Computational Dynamics Research Articles

Fitting of Coupled Potential Energy Surfaces via Discovery of Companion Matrices by Machine Intelligence.

Fitting coupled potential energy surfaces is a critical step in simulating electronically nonadiabatic chemical reactions and energy transfer processes. Analytic representation of coupled potential energy surfaces enables one to perform detailed dynamics calculations. Traditionally, fitting is performed in a diabatic representation to avoid fitting the cuspidal ridges of coupled adiabatic potential energy surfaces at conical intersection seams. In this work, we provide an alternative approach by carrying out fitting in the adiabatic representation using a modified version of the Frobenius companion matrices, whose usage was first proposed by Opalka and Domcke. Their work involved minimizing the errors in fits of the characteristic polynomial coefficients (CPCs) and diagonalizing the resulting companion matrix, whose eigenvalues are adiabatic potential energies. We show, however, that this may lead to complex eigenvalues and spurious discontinuities. To alleviate this problem, we provide a new procedure for the automatic discovery of CPCs and the diagonalization of a companion matrix by using a special neural network architecture. The method effectively allows analytic representation of global coupled adiabatic potential energy surfaces and their gradients with only adiabatic energy input and without experience-based selection of a diabatization scheme. We demonstrate that the new procedure, called the companion matrix neural network (CMNN), is successful by showing applications to LiH, H3, phenol, and thiophenol.

Thermal performance of MgCl2–NaCl–KCl eutectic salt for the next generation concentrated solar power and correlation between structure and thermophysical properties: Insights from atomic and electronic levels

MgCl2–NaCl–KCl (MgNaK) eutectic salt has emerged as a highly promising candidate for concentrated solar power (CSP), with extensive research conducted in recent years. However, the thermal property data for the MgNaK eutectic salt are still very limited. Here, we present a novel and accurate many-body potential trained by machine learning (ML) for MgNaK eutectic salt (with mole fractions of 45.4 %, 33 %, and 21.6 % respectively), developed using energies and forces extracted from first-principles molecular dynamics calculations (FPMD). The performance of the potential is well tested. We conduct a detailed investigation on the temperature-dependent structural and corresponding thermal properties, analyzing them from both the atomic and electronic perspectives. The introduction of Na+ or K+ ions disrupts the net structure formed by corner-joint and edge-joint MgClx units, thereby affecting the transport properties. The calculated density (ρ) and constant pressure specific heat capacity (CP) exhibit agreement with experimental data within a margin of 2 %. We observe and discuss the typical λ of negative linear temperature correlation, which is similar to other molten alkali chloride salts. Finally, based on the simulated and experimental values, we provide reliable recommendations for the λ and viscosities (η) of MgNaK eutectic salt across its entire operating temperature range.

A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.

Erosion-corrosion failure of overhead air cooler in atmospheric tower

ABSTRACT During the hydrogenation process in a petrochemical plant, the air cooler serves as a crucial heat exchanger in atmospheric towers. However, the frequent corrosion and leaks of air coolers significantly impact the safety of the process. Firstly, the Aspen software is utilized to simulate the operation of an air cooler. Then, Computational Fluid Dynamics (CFD) calculations are performed to analyze the internal flow in the air cooler. Finally, four inlet optimization strategies are proposed. It is indicated that the condensation of the oil phase results in the formation of liquid-phase oil droplets that cause erosion-corrosion. The absence of liquid-phase water condensation implies that there is minimal dew point corrosion in the tube bundle. The turbulence kinetic energy near the entrance exhibits significant variation, making erosion-corrosion more likely to occur near the entrance of the cooling tube bundle. Furthermore, vortexes form near the entrances of the first and second rows, exacerbating the corrosion in the tube bundle. By optimizing the air cooler, numerical simulations showed that the shear force of the vortex on the tube wall is reduced by nearly 50%. The internal erosion and scour effect of the improved air cooler is reduced.

Investigation on special reactivity of sulfite as an electron acceptor for the attenuated Fenton reaction and the abatement mechanism

