, aimed at visualizing non-covalent interactions, elucidates the role of cation-MXene and anion-MXene interactions in governing the intercalation phenomenon of ionic liquids within MXenes. The magnitude of this role depends on two factors: the specific arrangement of the cation, and the nature of the anionic species involved in the process.

A flux-based moving mesh method applied to solving the Poisson–Nernst–Planck equations

The moving mesh method is one of the important adaptive mesh methods which is practically useful when the mesh size and overall resolution are provided and fixed as seen in many engineering computations. We employ a moving mesh technique combined with a finite element method (FEM) to solve a widely-used charged carrier transport model, the Poisson–Nernst–Planck (PNP) equations, the solutions of which often have boundary or internal layers and sharp interfaces when the convection is dominated. Considering that flux is a significant physical quantity in designing stable and accurate numerical algorithm for transport problems such as the PNP system, we start from a relatively general functional and propose a flux-based monitor function and an adaptive step size-controlling algorithm for guiding the mesh movement in the FEM solution of the PNP equations. The numerical results illustrate that the moving mesh method is effectively addressing challenges arising from solution singularities and the convection-dominated effects. It also shows that the moving mesh finite element method we propose exhibits superior performance compared to both the traditional moving mesh finite element method and fixed mesh finite element method in some scenarios. Furthermore, it demonstrates better adherence to the physical properties of energy dissipation inherent in the PNP equation.

Derivation of Morse potential

The Morse potential is widely used in chemistry, physics, and material science to describe interatomic interactions. We show that the Morse potential can be derived from a simple screened charge model accounting for the shielding of the nuclear charge by electron density. In this model, the parameter governing the width of the potential is related to the average shielding strength and the exponents of the atomic orbitals used in electronic structure calculations. The parameter representing the bond dissociation energy of a diatomic molecule is derived by combining the quantum mechanical covalent and classical electrostatic interactions. Moreover, the revealed connection between the parameters of the Morse potential and Pauling’s bond order and electronegativity provides insight into the relation between the classical and quantum mechanical descriptions of chemical bonds. This derivation of the Morse potential from the atomic quantities is expected to contribute to the development of the new basis sets incorporating Slater-type orbitals for electronic structure calculations and the transferable reactive force fields for molecular dynamics simulations. Furthermore, this interpretation of the Morse potential helps to understand the relationships between widely used chemical concepts, including effective atomic charge, bond dissociation energy, electronegativity, and bond order.

Time-dependent Kohn-Sham electron dynamics coupled with nonequilibrium plasmonic response via atomistic electromagnetic model.

Computational modeling of plasmon-mediated molecular photophysical and photochemical behaviors can help us better understand and tune the bound molecular properties and reactivity and make better decisions to design and control nanostructures. However, computational investigations of coupled plasmon-molecule systems are challenging due to the lack of accurate and efficient protocols to simulate these systems. Here, we present a hybrid scheme by combining the real-time time-dependent density functional theory (RT-TDDFT) approach with the time-domain frequency dependent fluctuating charge (TD-ωFQ) model. At first, we transform ωFQ in the frequency-domain, an atomistic electromagnetic model for the plasmonic response of plasmonic metal nanoparticles (PMNPs), into the time-domain and derive its equation-of-motion formulation. The TD-ωFQ introduces the nonequilibrium plasmonic response of PMNPs and atomistic interactions to the electronic excitation of the quantum mechanical (QM) region. Then, we combine TD-ωFQ with RT-TDDFT. The derived RT-TDDFT/TD-ωFQ scheme allows us to effectively simulate the plasmon-mediated "real-time" electronic dynamics and even the coupled electron-nuclear dynamics by combining them with the nuclear dynamics approaches. As a first application of the RT-TDDFT/TD-ωFQ method, we study the nonradiative decay rate and plasmon-enhanced absorption spectra of two small molecules in the proximity of sodium MNPs. Thanks to the atomistic nature of the ωFQ model, the edge effect of MNP on absorption enhancement has also been investigated and unveiled.

Impact of Tolman–Kuchowicz potentials on Gauss–Bonnet gravity and isotropic stellar structures

In this manuscript, we study the behavior of charged isotropic compact stellar objects within the framework of modified f(G) theory of gravity by considering the Tolman–Kuchowicz spacetime. We use some matching condition of spherically symmetric space–time with Bardeen’s model as an exterior geometry and examine the physical behavior of stellar structures. In the current analysis, we discuss the energy conditions to check the viability of our model. Many physical aspects have been examined, such as energy density, pressure evolution, equation of state parameter, and causality condition. Furthermore, an equilibrium condition can be visualized through the modified Tolman–Oppenheimer–Volkov equation. Further, we study mass–radius function, compactness and redshift function, which are some essential features of charged compact star model. It is worthwhile to mention here for the current study that our stellar structure in the background of Bardeen’s model is more viable and stable.

