Atomic Charges Research Articles

Single-Atom Substituents in Copper Surfaces May Adsorb Multiple CO Molecules.

Copper is a good CO2 electroreduction catalyst as products beyond CO form, but efficiency and selectivity is low. Experiments have shown that an admixture of other elements can help, and computational screening studies have pointed out various promising candidates based on the adsorption of a single CO molecule as a descriptor. Our calculations of CO adsorption on surfaces where a first row transition metal atom replaces a Cu atom show that multiple CO molecules, not just one, bind to the substitutional atom. For Fe, Co, and Ni atoms, a decrease in binding energy is found, but the reverse trend, namely, increasing bond strength, is found for V, Cr, and Mn and the first three CO molecules. Magnetic moment, charge, and position of the substitutional atom are also strongly affected by the CO adsorption in most cases. Magnetic moment is stepwise reduced to zero, and the outward displacement of the substitutional atom increased.

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.

Eggshell-Like Carbon Microspheres with Curvature Scheme: Distorted Energy Band and Atomic Charge Waves-Driven High Performance for Zinc-Air Battery.

A metal-free nanocarbon with an eggshell structure is synthesized from chitosan (CS) and natural spherical graphite (NSG) as a cathode electrocatalyst for clean zinc-air batteries and fuel cells. It is developed using CS-derived carbons as an eggshell, covering NSG cores. The synthesis involves the in situ growth of CS on NSG, followed by ammonia-assisted pyrolysis for carbonization. The resulting catalyst displays a curved structure and completely coated NSG, showing superior oxygen reduction reaction (ORR) performance. In 1 M NaOH, the ORR half-wave potential reached 0.93 V, surpassing the commercial Pt/C catalyst by 50 mV. Furthermore, a zinc-air battery featuring the catalyst achieves a peak power density of 167 mW cm-2 with excellent stability, outperforming the Pt/C. The improved performance of the eggshell carbons can be attributed to the distorted energy band of the active sites in the form of N-C moieties. More importantly, the curved thin eggshells induce built-in electric fields that can promote electron redistribution to generate atomic charge waves around the N-C moieties on the carbon shells. As a result, the high positively charged and stable C+ sites adjacent to N atoms optimize the adsorption strength of oxygen molecules, thereby facilitating performance.

Electromechanical strain response of phosphorene nanotubes

Abstract Nanomaterials that undergo structural or other property changes upon application of external stimuli are called stimuli responsive materials and are particularly suited for drug delivery, biosensing or artificial muscle applications. Two-dimensional (2D) black phosphorus is an ideal material for such applications due to its remarkable electromechanical response. Given that one-dimensional (1D) black phosphorus nanotubes (PNTs) are calculated to be energetically stable, it is possible that they can undergo similar electromechanical responses to their 2D counterparts, allowing their potential application as nanochannel devices for drug delivery. Using first-principles density functional theory, we investigated the electromechanical response of different-sized PNTs upon charge injection. Upon hole injection, the diameter of the PNTs expands up to a maximum of 30.2% for a (0,15) PNT that is 0.24 nm in diameter. The PNTs become highly p-doped as the valence band maximum crosses the Fermi level and undergoes switching from a direct to indirect band gap. The mechanism behind the electromechanical response was determined through analysis of the structural deformations, charge density distribution and Bader partial charges. It was shown that injection of charge alters the Young’s Modulus of the PNTs, as hole injection weakens the structural integrity of the nanotube, allowing a greater electromechanical response, with PNT-15 showing the largest decrease in the Young’s Modulus of 15.34%. These findings show that 1D PNTs are promising materials for the development of nanoelectromechanical actuators which could be used for drug delivery, energy harvesting or similar applications.

2,4-dichloro-6-(1,4,5-triphenyl-1H-imidazol-2-yl) phenol: synthesis, DFT analysis, Molecular docking, molecular dynamics, ADMET properties against COVID-19 main protease (Mpro: 6WCF/6Y84/6LU7)

ABSTRACT New derivatives of 2,4-dichloro-6-(1,4,5-triphenyl-1H-imidazol-2-yl) phenol (DPIP) have been successfully synthesised and characterised using spectral methods such as FT-IR, 1H NMR and 13C NMR. Density functional theory (DFT) approach at B3LYP/6-311 G (d, p) level of theory is used to determine optimised bond parameters and single crystal XRD investigation of related derivatives confirms the structure of DPIP bond parameters. The single crystal XRD measurements and the optimised geometrical parameters produced by the DFT calculation agree well. The FT-IR bands seen in the experiment were attributed to distinct normal modes of the molecule. Frontier molecular orbital computations described the molecule stability, chemical reactivity and charge transfer. Atomic charges determined via Mulliken population analysis on the different DPIP atoms. MEP, which is mapped to the electron density surfaces, has discovered potential reactive sites of the molecule. The reported molecule is used as a potential NLO material since it has a high μβ0 value. Binding affinities were discovered using molecular docking against the COVID-19 major protease (Mpro: 6WCF/6Y84/6LU7). The behaviour of the complex structure formed by the Covid-19 protein under in silico physiological conditions was then confirmed by a 100 ns molecular dynamic simulation which looked at the structure stability over time and revealed a stable conformation and binding pattern in an environment of imidazole derivatives. Furthermore, favourable to moderate anti-viral activity was revealed by an in-silico analysis that anticipated the compound absorption, distribution, metabolism, excretion and toxicity profiles (ADMET).

DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference.

We present a modification to self-consistent charge density functional-based tight binding (SCC-DFTB), which allows computation based on approximate atomic charges. We obtain these charges by means of a machine learning (ML) process that combines a Coulomb model with a neural network. This allows us to avoid the SCC cycles in the SCC-DFTB calculation while maintaining its accuracy. The main input of the model is the atomic positions characterized by a set of atom-centered symmetry functions. The charge inference from our ML algorithm is as close as 10-2 units of charge from the exact SCC solution. Our ML-DFTB approach provides a good approximation of the density matrix and of the energy and forces with only a single diagonalization. This is a significant computational saving with respect to the complete SCC algorithm, which allows us to investigate a bigger ensemble of atoms. We show the quality of our approach in the case of charged silicon carbide (SiC) clusters. The ML-DFTB potential energy surface (PES) mimics the SCC-DFTB PES rather well, despite its simplicity. This allows us to obtain the same geometric structure ordering with respect to energy for small clusters. The dissociation barriers for ion emission are well-reproduced, which opens the way to investigating ion field emission and charged cluster stability. The ML-DFTB approach is obviously not limited to charged clusters or SiC materials. It opens a new route to investigate larger clusters than those investigated by standard SCC-DFTB, as well as surface and solid-state chemistry at the atomic level.

Upgrading Electrolyte Antioxidant Chemistry by Constructing Potential Scaling Relationship.

Rational design of advanced electrolytes to improve the high-voltage capability has been attracting wide attention as one critical solution to enable next-generation high-energy-density batteries. However, the limited understanding of electrolyte antioxidant chemistry as well as the lack of valid quantization approaches have resulted in knowledge gap, which hinders the formulation of new electrolytes. Herein, we construct a standard curve based on representative solvation structures to quantify the oxidation stability of ether-based electrolytes, which reveals the linear correlation between the oxidation potential and the atomic charge of the least oxidation-resistant solvent. Dictating by the regularity between solvation composition and oxidation potential, a (Trifluoromethyl)cyclohexane-based localized high-concentration electrolyte dominated by anion-less solvation structures was designed to optimize the cycling performance of 4.5 V 30-μm-Li||3.8-mAh cm-2-LiCoO2 batteries, which maintained 80% capacity retention even after 440 cycles. The consistency of experimental and computational results validates the proposed principles, offering a fundamental guideline to evaluate and design aggressive electrochemical systems.

Ambiguities in Decomposing Molecular Polarizability into Atomic Charge Flow and Induced Dipole Contributions.

The molecular dipole polarizability can be decomposed into components corresponding to the charge flow between atoms and changes in atomic dipole moments. Such decompositions are recognized to depend on how atoms are defined within a molecule, as, for example, by Hirshfeld, iterative Stockholder, or quantum topology partitioning of the electron density. For some of these, however, there are significant differences between the numerical results obtained by analytical response methods and finite field calculations. We show that this difference is due to analytical response methods accounting for (only) the change in electron density by a perturbation, while finite field methods may also include a component corresponding to a perturbation-dependent change in the definition of an atom within a molecule. For some atom-in-molecule definitions, such as the iterative Hirshfeld, iterative Stockholder, and quantum topology methods, the latter effect significantly increases the charge flow component. The decomposition of molecular polarizability into atomic charge flow and induced dipole components thus depends on whether the atom-in-molecule definition is taken to be perturbation-dependent.

Low‐intensity continuous ultrasound effect on proliferation and morphology of fibroblast cells

AbstractRecently, the use of ultrasound (US) for triggering drug release to specific tissues was explored, but its direct effects on cells have not been thoroughly understood. For this reason, this study aimed to investigate the impact of US powers and US irradiation times on fibroblast cells (NIH‐3T3). The results showed that the diverse US settings had varying effects on cell proliferation and distribution in the polystyrene culture dish. Interestingly, at 10 W, 43 kHz with changing exposed time up to 30 min either stimulated or inhibited fibroblast cell growth after 24 and 72 h of cultivation compared to the control sample in the absence of US, while longer US exposure time led to a moderate reduction in cell quantity. Moreover, higher US levels of 20 and 30 W could cause an aggregation of cells and sublethal damage to the cells. Importantly, the morphology of fibroblast was changed from stellate‐shape to round‐shape under greater US powers. Elevated US power also influenced interactions between proteins and lipids, affecting the atomic and molecular charges, leading to changes in both zeta potential and pH of the dispensed cell solution.

