scholarly journals Studying Materials and Processes with VISION, VirtuES and ICEMAN － Modeling INS Data with DFT Methods

hamon ◽  
2020 ◽  
Vol 30 (3) ◽  
pp. 154-159
Author(s):  
Anibal J Ramirez-Cuesta ◽  
Yongqiang (YQ) Cheng
Keyword(s):  
Dft Methods

Performance of Electronic Structure Methods for the Description of Hückel-Möbius Interconversions in Extended π-Systems

2019 ◽  
Author(s):  
Tatiana Woller ◽  
Ambar Banerjee ◽  
Nitai Sylvetsky ◽  
Xavier Deraet ◽  
Frank De Proft ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Basis Set ◽  
Dft Methods ◽  
Molecular Switching ◽  
Wide Range ◽  
Electronic Structure Methods ◽  
Explicitly Correlated ◽  
Relative Errors ◽  
The Stability ◽  
Basis Set Expansion

<p>Expanded porphyrins provide a versatile route to molecular switching devices due to their ability to shift between several π-conjugation topologies encoding distinct properties. Taking into account its size and huge conformational flexibility, DFT remains the workhorse for modeling such extended macrocycles. Nevertheless, the stability of Hückel and Möbius conformers depends on a complex interplay of different factors, such as hydrogen bonding, p···p stacking, steric effects, ring strain and electron delocalization. As a consequence, the selection of an exchange-correlation functional for describing the energy profile of topological switches is very difficult. For these reasons, we have examined the performance of a variety of wavefunction methods and density functionals for describing the thermochemistry and kinetics of topology interconversions across a wide range of macrocycles. Especially for hexa- and heptaphyrins, the Möbius structures have a pronouncedly stronger degree of static correlation than the Hückel and figure-eight structures, and as a result the relative energies of singly-twisted structures are a challenging test for electronic structure methods. Comparison of limited orbital space full CI calculations with CCSD(T) calculations within the same active spaces shows that post-CCSD(T) correlation contributions to relative energies are very minor. At the same time, relative energies are weakly sensitive to further basis set expansion, as proven by the minor energy differences between MP2/cc-pVDZ and explicitly correlated MP2-F12/cc-pVDZ-F12 calculations. Hence, our CCSD(T) reference values are reasonably well-converged in both 1-particle and n-particle spaces. While conventional MP2 and MP3 yield very poor results, SCS-MP2 and particularly SOS-MP2 and SCS-MP3 agree to better than 1 kcal mol<sup>-1</sup> with the CCSD(T) relative energies. Regarding DFT methods, only M06-2X provides relative errors close to chemical accuracy with a RMSD of 1.2 kcal mol<sup>-1</sup>. While the original DSD-PBEP86 double hybrid performs fairly poorly for these extended p-systems, the errors drop down to 2 kcal mol<sup>-1</sup> for the revised revDSD-PBEP86-NL, again showing that same-spin MP2-like correlation has a detrimental impact on performance like the SOS-MP2 results. </p>

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

A Systematic DFT Study of Two Elementary Steps Involving Hydrogen Peroxide and the Hydroxyl Radical in the Fenton Reaction

2018 ◽  
Author(s):  
Danilo Carmona ◽  
David Contreras ◽  
Oscar A. Douglas-Gallardo ◽  
Stefan Vogt-Geisse ◽  
Pablo Jaque ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Hydroxyl Radical ◽  
Ab Initio ◽  
Fenton Reaction ◽  
Basis Set ◽  
Basis Sets ◽  
Dft Methods ◽  
Elementary Steps ◽  
Oxygen Oxygen ◽  
Complete Basis Set