A neural network inverse problem solution for a MIMO magnetic levitation actuator

A multiple-input and multiple-output planar actuator is proposed, which utilizes mechanically driven stator magnet arrays to levitate a permanent magnet mover. A state of levitation and actuation is obtained by mechanically altering the orientation of the stator magnets to control the forces and torques on the mover. A challenge for the design and control of the actuator is inverting the relationship between the force and stator magnet rotation angles, as there is no closed-form analytical solution. In this study, a feed-forward neural network is applied to model the forward relation between stator magnet angle input and a force and torque output to reduce the forward computation time for the design process and for error estimation in real-time applications. Additionally, the neural network is considered for inverting the solution for a motion profile sampled at 1000 Hz. The developed forward model is able to calculate the forces and torques on the mover a factor 10 faster than the equivalent charge or Fourier model with an absolute error of 3 mN and 0.1 mNm for the forces and torques, respectively, and a feed-forward neural network is able to accurately learn an inverse solution for small motion profiles.

Learning Electronic Polarizations in Aqueous Systems.

The polarization of periodically repeating systems is a discontinuous function of the atomic positions, a fact which seems at first to stymie attempts at their statistical learning. Two approaches to build models for bulk polarizations are compared: one in which a simple point charge model is used to preprocess the raw polarization to give a learning target that is a smooth function of atomic positions and the total polarization is learned as a sum of atom-centered dipoles and one in which instead the average position of Wannier centers around atoms is predicted. For a range of bulk aqueous systems, both of these methods perform perform comparatively well, with the former being slightly better but often requiring an extra effort to find a suitable point charge model. As a challenging test, we also analyze the performance of the models at the air-water interface. In this case, while the Wannier center approach delivers accurate predictions without further modifications, the preprocessing method requires augmentation with information from isolated water molecules to reach similar accuracy. Finally, we present a simple protocol to preprocess the polarizations in a data-driven way using a small number of derivatives calculated at a much lower level of theory, thus overcoming the need to find point charge models without appreciably increasing the computation cost. We believe that the training strategies presented here help the construction of accurate polarization models required for the study of the dielectric properties of realistic complex bulk systems and interfaces with ab initio accuracy.

Synthesis of 1D LaFeO3 nanofibers/2D MXene heterostructures for formaldehyde detection at low temperature

Gas sensors play an indispensable role in air quality monitoring. In this paper, the LaFeO3 nanofibers/Ti3C2TX MXene composite sensor composed of one-dimensional (1D) porous LaFeO3 nanofibers and two-dimensional (2D) Ti3C2TX MXene nanosheets was prepared by electrospinning and electrostatic self-assembly technology. The morphology, structure and phase composition were analyzed by various techniques. The results show that the diameter of LaFeO3 nanofibers is about 150 nm, and the fibers are evenly distributed on the surface of the accordion-like MXene multilayer nanosheets. Gas-sensing test data show that compared with pure LaFeO3, LaFeO3 nanofibers/Ti3C2TX MXene composite sensor has excellent response value (18.86) and better stability to formaldehyde gas. Besides, its optimal operating temperature is reduced from the original 180 ℃ to 60 ℃, which widens its practical application range. On this basis, the sensing mechanism is discussed in detail by surface oxygen adsorption and space charge model, and the density functional theory (DFT) is used to analyze the sensing mechanism. The improvement of the response value is mainly attributed to the unique morphology and structure, the increase of specific surface area and the formation of p-p heterojunction. This study provides a new idea and innovative way for LaFeO3-based gas sensors.

Miller capacitance reduction in negative capacitance TFETs

ABSTRACT A dielectric stacked hetero-gate topology is proposed to reduce the Miller capacitance in a negative capacitance double gate TFET. An analytical charge-based model is introduced for the analysis of the terminal capacitance behaviour of the device. Charge and capacitance models are developed using the surface potential modelling approach by solving the Poisson Equation. The proposed closed-form charge model is SPICE-compatible and can be used for circuit simulations. The model accuracy is validated by combining one-dimensional Landau–Khalatnikov equation with the two-dimensional simulations of the double gate stacked-hetero-gate tunnel field effect transistor. The model shows excellent agreement with device simulations. The proposed topology exhibits Miller capacitance reduction of 30% which results in the gain band width product improvement of 43% more than the stacked homo-gate counterpart.