Development of a ReaxFF reactive force field for ternary phosphate-based bioactive glasses.

Phosphate-based glasses (PBGs) in the CaO-Na2O-P2O5 system have diverse applications as biomaterials due to their unique dissolution properties. A reactive force field (ReaxFF) has been developed to simulate these materials at the atomic level. The ReaxFF parameters of PBGs, including the interaction between phosphorus and calcium atoms, have been developed using a published code based on genetic algorithms. The training data, including the atomic charges, atomic forces, bond and angle parameters, and different differential energies, are chosen and measured from static quantum-mechanical calculations and abinitio molecular dynamics of PBGs. We did different short- and medium-range structural analyses on the bulk simulated PBGs with different compositions to validate the developed potential. Radial and angular distribution functions and coordination numbers of network formers and modifiers, as well as the network connectivity of the glass, are in agreement with experimental and previous simulations using both shell-model classical force fields and abinitio simulated data; for example, the coordination number of phosphorus is 4.0. This successful development of ReaxFF parameters being able to describe the bulk PBGs enables us to work on the dissolution behavior of the glasses, including the interaction of water molecules with PBGs, in future works.

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.

Solvation structure design for stabilizing MXene in transition metal ion solutions

AbstractAlthough MXene has attracted great interest in diverse fields, it is susceptible to oxidation in water (H2O) with transition metal ions such as Co2+, Fe2+, and Cu2+, which is pronounced at high temperatures. This impedes the preparation of MXene‐based composites and their functional applications. Here, this study revealed that Co2+ increases the maximum and average atomic charge of H in H2O to improve the reactivity of H2O, which leads to the fact that Co2+ catalyzes the oxidation of Ti3C2Tx MXene. Furthermore, the addition of N,N‐dimethyl formamide (DMF) reduces the H2O activity and improves the oxidation stability of Ti3C2Tx in the presence of Co2+ via preferentially forming coordination bonds with Co2+. This strategy is also effective in enhancing the oxidation tolerance of Ti3C2Tx to Fe2+ in H2O. Moreover, it is feasible to enhance the oxidation stability of Ti2CTx MXene in H2O with the existence of Co2+. By virtue of these, the CoO/Ti3C2Tx composite was successfully prepared without obvious Ti3C2Tx oxidation, which is desirable to harness the advantages of Ti3C2Tx as the complementary component for lithium‐ion batteries. This work provides a straightforward paradigm to enhance the oxidation resistance of MXene in H2O in the presence of transition metal ions and at high temperatures, which opens a new vista to use MXene for target applications.

Disentangling Acid-Base Chemistry through Blue Shifting Hydrogen Bond Contributions.

The blue shifting of vibrational frequencies in hydrogen bonded molecules, as observed in aqueous environments, has been attributed to local partial charge transfer from solvation. Here, we extrapolate the blue shift model to the stronger ionic interactions between hydrogen bond acceptors associated with protonation through augmented pH levels and competitive interactions with counter ion pairing. The chemical model we utilize in this work is the aqueous pyridine-pyridinium equilibrium to characterize the blue shifts observed in the pyridinium chloride ionic system. The observed agreement between observed experimental and calculated spectral shifts shows that the blue shifting model can be extrapolated to stronger interactions and accurately describe the nature of the hydrogen bond.

Structural and Functional Characterization of Medicinal Plants as Selective Antibodies towards Therapy of COVID-19 Symptoms

Considering the COVID-19 pandemic, this research aims to investigate some herbs as probable therapies for this disease. Achillea millefolium (Yarrow), Alkanet, Rumex patientia (Patience dock), Dill, Tarragon, and sweet fennel, including some principal chemical compounds of achillin, alkannin, cuminaldehyde, dillapiole, estragole, and fenchone have been selected. The possible roles of these medicinal plants in COVID-19 treatment have been investigated through quantum sensing methods. The formation of hydrogen bonding between the principal substances selected in anti-COVID natural drugs and Tyr-Met-His (the database amino acids fragment), as the active area of the COVID protein, has been evaluated. The physical and chemical attributes of nuclear magnetic resonance, vibrational frequency, the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy, partial charges, and spin density have been investigated using the DFT/TD-DFT method and 6-311+G (2d,p) basis set by the Gaussian 16 revision C.01 program toward the industry of drug design. This research has exhibited that there is relative agreement among the results that these medicinal plants could be efficient against COVID-19 symptoms.

