Adsorption of polyaromatic heterocycles on pyrophyllite surface by means of different theoretical approaches

2011 ◽  
Vol 8 (4) ◽  
pp. 429 ◽  
Author(s):  
C. Ignacio Sainz-Díaz ◽  
Misaela Francisco-Márquez ◽  
Annik Vivier-Bunge
Keyword(s):  
Adsorption Energy ◽  
Density Functional ◽  
Interlayer Space ◽  
External Surface ◽  
Experimental Studies ◽  
Atmospheric Pollutants ◽  
Interatomic Potentials ◽  
Dft Methods ◽  
Theoretical Approaches ◽  
Mechanical Methods

Environmental contextVolatile organic compounds can adsorb to the surfaces of silicates present in atmospheric aerosols, but the mechanisms and interactions are not well understood. We compare theoretical approaches for describing the adsorption of polyaromatic heterocycles to a model phyllosilicate surface. The enthalpy and spectroscopic data for this adsorption provide valuable information for future experimental studies on these atmospheric pollutants. AbstractThe adsorption of thiophene, benzothiophene and dibenzothiophene, as models of polyaromatic heterocycles, on the (001) surface of pyrophyllite, as a model of phyllosilicates, has been investigated by means of empirical interatomic potentials and quantum-mechanical methods based on Hartree–Fock and Density Functional Theory (DFT) approximations. Molecular Dynamic simulations have also been performed for this adsorption, exploring the different configurations that these polyaromatic heterocycles can adopt with respect to the surface. These adsorbates adopt more likely a planar disposition with respect to the phyllosilicate surface. Spectroscopic shifts of the main vibration frequencies upon adsorption of these heterocycles on the phyllosilicate surface have been identified. The adsorption energy calculated with different methods are compared and discussed in terms of adequacy of empirical potentials and DFT methods for describing the weak interactions observed. In addition to considering the (001) surface of pyrophyllite as an external surface of the mineral, the adsorption in the interlayer space was also explored obtaining a d(001) spacing of 12.64 Å. However, the adsorption energy is much lower than the cleavage energy of the interlayer space and it is clear that adsorption is more likely to occur on the external surface than in the interlayer space.

Combined Computational and Experimental Studies of Trans- and Cis-Isomers of Potassium Diaquabis(Oxalato)Chromate (III)

2017 ◽  
Vol 757 ◽  
pp. 103-107
Author(s):  
Songtham Ruangchaithaweesuk ◽  
Juthathip Chorkate ◽  
Thana Maihom ◽  
Potjaman Poolmee ◽  
Piti Treesukol ◽  
...  
Keyword(s):  
Excited States ◽  
Density Functional ◽  
Experimental Studies ◽  
Relative Energy ◽  
Time Dependent ◽  
Dft Methods ◽  
Functional Theory ◽  
Stable Forms ◽  
Tddft Method ◽  
Dependent Density

The trans- and cis-isomers of potassium diaquabis(oxalato)chromate (III) were studied computationally and experimentally. The structures of trans- and cis-configurations of [Cr(H2O)2(C2O4)2]- were optimized by DFT methodology with various functionals namely: B3LYP, CAM-B3LYP, TPSS, PBE, M06-L and ωB97X-D along with the more sophisticated MP2 method. The calculations show that the most stable forms for both isomers are in quartet states. The results from all DFT methods reveal that the cis-isomer is literally more stable than the trans-isomer with the lower average relative energy of 2.1 kcal/mol. These are consistent with the results from MP2 calculation and experimental observation. The absorption wavelengths for the excited states of trans- and cis-structures were calculated by the time-dependent density functional theory (TDDFT) method. For the experiments, the trans- and cis-isomers of potassium diaquabis(oxalato)chromate (III) were synthesized and characterized by UV-Vis spectrophotometry. Both isomers have two maximum absorption wavelengths at 415 and 560 nm.

scholarly journals Application of Density Functional Theory in Coordination Chemistry: A Case Study of Group 13 Monohalide as a Ligand

2021 ◽  
Author(s):  
Thayalaraj Christopher Jeyakumar ◽  
Francisxavier Paularokiadoss
Keyword(s):  
Density Functional Theory ◽  
Coordination Chemistry ◽  
Density Functional ◽  
Experimental Studies ◽  
Theoretical Studies ◽  
Dft Methods ◽  
Functional Theory ◽  
Group 13 ◽  
Metal Bonding

The chemistry of Group 13 Monohalide is of great interest due to its isoelectronic relationship with carbon monoxide and dinitrogen. In recent years, theoretical and experimental studies have been evolved on the group-13 atom-based diatomic molecules as a ligand. The synthetic, characterisation and reactivity of various metal complexes have been well discussed in recent reviews. The nature of the metal bonding of these ligands of various types has been explained in addition by the variety of theoretical studies (using DFT methods) such as FMO and EDA. This chapter has a comprehensive experimental and theoretical study of group 13 monohalides as a ligand in coordination chemistry.