Sulfur(IV) as a ubiquitous substance in the atmosphere and water bodies can be deliberately involved in different redox reactions based on the known ability to donate electrons, but the inhibition effect on the advanced oxidation processes (AOPs) especially the Fenton reaction, which played a vital role in aerosol aging and pollution treatment, still stayed fuzzy. In this work, it was first found that sulfite had an unexpected reactivity to accept electrons from Fe(II) monitored by the chemiluminescence (CL) method to reduce Fe(II) seriously. Dynamics calculation, UV–vis absorption, HPLC, and CL analysis indicated that sulfite anions can be transferred into SO2·- or dimer S2O42− through reacting with Fe(II). The direct consumption of Fe(II) in Fenton reaction only happened by sulfite among other inorganic anions during degrading different organic pollution, and the produced S2O42− showed an overpowering inhibitory ability on hydroxyl radical especially at pH 5.3, both resulting in a strong inhibition effect on the Fenton reaction. In contrast at pH 3.1, the fast depletion of sulfite by H2O2 and decomposition of S2O42− fell off the inhibition effect. Furthermore, abatement experiments showed that the effect of sulfite could be circumvented by using a non-iron-based oxidation system with other metal ions, additionally clarifying the reaction of Fe(II)-sulfite played the primary role in the inhibition. Significantly, this inhibition effect of sulfite on the Fenton reaction was demonstrated to take place both in the natural wastewater and degrading two acids from biomass burning in the atmosphere, identifying the attenuated Fenton reaction in the presence of sulfur(IV) in the environment

The Effect of the Acceleration Voltage on the Quality of Structure Determination by 3D-Electron Diffraction

Nowadays, 3D Electron Diffraction (3DED) is widely used for the structure determination of sub-micron-sized particles. In this work, we investigate the influence of the acceleration voltage on the quality of 3DED datasets acquired on BaTiO3 nanoparticles. Datasets were acquired using a wide range of beam energies, from common, high acceleration voltages (300 kV and 200 kV) to medium (120 kV and 80 kV) and low acceleration voltages (60 kV and 30 kV). In the integration process, Rint increases as the beam energy reduces, which is mainly due to the increased dynamical scattering. Nevertheless, the structure was solved successfully in all cases. The structure refinement was comparable for all beam energies with small deficiencies such as negative atomic displacements for the heaviest atom in the structure, barium. Including extinction correction in the refinement noticeably improved the model for low acceleration voltages, probably due to higher beam absorption in these cases. Dynamical refinement, however, shows superior results for higher acceleration voltages, since the dynamical refinement calculations currently discard inelastic scattering effects.

A Numerical Method Coupling the T-A Formulation for Dynamic Resistance Computation in HTS Coated Conductors

A Numerical Method Coupling the <i>T-A</i> Formulation for Dynamic Resistance Computation in HTS Coated Conductors

Hydrogen adsorption, desorption, sensor and storage properties of Cu doped / decorated boron nitride nanocone materials: A density functional theory and molecular dynamic study

The hydrogen storage and sensor properties of Cu-modified boron nitride nanocone (BNNC) structure were comprehensively investigated using the density functional theory method (DFT). By replacing nitrogen atoms with copper atoms on the BNNC structure, the hydrogen molecule's adsorption enthalpy and Gibbs free energy values were calculated as −48.6 and −16.9 kJ/mol, respectively. 4 Cu-doped BNNC structure achieved a notable hydrogen storage capacity of 5.38 wt%. The molecular dynamics calculations demonstrate that the structure modified with four Cu atoms maintains its dynamic stability. Furthermore, this study reveals that the desorption temperatures tend to rise with an increase in the number of adsorbed hydrogen molecules, and the average desorption temperature under 1 atm pressure is determined to be 332 K. The changes of work function values after hydrogen adsorption point out that the Cu-modified BNNC has Φ-sensor feature. Overall, Cu-modified BNNC structures show promising potential as hydrogen storage materials in ambient conditions.

Conserved gatekeeper methionine regulates the binding and access of kinase inhibitors to ATP sites of MAP2K1, 4, and 7: Clues for developing selective inhibitors

Mitogen-activated protein kinase kinases (MAP2Ks) 1, 4, and 7 are potential targets for treating various diseases. Here, we solved the crystal structures of MAP2K1 and MAP2K4 complexed with covalent inhibitor 5Z-7-oxozeaenol (5Z7O). The elucidated structures showed that 5Z7O was non-covalently bound to the ATP binding site of MAP2K4, while it covalently attached to cysteine at the DFG-1 position of the deep ATP site of MAP2K1. In contrast, we previously showed that 5Z7O covalently binds to MAP2K7 via another cysteine on the solvent-accessible edge of the ATP site. Structural analyses and molecular dynamics calculations indicated that the configuration and mobility of conserved gatekeeper methionine located at the central ATP site regulated the binding and access of 5Z7O to the ATP site of MAP2Ks. These structural features provide clues for developing highly potent and selective inhibitors against MAP2Ks.Abbreviations: ATP, adenosine triphosphate; FDA, Food and Drug Administration; MAP2Ks, mitogen-activated protein kinase kinases; MD, molecular dynamics; NSCLC, non-small cell lung cancer; 5Z7O, 5Z-7-oxozeaenol; PDB, protein data bank; RMSD, root-mean-square deviation

Ultra-Dilute SnCl4-Catalyzed Conversion of Concentrated Glucose to 5-Hydroxymethylfurfural in Aqueous Deep Eutectic Solvent.

