scholarly journals Computational screen-out strategy for electrically pumped organic laser materials

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi Ou ◽  
Qian Peng ◽  
Zhigang Shuai
Keyword(s):  
Density Functional ◽  
Optical Pumping ◽  
Molecular Design ◽  
Laser Materials ◽  
Structure Information ◽  
Organic Optoelectronics ◽  
Wide Range ◽  
Photophysical Parameters ◽  
Electrically Pumped ◽  
Organic Laser

Abstract Electrically pumped organic lasing is one of the most challenging issues in organic optoelectronics. We present a systematic theoretical investigation to screen out electrical pumping lasing molecules over a wide range of organic materials. With the electronic structure information obtained from time-dependent density functional theory, we calculate multiple photophysical parameters of a set of optical pumping organic laser molecules in our self-developed molecular material property prediction package (MOMAP) to judge whether the electrically pumped lasing conditions can be satisfied, namely, to avoid reabsorption from excitons and/or polarons, and the accumulation of triplet excitons. In addition, a large oscillator strength of S1 and weak intermolecular π–π interaction are preferred. With these criteria, we are able to conclude that BP3T, BSBCz, and CzPVSBF compounds are promising candidates for electrically pumped lasing, and the proposed computational strategy could serve as a general protocol for molecular design of organic lasing materials.

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.

Ab initio calculation on the Optical Properties of AA- Stacked two dimensional Graphene, Silicene, Germanene, and Stanene

2018 ◽  
Vol 1 (1) ◽  
pp. 46-50
Author(s):  
Rita John ◽  
Benita Merlin
Keyword(s):  
Optical Properties ◽  
Band Structure ◽  
Density Functional ◽  
Electronic Band Structure ◽  
Complex Refractive Index ◽  
Single Layer ◽  
Generalized Gradient ◽  
Electronic Band ◽  
Wide Range ◽  
Optical Region

In this study, we have analyzed the electronic band structure and optical properties of AA-stacked bilayer graphene and its 2D analogues and compared the results with single layers. The calculations have been done using Density Functional Theory with Generalized Gradient Approximation as exchange correlation potential as in CASTEP. The study on electronic band structure shows the splitting of valence and conduction bands. A band gap of 0.342eV in graphene and an infinitesimally small gap in other 2D materials are generated. Similar to a single layer, AA-stacked bilayer materials also exhibit excellent optical properties throughout the optical region from infrared to ultraviolet. Optical properties are studied along both parallel (||) and perpendicular ( ) polarization directions. The complex dielectric function (ε) and the complex refractive index (N) are calculated. The calculated values of ε and N enable us to analyze optical absorption, reflectivity, conductivity, and the electron loss function. Inferences from the study of optical properties are presented. In general the optical properties are found to be enhanced compared to its corresponding single layer. The further study brings out greater inferences towards their direct application in the optical industry through a wide range of the optical spectrum.

scholarly journals Chalcogen Bond versus Weak Hydrogen Bond: Changing Contributions in Determining the Crystal Packing of [1,2,5]-Chalcogenadiazole-Fused Tetracyanonaphthoquinodimethanes

2021 ◽  
Vol 03 (02) ◽  
pp. 090-096
Author(s):  
Yusuke Ishigaki ◽  
Kota Asai ◽  
Takuya Shimajiri ◽  
Tomoyuki Akutagawa ◽  
Takanori Fukushima ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Density Functional ◽  
Molecular Design ◽  
Crystal Packing ◽  
Cyano Group ◽  
Density Functional Theory Calculations ◽  
Weak Hydrogen Bond ◽  
Functional Theory ◽  
Chalcogen Bond ◽  
Thiadiazole Ring

The crystal structures of a series of tetracyanonaphthoquinodimethanes fused with a selenadiazole or thiadiazole ring revealed that their molecular packing is determined mainly by two intermolecular interactions: chalcogen bond (ChB) and weak hydrogen bond (WHB). ChB between Se and a cyano group dictates the packing of selenadiazole derivatives, whereas the S-based ChB is much weaker and competes with WHB in thiadiazole analogues. This difference can be explained by different electrostatic potentials as revealed by density functional theory calculations. A proper molecular design that weakens WHB can change the contribution of ChB in determining the crystal packing of thiadiazole derivatives.

scholarly journals Engineering the Interface between Inorganic Nanoparticles and Biological Systems through Ligand Design

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1001
Author(s):  
Rui Huang ◽  
David C. Luther ◽  
Xianzhi Zhang ◽  
Aarohi Gupta ◽  
Samantha A. Tufts ◽  
...  
Keyword(s):  
Organic Synthesis ◽  
Molecular Design ◽  
Biological Systems ◽  
Ligand Design ◽  
Inorganic Nanoparticles ◽  
Organic Ligands ◽  
Biological Applications ◽  
Wide Range ◽  
Insight Into

Nanoparticles (NPs) provide multipurpose platforms for a wide range of biological applications. These applications are enabled through molecular design of surface coverages, modulating NP interactions with biosystems. In this review, we highlight approaches to functionalize nanoparticles with ”small” organic ligands (Mw < 1000), providing insight into how organic synthesis can be used to engineer NPs for nanobiology and nanomedicine.

scholarly journals Tuning the electronic structure of Ag-Pd alloys to enhance performance for alkaline oxygen reduction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
José A. Zamora Zeledón ◽  
Michaela Burke Stevens ◽  
G. T. Kasun Kalhara Gunasooriya ◽  
Alessandro Gallo ◽  
Alan T. Landers ◽  
...  
Keyword(s):  
Metal Binding ◽  
Precious Metal ◽  
Metal Content ◽  
Density Functional ◽  
Binding Energies ◽  
Intrinsic Activity ◽  
Energy Technologies ◽  
Highly Active ◽  
Wide Range ◽  
Precious Metal Content

AbstractAlloying is a powerful tool that can improve the electrocatalytic performance and viability of diverse electrochemical renewable energy technologies. Herein, we enhance the activity of Pd-based electrocatalysts via Ag-Pd alloying while simultaneously lowering precious metal content in a broad-range compositional study focusing on highly comparable Ag-Pd thin films synthesized systematically via electron-beam physical vapor co-deposition. Cyclic voltammetry in 0.1 M KOH shows enhancements across a wide range of alloys; even slight alloying with Ag (e.g. Ag0.1Pd0.9) leads to intrinsic activity enhancements up to 5-fold at 0.9 V vs. RHE compared to pure Pd. Based on density functional theory and x-ray absorption, we hypothesize that these enhancements arise mainly from ligand effects that optimize adsorbate–metal binding energies with enhanced Ag-Pd hybridization. This work shows the versatility of coupled experimental-theoretical methods in designing materials with specific and tunable properties and aids the development of highly active electrocatalysts with decreased precious-metal content.

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 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.

scholarly journals Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

2021 ◽  
Author(s):  
Michael D. T. McDonnell ◽  
Daniel Arnaldo ◽  
Etienne Pelletier ◽  
James A. Grant-Jacob ◽  
Matthew Praeger ◽  
...  
Keyword(s):  
Short Pulse ◽  
Surface Texturing ◽  
Laser Surface Texturing ◽  
Laser Machining ◽  
Laser Materials ◽  
Parameter Optimisation ◽  
Short Pulse Laser ◽  
Alternative Approach ◽  
Highly Nonlinear ◽  
Wide Range

AbstractInteractions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

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