The Li battery digital twin – Combining 4D modelling, electro-chemistry, neutron, and ion-beam techniques

Knowledge driven materials and component design is key for improving the performance of Li-ion batteries and solving the remaining hurdles for next-generation battery concepts like all-solid-state batteries. While the spatial and time dependent distribution of Li would help elucidating performance bottlenecks and degradation phenomena, only a few analysis techniques are available, due to the unique nature of Li, especially as Li+-ion. In fact, only two non-destructive techniques with good time resolution can combine spatial information with absolute quantification of Li, one being Neutron Depth Profiling (NDP), the other Ion-Beam-Analysis (IBA). While both exploit nuclear processes, the information gained is complementary. NDP provides high depth resolution, but only limited lateral resolution, whereas IBA has high lateral, but only limited depth resolution. In this study, we benchmark both techniques for the first time using a set of Li-battery test-samples and show the strengths and synergies of both techniques. The derived information regarding the depth dependent Li-concentration is then used to validate a microstructure resolved continuum model of charge, discharge, and relaxation behavior of the cells and electro-chemical analysis. This fundamental work demonstrates a new route to optimize Li batteries on material and component level by combining advanced characterization and digital twin modelling.

Coordinate-Free and Low-Order Scaling Machine Learning Model for Atomic Partial Charge Prediction for Any Size of Molecules.

The atomic partial charge is of great importance in many fields, such as chemistry and drug-target recognition. However, conventional quantum-based computing of atomic charges is relatively slow, limiting further applications of atomic charge analysis. With the help of machine learning methods, various kinds of models appear to speed up atomic charge calculations. However, there are still some concerning problems. Some models based on geometric coordinates require high-accuracy geometry optimization as a preprocess, while other models have a limitation on the size of input molecules that narrow the applications of the model. Here, we propose a machine learning atomic charge model based on a message-passing featurizer. This preprocessing featurizer can quickly extract atomic environment information from a molecule according to the connectivity inside the molecule. The resulting descriptor can be used with a neural network to quickly predict the atomic partial charge. The model is able to automatically adapt to any size of molecule while remaining efficient and achieves a root-mean-square error in the Hirshfeld charge prediction of 0.018e, with an overall time complexity of O(n2). Thus, this model could enlarge the range of applications of atomic partial charge to more fields and cases.

Multi-stage layout strategies of intercity charging and swapping infrastructure for electric logistics vehicles: random, radical, or conservative?

The arrangement of charging infrastructure has become an urgent issue as electric logistics vehicles (ELVs) transition from urban areas to intercity operations. In this study, we established a multi-stage layout model for intercity charging and swapping infrastructure (CSI) based on the travel characteristics of electric logistics vehicles, to minimize the investment cost of operators and the time cost of vehicle users in Hubei Province, China. Furthermore, the effect of “random”, “conservative”, and “radical” layout strategies for the infrastructure deployment was analyzed based on an improved genetic algorithm. It is found that the random layout strategy leads to a balance between infrastructure operators and users with the lowest total cost, which is 81.2% and 88.8% of the costs based on the radical strategy and the conservative strategy, respectively. The conservative strategy could reduce investment risks for operators with the lowest investment cost, while the radical strategy prioritizes the convenience of electric logistics vehicles, with the lowest user time cost. The case study suggests that choosing conservative strategies can bring benefits when the budget is limited, while radical strategies have more advantages when the budget is abundant. Random strategies bring the best overall social benefits. The configuration of CSI and travel characteristics of ELVs play important roles in determining layout strategies.

Understanding the Origins of Quark Charges, Quantum of Magnetic Flux, Planck’s Radiation Constant and Celestial Magnetic Moments with the 4G Model of Nuclear Charge