5-Hydroxymethylfurfural(HMF) is a versatile chemical synthesized from glucose dehydration catalyzed by metal chloride (MClx) in deep eutectic solvents (DESs). However, the low glucose concentration and high catalyst dosage hinder large-scale HMF production. Herein, we report an aqueous DES of tetraethylammonium bromide(TEAB)-glucose for converting concentrated glucose (40 wt%, relative to TEAB) using ultra-dilute SnCl4 (0.25 mol%), achieving a 62% yield of HMF. Ultra-dilute MClx-catalyzed selective conversion of glucose is feasible only when combining SnCl4 with Br-based DES, which is elucidated by density functional theory and molecular dynamic calculations. Using SnCl4 is essential due to its higher glucose isomerization activity than AlCl3 and CrCl3, which can be attributed to its low-barrier coordination with glucose and its barrier-free separation from fructose. Halide anions in DESs strongly interact with glucose, hindering the MClx-glucose coordination and thereby reducing MClx's activity for glucose isomerization. Consequently, Br-based DESs facilitate higher activity of MClx than Cl-based DESs, due to the weaker interaction between halide anion and glucose. In addition, we elucidated the side reactions including condensation, polymerization, and isomerization, and proposed a reaction network. Our findings clarify the differential activity of MClx and the impact of halide anions in DESs on MClx's activity.

Incorporated surfaces and confined spaces of Na2HPO4·12H2O–Layered vermiculite nanosheets for phase change materials with enhanced thermal properties

The phase change process of hydrated salts with a large latent heat and non-flammable characteristics is a promising route for achieving energy-efficient devices. To improve the reliability of the inorganic phase change material (PCM) system, excellent heat storage capacity, low supercooling degree, and good stability of hydrated salts are required. In this study, novel Na2HPO4·12H2O (DHPD)–vermiculite nanosheet (VMTNS) composite PCMs (CPCMs) possessing both surfaces and confined spaces were theoretically designed and synthesized. Incorporating the environmentally friendly VMTNSs, a new type of nano-reinforced fillers with multiple active sites, into DHPD hydrated salt results in excellent crystallization, great diffusion ability (33.277 × 10−11 m2/s), a low supercooling degree (0.8 °C), a high latent heat of melting (260.7 J/g), and great thermal stability of CPCMs. The DHPD arrangement both in the confined spaces and on the surfaces of the VMTNSs was simulated via molecular dynamics calculations. It was found that the adsorption energy of DHPD in the confined spaces of the VMTNSs and on their surfaces is –5.991 and –5.543 eV, respectively. This study provides an innovative method to construct novel CPCMs for efficient energy storage.

Polyampholytes with Various Charge Distributions: Conformation States via Computer Simulation.

By means of molecular dynamics computer simulation, the conformational space of polyampholyte macromolecules with various distributions of the charged groups along the chain is studied. A coarse-grained model where each monomer unit of the chain is presented as a non-charged group in the backbone of the macromolecule connected with a charged side pendant is considered. A limiting case of fully charged chains in the isoelectric point is investigated. The oppositely charged monomer units are distributed in various patterns: regular alternating, multiblock, or random sequences. It is found that the chains with random unit distribution adopt much more compacted conformations than the chains with regular distributions with comparable block lengths. Calculating the chain size and its fluctuation along with the spatial density distribution, coil, and globular conformations are distinguished and arranged on the diagrams in terms of chain length, block length, and Bjerrum length.

FORECASTING CHANGE IN THE LEVEL OF FOREST COVERAGE USING THE GLOBAL FOREST WATCH SERVICE AND THE PROGRAMMING AND DATA ANALYSIS LANGUAGE R