scholarly journals Molecular Interpretation of Pharmaceuticals’ Adsorption on Carbon Nanomaterials: Theory Meets Experiments

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 642 ◽  
Author(s):  
Daniele Veclani ◽  
Marilena Tolazzi ◽  
Andrea Melchior
Keyword(s):  
Density Functional ◽  
Carbon Nanomaterials ◽  
Experimental Studies ◽  
Md Simulations ◽  
Spectroscopic Properties ◽  
Pharmaceutical Drugs ◽  
Mechanical Methods ◽  
Adsorption Processes ◽  
Carbon Based Nanomaterials

The ability of carbon-based nanomaterials (CNM) to interact with a variety of pharmaceutical drugs can be exploited in many applications. In particular, they have been studied both as carriers for in vivo drug delivery and as sorbents for the treatment of water polluted by pharmaceuticals. In recent years, the large number of experimental studies was also assisted by computational work as a tool to provide understanding at molecular level of structural and thermodynamic aspects of adsorption processes. Quantum mechanical methods, especially based on density functional theory (DFT) and classical molecular dynamics (MD) simulations were mainly applied to study adsorption/release of various drugs. This review aims to compare results obtained by theory and experiments, focusing on the adsorption of three classes of compounds: (i) simple organic model molecules; (ii) antimicrobials; (iii) cytostatics. Generally, a good agreement between experimental data (e.g. energies of adsorption, spectroscopic properties, adsorption isotherms, type of interactions, emerged from this review) and theoretical results can be reached, provided that a selection of the correct level of theory is performed. Computational studies are shown to be a valuable tool for investigating such systems and ultimately provide useful insights to guide CNMs materials development and design.

scholarly journals Density Functional Theory Study of Metal and Metal-Oxide Nucleation and Growth on the Anatase TiO2(101) Surface

Computation ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 125
Author(s):  
Leila Kalantari ◽  
Fabien Tran ◽  
Peter Blaha
Keyword(s):  
Density Functional Theory ◽  
Metal Oxide ◽  
Adsorption Energy ◽  
Density Functional ◽  
Magnetic Moments ◽  
Experimental Studies ◽  
Anatase Tio2 ◽  
Functional Theory ◽  
Transition Electron Microscopy ◽  
Production Of Hydrogen

Experimental studies have shown the possible production of hydrogen through photocatalytic water splitting using metal oxide (MOy) nanoparticles attached to an anatase TiO2 surface. In this work, we performed density functional theory (DFT) calculations to provide a detailed description of the stability and geometry of MxOy clusters M = Cu, Ni, Co, Fe and Mn, x = 1–5, and y = 0–5 on the anatase TiO2(101) surface. It is found that unsaturated 2-fold-coordinated O-sites may serve as nucleation centers for the growth of metal clusters. The formation energy of Ni-containing clusters on the anatase surface is larger than for other M clusters. In addition, the Nin adsorption energy increases with cluster size n, which makes the formation of bigger Ni clusters plausible as confirmed by transition electron microscopy images. Another particularity for Ni-containing clusters is that the adsorption energy per atom gets larger when the O-content is reduced, while for other M atoms it remains almost constant or, as for Mn, even decreases. This trend is in line with experimental results. Also provided is a discussion of the oxidation states of M5Oy clusters based on their magnetic moments and Bader charges and their possible reduction with oxygen depletion.

scholarly journals A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning

2019 ◽  
Author(s):  
Peng Gao ◽  
Jun Zhang ◽  
Qian Peng ◽  
Vassiliki-Alexandra Glezakou
Keyword(s):  
Machine Learning ◽  
1H Nmr ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Computational Cost ◽  
Experimental Studies ◽  
Dft Methods ◽  
Nmr Chemical Shifts

Accurate prediction of NMR chemical shifts with affordable computational cost is of great importance for rigorous structural assignments of experimental studies. However, the most popular computational schemes for NMR calculation—based on density functional theory (DFT) and gauge-including atomic orbital (GIAO) methods—still suffer from ambiguities in structural assignments. Using state-of-the-art machine learning (ML) techniques, we have developed a DFT+ML model that is capable of predicting 13C/1H NMR chemical shifts of organic molecules with high accuracy. The input for this generalizable DFT+ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT+ML model was trained with a dataset containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root-mean-square-derivations (RMSDs) for the errors between predicted and experimental 13C/1H chemical shifts are as small as 2.10/0.18 ppm, which is much lower than the typical DFT (5.54/0.25 ppm), or DFT+linear regression (4.77/0.23 ppm) approaches. It also has smaller RMSDs and maximum absolute errors than two previously reported NMR-predicting ML models. We test the robustness of the model on two classes of organic molecules (TIC10 and hyacinthacines), where we unambiguously assigned the correct isomers to the experimental ones. This DFT+ML model is a promising way of predicting NMR chemical shifts and can be easily adapted to calculated shifts for any chemical compound.<br>

scholarly journals Semiconductor Materials for Fabrication of Multi-Color Organic Light-Emitting Diodes (OLEDs)