Introduction: In our previous published papers, considering 3 large atomic gravitational constants assumed to be associated with weak, strong and electromagnetic interactions, we have proposed the existence of a nuclear charge of magnitude, en=2.95e and developed a nuclear mass formula associated with strong and weak interactions having 4 simple terms and only one energy coefficient. Methods: Two important assumptions are that there exists a weak fermion of rest energy 585 GeV and a strong coupling constant is the squared ratio of electromagnetic charge and nuclear charge. The aim of this paper is associated with understanding the mystery of the quantum of magnetic flux, Planck’s quantum radiation constant and Reduced Planck’s constant. Proceeding further, quark charges, strong coupling constant, nuclear stability, nuclear binding energy, medium and heavy atomic X-ray levels and celestial magnetic moments can be understood in a unified approach. It may also be noted that, by considering the integral nature of elementary particle masses, it seems possible to understand the discreteness of angular momentum. Results: Considering our proposed en=2.95e=3e as a characteristic nuclear charge, it seems possible to understand the integral nature of quarks electromagnetic charge. With this idea, neutron, proton and pion decay can be understood very easily. Conclusion: In all the cases, the up quark of charge (±2e) seems to play a crucial role in the internal transformation of the down quark of charge (±e) and external observable elementary basic elementary particles. It needs further study at the fundamental level.

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.

Conformational Dynamics in Corynebacterium glutamicum Diaminopimelate Epimerase: Insights from Ligand Parameterization, Atomistic Simulation, and Markov State Modeling.

The microbial enzyme diaminopimelate epimerase (DapF), a vital enzyme in the lysine biosynthetic pathway, catalyzes the conversion of L, L-diaminopimelate (L, L-DAP) to D, L-diaminopimelate (D, L-DAP) using a catalytic cysteine dyad with one cysteine in thiol state and another in thiolate. Under oxidizing conditions, the catalytic cysteines of apo DapF form a disulfide bond that alters the structure and function of DapF. Given its potential as a target for antimicrobial resistance treatments, understanding DapF's functional dynamics is imperative. In the present work, we employ microsecond-scale all-atom molecular dynamics simulations of product-bound DapF and apo-DapF under oxidized and reduced conditions. We employ a polarized charge model for the ligand and the active site residues, which was necessary to preserve the electrostatic environment in the active site and retain the ligand in the active site. The product-bound DapF and apo-DapF in oxidized and reduced conditions exhibit a closed, semi-open, and open conformation, respectively, as identified using the internal coordinates of the dimeric enzyme and the principal component analysis. The conformational switch is guided by the dynamic catalytic (DC) loop, loop II, and loop III movements in the active site. The time scale of the close-to-open conformational transition is estimated to be 0.8 μs through Markov state modeling (MSM) and transition path theory (TPT). The present study explains the role of various active site residues and loops in ligand binding and protein dynamics in the DapF enzyme under different redox conditions. Such information will be helpful in future inhibitor design studies targeting the DapF enzyme.

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %–26.3 %, and increases Charging pile revenue.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Effect of the refrigerant charge on transcritical CO2 direct evaporation ice-making system

The low global warming potential and zero ozone depletion potential of CO2 make it highly suitable for application in ice rinks. The refrigerant charge prediction model of the transcritical CO2 direct evaporation ice-making system is proposed and compared. According to the predictive results, the performance of the ice-making system was experimentally studied at three predictive refrigerant charges. The results show that the ice-making system with the refrigerant charge predicted by the comprehensive model with GC + ECH (gas cooler refrigerant charge model and evaporator refrigerant charge model with Hughmark correlations) presents the optimum performance. The refrigerant charge in the pipeline accounts for 46.6 %–57.2 % of the total refrigerant charge, so proper pipeline arrangements are crucial to reduce the overall refrigerant charge, especially before and after the throttle valve. The experiment data shows that the optimal high-side pressure of the ice-making system is between 7.9 MPa and 8.3 MPa, and the optimal COP (coefficient of performance) is between 1.34 and 1.75. Excessive or insufficient refrigerant charge leads to overall superheating or overall unsaturation of the refrigerant at the outlet of the evaporator. At the proposed refrigerant charge, both cooling capacity and COP increase by 8.3 %–38.3 % and 21.5 %–30.6 % respectively compared to other charges tested. In addition, the ice-making time at the proposed refrigerant charge can be saved by 10.0 %–15.6 %.

Pulsed Electron Lenses for Space Charge Mitigation.

To produce ultimate high-brightness hadron beams, synchrotrons need to overcome a most prominent intensity limitation, i.e., space charge. This Letter characterizes the potential of pulsed electron lenses in detailed 3D tracking simulations, key to which is a realistic machine and space charge model. The space charge limit, imparted by betatron resonances, is shown to be increased by up to 50% using a low symmetric number of electron lenses in application to the Facility for Antiproton and Ion Research SIS100 synchrotron. Conceptually, a 100% increase is demonstrated with a larger number of electron lenses, which is found to rapidly saturate near the theoretical 2D limit.