The problem of calculating the level of forest cover is considered, including forecasting changes in forest cover in individual forestry. It is stated that the authors previously developed software for calculating forest cover and processing information about forest plantations using the example of the village of Spivakivka in the Izyum district of the Kharkiv region. A comparison of forest cover over a number of years was also made using the Global Forest Watch resource. From this resource, images of Prydonetsk Forestry were taken with conventional designations: areas where new forest plantations are being planted are shown in blue, and areas where cutting is taking place are shown in pink. It is proposed to divide each of the uploaded images of the selected forestry into squares, and then analyze the data for each square. The pink color saturation was calculated and stored in the table. It is noted that forecasting the change in forest stands on the selected site, that is, the change in the percentage of felling, can be done in different ways. First, use regression analysis - apply the regression equation separately to the values of each square, as well as to the entire forestry. Secondly, to form a list of input factors containing indicators on the selected plot in the two previous years and the same indicators on neighboring plots. Thus, the number of factors will be equal to 27: 26 input and 1 output (values on the studied square). Such a forecasting problem can be solved either by the method of multivariate linear regression or by the method of artificial neural networks. The R programming and data analysis language was used to perform calculations using both methods. A script was created that performs calculations by constructing regression lines and an artificial neural network, and also allows determining the best architecture of a neural network and a more effective method of its training for a certain data set. The calculation of the felling dynamics in the entire forestry (the forecast for the last year provides an error of 1 %) and the calculation of the felling dynamics in the selected square (the forecast for the last year provides an error of 3.5 %) are given. After many runs of the script, it was found that the best result is provided by a perceptron with two hidden layers and two neurons in each layer. The results of the calculations indicate a high correlation of the data for determining the percentage of forest that will be cut down in a certain square. Application of this perceptron for forecasting for the last year showed an error of 3 %.

Vibrational dynamics dictate heat transport in metal-organic frameworks with gas adsorbates

Understanding the thermal conductivity (κ) of metal-organic frameworks (MOFs) with adsorbates is critical for tailoring their heat dissipation in practical applications. However, a detailed description of how adsorbates influence the heat transport of MOFs is still lacking. In this work, we employ molecular dynamics simulations combined with lattice dynamics calculations to investigate the effect of gas adsorbates on κ of representative crystalline and glassy zeolitic imidazolate frameworks (ZIFs). We identify that the diffusive adsorbate motions in the crystalline ZIFs with large pores can enhance lattice anharmonicity, serving as a source of phonon scattering and significantly suppressing κ. Conversely, the adsorbates with restricted dynamics in limited space (i.e., in the dense ZIF crystals or glasses) behave more like a heat transfer medium and facilitate heat transport. Besides pore size, gas dynamics are also proved to vary upon gas-lattice interaction strength, and dictate if gas adsorbates serve as a heat conduction suppressor or promoter. We anticipate that this governing role of gas dynamics is applicable to a diverse range of MOF-adsorbate systems. As such, our work offers guidance for the practical thermal management of MOF porous materials to ensure their desired storage or separation performance.

Enhanced Selectivity in Microdroplet-Mediated Enzyme Catalysis.

Natural enzymes with enhanced catalytic activity and selectivity have long been studied by tuning the microenvironment around the active site, but how to modulate the active-site electric field in a simple fashion remains challenging. Here, we demonstrate that microdroplets as a simple yet versatile reactor can enhance the electric field at the active site of an enzyme. By using horseradish peroxidase as a model, improved selectivity in microdroplet-mediated enzyme catalysis can be obtained. Quantum mechanical/molecular dynamics calculations and vibrational Stark spectroscopy reveal that the electric field at the microdroplet interface can influence the electrostatic preorganization and orientation of the enzyme to enhance its internal electric field. As a result, the free energies of the substrate and heme can be tuned by the internal electric field, thereby changing its catalytic reaction pathway for a classical substrate, 3,3',5,5'-tetramethylbenzidine, and enabling selective C-N additions for specific substrates. This finding provides a green, simple, and effective way to modulate enzyme-catalyzed reactions and holds promise for a broad spectrum of biosensing and biosynthesis applications.

Mussel-Bioinspired Lignin Adhesive for Wearable Bioelectrodes.

As a natural "binder," lignin fixes cellulose in plants to foster growth and longevity. However, isolated lignin has a poor binding ability, which limits its biomedical applications. In this study, inspired by mussel adhesive proteins, acidic/basic amino acids (AAs) are introduced in alkali lignin (AL) to form ionic-π/spatial correlation interactions, followed by demethylation to create catechol residues for enhanced adhesion activity. Atomic force microscopy reveals that catechol residues are the primary adhesion structures, with basic AAs exhibiting superior synergistic effects compared to acidic AAs. Demethylated lysine-grafted AL exhibits the strongest adhesion force toward skin tissue. Molecular dynamic simulation and density functional theory calculations indicate that adhesion against skin tissue mainly results from hydrogen bonds and cation-π interactions, with the adhesion mechanism being based on the Gibbs free energy of the Schiff base reaction. In summary, a biomimetic electrode based on lignin inspired by mussel adhesive proteins is prepared; the presented method offers a straightforward strategy for the development of biomimetic adhesives. Furthermore, this mussel-inspired adhesive can be used as a wearable bioelectrode in biomedical applications.