2021 ◽  
Vol 40 (4) ◽  
pp. 874-882
Author(s):  
Aftab Ahmad ◽  
Nasif Raza Jaffri ◽  
Usama Abrar
Keyword(s):  
Liquid Crystals ◽  
Electronic Properties ◽  
Density Functional ◽  
Light Emitting Diodes ◽  
Organic Light Emitting Diodes ◽  
Dft Methods ◽  
Electronic Density ◽  
Light Emitting ◽  
Organic Light ◽  
Mechanical Methods

This study aims to propose organic materials for the development of light-emitting semiconductor diodes for colored displays. Studies show that these materials are capable of creating a variety of different colors rather than white light. But of Organic Light-Emitting Diodes (OLEDs) are only used as a source of white back-light for OLED displays and liquid crystals are used for color generation. This work suggests that OLEDs can be used to make color displays on their own without the help of Liquid Crystals (LCs). Recently, organic devices are widely under discussion as are comparatively cheap, can be processed economically and effortlessly at ambient temperature besides their effortless handling. The calculation of the electronic properties of molecular species was achieved by the use of ab-initio quantum mechanical methods, i.e., Density Functional Theory (DFT). DFT methods are suited to calculate the electronics properties of the organic molecules, enabling the determination of band gaps and quantum efficiencies. DFT views electron stochastic nature and thus calculates the material’s solid-state properties. DFT calculations on isolated molecules were carried by the Gaussian software package to predict electronic properties. Pentacene is used as test molecule in this work. B3LYP functional use Kohn-Sham orbitals to predict the band energy values said material rather than LDA functional that depends on the value of electronic density at each point on the space. The substitution process was used to make changes in bandgaps; which affect shades of light emitted by OLEDs.

scholarly journals A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning

2019 ◽  
Author(s):  
Peng Gao ◽  
Jun Zhang ◽  
Qian Peng ◽  
Vassiliki-Alexandra Glezakou
Keyword(s):  
Machine Learning ◽  
1H Nmr ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Computational Cost ◽  
Experimental Studies ◽  
Dft Methods ◽  
Nmr Chemical Shifts

Accurate prediction of NMR chemical shifts with affordable computational cost is of great importance for rigorous structural assignments of experimental studies. However, the most popular computational schemes for NMR calculation—based on density functional theory (DFT) and gauge-including atomic orbital (GIAO) methods—still suffer from ambiguities in structural assignments. Using state-of-the-art machine learning (ML) techniques, we have developed a DFT+ML model that is capable of predicting 13C/1H NMR chemical shifts of organic molecules with high accuracy. The input for this generalizable DFT+ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT+ML model was trained with a dataset containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root-mean-square-derivations (RMSDs) for the errors between predicted and experimental 13C/1H chemical shifts are as small as 2.10/0.18 ppm, which is much lower than the typical DFT (5.54/0.25 ppm), or DFT+linear regression (4.77/0.23 ppm) approaches. It also has smaller RMSDs and maximum absolute errors than two previously reported NMR-predicting ML models. We test the robustness of the model on two classes of organic molecules (TIC10 and hyacinthacines), where we unambiguously assigned the correct isomers to the experimental ones. This DFT+ML model is a promising way of predicting NMR chemical shifts and can be easily adapted to calculated shifts for any chemical compound.<br>

scholarly journals The Lowest-Energy Isomer of C2Si2H4 Is a Bridged Ring: Reinterpretation of the Spectroscopic Data Based on DFT and Coupled-Cluster Calculations

Inorganics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 51
Author(s):  
Jesse J. Lutz ◽  
Larry W. Burggraf
Keyword(s):  
Density Functional ◽  
Experimental Studies ◽  
Basis Set ◽  
Many Body ◽  
Dft Methods ◽  
Functional Theory ◽  
Energy Profiles ◽  
Cluster Calculations ◽  
Complete Basis Set ◽  
Many Body Perturbation Theory

The lowest-energy isomer of C 2 Si 2 H 4 is determined by high-accuracy ab initio calculations to be the bridged four-membered ring 1,2-didehydro-1,3-disilabicyclo[1.1.0]butane (1), contrary to prior theoretical and experimental studies favoring the three-member ring silylsilacyclopropenylidene (2). These and eight other low-lying minima on the potential energy surface are characterized and ordered by energy using the CCSD(T) method with complete basis set extrapolation, and the resulting benchmark-quality set of relative isomer energies is used to evaluate the performance of several comparatively inexpensive approaches based on many-body perturbation theory and density functional theory (DFT). Double-hybrid DFT methods are found to provide an exceptional balance of accuracy and efficiency for energy-ordering isomers. Free energy profiles are developed to reason the relatively large abundance of isomer 2 observed in previous measurements. Infrared spectra and photolysis reaction mechanisms are modeled for isomers 1 and 2, providing additional insight about previously reported spectra and photoisomerization channels.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

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