The Fenton reaction plays a central role in many chemical and biological processes and has various applications as e.g. water remediation. The reaction consists of the iron-catalyzed homolytic cleavage of the oxygen-oxygen bond in the hydrogen peroxide molecule and the reduction of the hydroxyl radical. Here, we study these two elementary steps with high-level ab-initio calculations at the complete basis set limit and address the performance of different DFT methods following a specific classification based on the Jacob´s ladder in combination with various Pople's basis sets. Ab-initio calculations at the complete basis set limit are in agreement to experimental reference data and identified a significant contribution of the electron correlation energy to the bond dissociation energy (BDE) of the oxygen-oxygen bond in hydrogen peroxide and the electron affinity (EA) of the hydroxyl radical. The studied DFT methods were able to reproduce the ab-initio reference values, although no functional was particularly better for both reactions. The inclusion of HF exchange in the DFT functionals lead in most cases to larger deviations, which might be related to the poor description of the two reactions by the HF method. Considering the computational cost, DFT methods provide better BDE and EA values than HF and post--HF methods with an almost MP2 or CCSD level of accuracy. However, no systematic general prediction of the error based on the employed functional could be established and no systematic improvement with increasing the size in the Pople's basis set was found, although for BDE values certain systematic basis set dependence was observed. Moreover, the quality of the hydrogen peroxide, hydroxyl radical and hydroxyl anion structures obtained from these functionals was compared to experimental reference data. In general, bond lengths were well reproduced and the error in the angles were between one and two degrees with some systematic trend with the basis sets. From our results we conclude that DFT methods present a computationally less expensive alternative to describe the two elementary steps of the Fenton reaction. However, choice of approximated functionals and basis sets must be carefully done and the provided benchmark allows a systematic validation of the electronic structure method to be employed

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.

Conformational Stability, FMO, NMR, MEP and NBO Analysis of 2,5-Di-methyl-2,5-bis(methylthio)-1,4-dithiane and Dimethoxy compounds by DFT Approach

2021 ◽  
Vol 17 ◽  
Author(s):  
Nasrin Masnabadi
Keyword(s):  
Computational Methods ◽  
Electrostatic Potential ◽  
Conformational Stability ◽  
Nbo Analysis ◽  
Dft Methods ◽  
Geometrical Properties ◽  
Stereoelectronic Effect ◽  
Nmr Parameters ◽  
Electron Transfers ◽  
Stability Energy

Abstract: Conformational behaviors of 2,5-dimethoxy-2,5-dimethyl-1,4-dithiane (compound 1) and 2,5-dimethyl-2,5-bis (methylthio)-1,4-dithiane (compound 2) investigated by computational methods including B3LYP/6-311+G** and M06-2X/6-311+G** levels of theory and NBO analysis. The stereoelectronic effect of axial, axial (ax, ax) and equatorial, equa-torial (eq, eq) conformations were studied using NBO analysis. Using NBO analysis, the values of the stereoelectronic effects were calculated through the energy of stability associated with the electron transfers of compounds 1 and 2. The results showed that the eq, eq conformations of the studied compounds were more stable than their corresponding ax, ax conformations, and LP2X→σS1-C2 and LP2S→σ*C2-X electron transfers play important roles in the conformational be-havior of the studied compounds. The main purpose of the present work was to study the effects of stereoelectronic inter-actions and steric on the conformational superiority of the di-methoxy (compound 1) and di-thiomethyl compounds (com-pound 2). Thus, the values of resonance stability energy, non-diagonal elements, and orbital populations were investigated. Also, active electrophilic and nucleophilic centers were identified using fronting orbitals analysis obtained by DFT methods. The electrostatic potential maps of the title compounds were investigated at the B3LYP/6-311+G* level of theory. All of the NMR parameters and geometrical properties of both compounds were determined in this study.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

Polar Diels-Alder Reactions Developed in a Protic Ionic Liquid: 3-Nitroindole as Dienophile. Theoretical Studies Using DFT Methods

2012 ◽  
Vol 9 (10) ◽  
pp. 691-695 ◽  
Author(s):  
Claudia D. D. Rosa ◽  
Carla M. Ormachea ◽  
Ana S. Sonzogni ◽  
Maria N. Kneeteman ◽  
Luis R. Domingo ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Diels Alder ◽  
Theoretical Studies ◽  
Dft Methods ◽  
Protic Ionic Liquid

Benchmarking dispersion-corrected DFT methods for the evaluation of materials with anisotropic properties: structural, electronic, dielectric, optical and vibrational analysis of calcite (CaCO3, space group R-3c)

2021 ◽  
Author(s):  
Gianfranco Ulian ◽  
Daniele Moro ◽  
Giovanni Valdre
Keyword(s):  
Physical Properties ◽  
Solid Phase ◽  
Vibrational Analysis ◽  
Simulation Methods ◽  
Dft Methods ◽  
Theoretical Simulation ◽  
Space Group ◽  
Anisotropic Properties

Calcite (CaCO3, space group R-3c) is a solid phase whose well-known highly anisotropic physical properties can be exploited to compare and calibrate various theoretical simulation methods. In this work, to...

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