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

Transferable Anisotropic Mie Potential Force Field for Alkanediols.

The development of force fields for polyfunctional molecules, such as alkanediols, requires a careful account of different average intramolecular conformations for gas states compared to dense liquid states, where intra- and intermolecular hydrogen bonds compete. In the present work, the transferable anisotropic Mie (TAMie) potential is extended to 1,n-alkanediols. Using the convention that intramolecular nonbonded interactions up to and including the third neighbor are excluded, all force field parameters developed previously for 1-alcohols were transferred to 1,5-pentanediol and beyond, with good agreement with experimental phase equilibrium data. To obtain trans-gauche ratios of 1,2-ethanediol and 1,3-propanediol that are consistent with experimental results, the propensities for intra- and intermolecular hydrogen bonds had to be balanced. This was achieved by parameterizing the intramolecular dihedral energy functions governing the O-C-C-O and O-C-C-C angles while intramolecular charge-charge interactions were active. All partial charges belonging to a functional group are collected in a charge group and all interactions among two charge groups are evaluated even if they are separated by less than three bonds. With this approach, it is possible to apply the nonbonded parameters from 1-alcohols to alkanediols without further refinement. The agreement with experimental phase equilibrium and shear viscosity data is of similar quality as for the 1-alcohols and the trans-gauche ratio agrees with literature results from spectroscopic measurements and ab initio calculations.

Optimization of the OPLS force field for “water-in-salt” electrolytes

Water-in-salt (WIS) electrolytes have attracted considerable interest in rechargeable batteries and supercapacitors, owing to their wide electrochemical stability window, non-flammability, and safety. A comprehensive understanding of the microstructure in WIS electrolytes provides a crucial theoretical foundation for their further development. In this work, the optimized potentials for liquid simulations (OPLS) force field parameters have been revisited by scaling atom charges to consider their ionic polarization. The ionic conductivity of different WIS electrolytes, calculated using the optimized force field, aligns well with the experimental observations. The polarization effects of ions exhibit augmentation as their size enlarges, which is caused by the enlargement of ion size inducing the asymmetry in the charge distribution. Additionally, the microstructures indicate that nitrates and perchlorates offer superior ionic conductivity, benefiting from a less ionic coordination number and lower viscosity. These insights could be of assistance in selecting force fields and comprehending the microstructure of WIS electrolytes.

Interpretation of molecular electron transport in ab initio many-electron framework incorporating zero-point nuclear motion effects.

A computational methodology, founded on chemical concepts, is presented for interpreting the role of nuclear motion in the electron transport through single-molecule junctions (SMJ) using many-electron ab initio quantum chemical calculations. Within this approach the many-electron states of the system, computed at the SOS-ADC(2) level, are followed along the individual normal modes of the encapsulated molecules. The inspection of the changes in the partial charge distribution of the many-electron states allows the quantification of the electron transport and the estimation of transmission probabilities. This analysis improves the understanding of the relationship between internal motions and electron transport. Two SMJ model systems are studied for validation purposes, constructed from a conductor (BDA, benzene-1,4-diamine) and an insulator molecule (DABCO, 1,4-diazabicyclo[2.2.2]octane). The trends of the resulting transmission probabilities are in agreement with the experimental observations, demonstrating the capability of the approach to distinguish between conductor and insulator type systems, thereby offering a straightforward and cost-effective tool for such classifications via quantum chemical calculations.

Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo

Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties of interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by the available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network for partial charge prediction via active learning based on Dropout Monte Carlo. We show that active learning significantly reduces the required amount of labeled MOFs to reach a target accuracy. The obtained model generalizes well to different distributions of MOFs and Zeolites. In addition, the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen MOFs. This work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges, supporting in-silico material design.

Flavonoid Oxidation Potentials and Antioxidant Activities-Theoretical Models Based on Oxidation Mechanisms and Related Changes in Electronic Structure.

Herein, I will review our efforts to develop a comprehensive and robust model for the estimation of the first oxidation potential, Ep1, and antioxidant activity, AA, of flavonoids that would, besides enabling fast and cheap prediction of Ep1 and AA for a flavonoid of interest, help us explain the relationship between Ep1, AA and electronic structure. The model development went forward with enlarging the set of flavonoids and, that way, we had to learn how to deal with the structural peculiarities of some of the 35 flavonoids from the final calibration set, for which the Ep1 measurements were all made in our laboratory. The developed models were simple quadratic models based either on atomic spin densities or differences in the atomic charges of the species involved in any of the three main oxidation mechanisms. The best model takes into account all three mechanisms of oxidation, single electron transfer-proton transfer (SET-PT), sequential proton loss electron transfer (SPLET) and hydrogen atom transfer (HAT), yielding excellent statistics (R2 = 0.970, S.E. = 0.043).