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.

A Machine Learning Platform for the Discovery of Materials

2020 ◽  
Author(s):  
Carl Belle ◽  
Vural Aksakalli ◽  
Salvy Russo
Keyword(s):  
Machine Learning ◽  
Function Theory ◽  
Gw Approximation ◽  
Dft Methods ◽  
Materials Properties ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
New Machine

Abstract For photovoltaic materials, properties such as band gap E g are critical indicators of the material's suitability to perform a desired function. Calculating E g is often performed using Density Function 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 Expresso . 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 of a wide range of materials.

Importance of Unit Cells in Accurate Evaluation of the Characteristics of Graphene

2016 ◽  
Vol 71 (4) ◽  
pp. 315-320 ◽  
Author(s):  
Hassan Sabzyan ◽  
Narges Sadeghpour
Keyword(s):  
Electric Field ◽  
Density Functional ◽  
Unit Cell ◽  
Atomic Charges ◽  
Dft Methods ◽  
Functional Theory ◽  
Accurate Evaluation ◽  
Unit Cell Size ◽  
Unit Cells ◽  
Cell Energy

AbstractEffects of the size of the unit cell on energy, atomic charges, and phonon frequencies of graphene at the Γ point of the Brillouin zone are studied in the absence and presence of an electric field using density functional theory (DFT) methods (LDA and DFT-PBE functionals with Goedecker–Teter–Hutter (GTH) and Troullier–Martins (TM) norm-conserving pseudopotentials). Two types of unit cells containing nC=4–28 carbon atoms are considered. Results show that stability of graphene increases with increasing size of the unit cell. Energy, atomic charges, and phonon frequencies all converge above nC=24 for all functional-pseudopotentials used. Except for the LDA-GTH calculations, application of an electric field of 0.4 and 0.9 V/nm strengths does not change the trends with the size of the unit cell but instead slightly decreases the binding energy of graphene. Results of this study show that the choice of unit cell size and type is critical for calculation of reliable characteristics of graphene.

Abstraction and addition kinetics of C2H radicals with CH4, C2H6, C3H8, C2H4, and C3H6: CVT/SCT/ISPE and hybrid meta-DFT methods

2015 ◽  
Vol 17 (5) ◽  
pp. 3142-3156 ◽  
Author(s):  
Manas Ranjan Dash ◽  
B. Rajakumar
Keyword(s):  
Density Functional ◽  
Transition State Theory ◽  
State Theory ◽  
Rate Coefficients ◽  
Dft Methods ◽  
Functional Theory ◽  
Variational Transition State Theory ◽  
Wide Range ◽  
P Rate ◽  
Kinetics Of

Rate coefficients for the reactions of C2H radicals with methane (k1), ethane (k2), propane (k3), ethylene (k4), and propylene (k5) were computed using canonical variational transition state theory (CVT) coupled with hybrid-meta density functional theory (DFT) over a wide range of temperatures from 150 to 5000 K.

Intrinsic Stacking Interactions of Natural and Artificial Nucleobases

2019 ◽  
Author(s):  
Drew P. Harding ◽  
Laura J. Kingsley ◽  
Glen Spraggon ◽  
Steven Wheeler
Keyword(s):  
Gas Phase ◽  
Electrostatic Interactions ◽  
Density Functional ◽  
Molecular Descriptors ◽  
Stacking Interactions ◽  
Interaction Energies ◽  
Functional Theory ◽  
Binding Partner ◽  
Heavy Atoms ◽  
Wide Range

The intrinsic (gas-phase) stacking energies of natural and artificial nucleobases were explored using density functional theory (DFT) and correlated ab initio methods. Ranking the stacking strength of natural nucleobase dimers revealed a preference in binding partner similar to that seen from experiments, namely G > C > A > T > U. Decomposition of these interaction energies using symmetry-adapted perturbation theory (SAPT) showed that these dispersion dominated interactions are modulated by electrostatics. Artificial nucleobases showed a similar stacking preference for natural nucleobases and were also modulated by electrostatic interactions. A robust predictive multivariate model was developed that quantitively predicts the maximum stacking interaction between natural and a wide range of artificial nucleobases using molecular descriptors based on computed electrostatic potentials (ESPs) and the number of heavy atoms. This model should find utility in designing artificial nucleobase analogs that exhibit stacking interactions comparable to those of natural nucleobases. Further analysis of the descriptors in this model unveil the origin of superior stacking abilities of certain nucleobases, including cytosine and guanine.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

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.

scholarly journals Solution-Processed OLEDs Based on Thermally Activated Delayed Fluorescence Copper(I) Complexes with Intraligand Charge-Transfer Excited State

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1125
Author(s):  
Teng Teng ◽  
Jinfan Xiong ◽  
Gang Cheng ◽  
Changjiang Zhou ◽  
Xialei Lv ◽  
...  
Keyword(s):  
Charge Transfer ◽  
Density Functional ◽  
Delayed Fluorescence ◽  
Quantum Yields ◽  
Functional Theory ◽  
Solution Processed ◽  
Thermally Activated Delayed Fluorescence ◽  
Light Emitting ◽  
Thermally Activated ◽  
Wide Range

A new series of tetrahedral heteroleptic copper(I) complexes exhibiting efficient thermally-activated delayed fluorescence (TADF) in green to orange electromagnetic spectral regions has been developed by using D-A type N^N ligand and P^P ligands. Their structures, electrochemical, photophysical, and electroluminescence properties have been characterized. The complexes exhibit high photoluminescence quantum yields (PLQYs) of up to 0.71 at room temperature in doped film and the lifetimes are in a wide range of 4.3–24.1 μs. Density functional theory (DFT) calculations on the complexes reveal the lowest-lying intraligand charge-transfer excited states that are localized on the N^N ligands. Solution-processed organic light emitting diodes (OLEDs) based on one of the new emitters show a maximum external quantum efficiency (EQE) of 7.96%.

scholarly journals Propiedades electrónicas y estructurales del Ga1-xCrxAs

2018 ◽  
Vol 42 (162) ◽  
pp. 26
Author(s):  
Nasly Y. Martínez Velásquez ◽  
Jairo Arbey Rodríguez Martínez
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Functional Theory ◽  
Half Metallic ◽  
El Sistema ◽  
Quantum Espresso

Mediante el uso de principios basados en la teoría del funcional de la densidad - DFT (Density Functional Theory) se calcularon las propiedades electrónicas y estructurales del compuesto Ga1-xCrxAs. Empleando el método de ondas planas y la aproximación de pseudopotenciales atómicos ultra suaves se resolvieron las ecuaciones de Kohn-Sham. Para la energía de intercambio y correlación se empleó la aproximación de gradiente generalizado, dentro de la parametrización de Perdew-Burke-Ernzerhof (PBE) tal como está implementada en el código computacional Quantum-Espresso. Al dopar GaAs con impurezas de Cr, el sistema exhibe un comportamiento tipo half-metallic. Dicho material puede ser usado en espintrónica. © 2018. Acad. Colomb. Cienc. Ex. Fis. Nat.

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