Ab Initio Molecular Dynamics Insight to Structural Phase Transition and Thermal Decomposition of InN

Extensive ab initio density functional theory molecular dynamics calculations were used to evaluate stability conditions for relevant phases of InN. In particular, the p-T conditions of the thermal decomposition of InN and pressure-induced wurtzite–rocksalt solid–solid phase transition were established. The comparison of the simulation results with the available experimental data allowed for a critical evaluation of the capabilities and limitations of the proposed simulation method. It is shown that ab initio molecular dynamics can be used as an efficient tool for simulations of phase transformations of InN, including solid–solid structural transition and thermal decomposition with formation of N2 molecules. It is of high interest, because InN is an important component of epitaxial quantum structures, but it has not been obtained as a bulk single crystal. This makes it difficult to determine its basic physical properties to develop new applications.

Sage (Salvia macrochlamys): LC-HRMS for phytochemical analysis, cytotoxicity, enzyme inhibition, antioxidant activity, molecular docking and molecular dynamics simulations

This study primarily aims to evaluate the phenolic content and biological activity of Salvia macrochlamys both in vitro and in silico. The antioxidant activities of Salvia macrochlamys were assessed using various methods. Additionally, cytotoxic effects and the α-amylase, α-glucosidase, and acetylcholinesterase (AChE) enzyme inhibitory abilities of ethanolic extract of the aerial parts of Salvia macrochlamys (EESM) were evaluated. Then, the molecular docking interactions and molecular dynamic calculations of EESM against α-amylase, α-glycosidase, and AChE enzymes were carried out. High antioxidant activity was determined in both ethanol and water extracts. The IC50 values of EESM against AChE (EC 3.1.1.7), α-glycosidase (E.C.3.2.1.20), and α-amylase (E.C.3.2.1.1) were determined as 1.622 μg/mL, 0.530 μg/mL, and 1.320 μg/mL, respectively. High enzyme-inhibiting properties of EESM were determined in also in silico. Rosmarinic acid was identified as one of the most abundant phenolic compounds in both EESM and water extract of the aerial parts of Salvia macrochlamys (WESM) (3919.65 mg/kg and 10520.77 mg/kg, respectively). Their high level of flavonoids and phenolics, as well as the potent antioxidant and enzyme-inhibitory properties displayed by extracts, may contribute to broadening the use of Salvia macrochlamys in both traditional and modern therapeutic applications.

Construction of diabatic potential energy surfaces for the SiH2+ system and dynamics studies of the Si+(2P1/2, 3/2) + H2 reaction.

The construction of diabatic potential energy surfaces (PESs) for the SiH2+ system, related to the ground (12A') and excited states (22A'), has been successfully achieved. This was accomplished by utilizing high-level abinitio energy points, employing a neural network fitting method in conjunction with a specifically designed function. The newly constructed diabatic PESs are carefully examined for dynamics calculations of the Si+(2P1/2, 3/2) + H2 reaction. Through time-dependent quantum wave packet calculations, the reaction probabilities, integral cross sections (ICSs), and differential cross sections (DCSs) of the Si+(2P1/2, 3/2) + H2 reaction were reported. The dynamics results indicate that the total ICS is in excellent agreement with experimental data within the collision energy range studied. The results also indicate that the SiH+ ion is hardly formed via the Si+(2P3/2) + H2 reaction. The results from the DCSs suggest that the "complex-forming" reaction mechanism predominates in the low collision energy region. Conversely, the forward abstraction reaction mechanism is dominant in the high collision energy region.

Multifunctional Power Generators beyond Moisture Limitation

AbstractThe moisture‐enabled electricity generators (MEGs) represent an appealing clean power strategy within the realm of hydrovoltaic technology. However, their stable operation under all‐weather conditions is still challengeable due to the effect of ambient humidity. Herein, a self‐water‐storage MEGs with multifunctionality was developed. Gel textiles are employed as the proton‐release layer, while graphite fibers loaded with tin‐bismuth alloy liquid metal serve as water‐storing electrode. The gel textiles absorb water permeating down from the electrodes, releasing protons, which then react with the active electrode to enhance output performance. The experiment demonstrates that a device can generate a voltage of 0.55 V and a current of 7.08 μA after water storage, with a maximum power density of 1.14 μW·cm‐². The device's performance output can remain stable within a wide temperature range. The experiment confirms that the electrical output of the device originates from ionic diffusion, while density functional theory (DFT) and molecular dynamics (MD) calculations reveal the importance of the strong hydrolysis ability of the polymer and the high adsorption energy of the liquid metal for H3O+ on the output performance of the device. Furthermore, the integrated device has demonstrated effective applications in driving microelectronic devices, charging capacitors and, self‐powered health monitoring.