scholarly journals Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

2021 ◽  
Author(s):  
Wei Li ◽  
Haibo Ma ◽  
Shuhua Li ◽  
Jing Ma
Keyword(s):  
Machine Learning ◽  
Quantum Mechanics ◽  
Electrical Properties ◽  
Material Design ◽  
Data Driven ◽  
Optical And Electrical Properties ◽  
Molecular Materials ◽  
Molecular Material

Low scaling quantum mechanics calculations and machine learning can be employed to efficiently predict the molecular energies, forces, and optical and electrical properties of molecular materials and their aggregates.

Machine learning for high performance organic solar cells: current scenario and future prospects

2021 ◽  
Author(s):  
Asif Mahmood ◽  
Jin-Liang Wang
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Organic Solar Cells ◽  
Organic Solar Cell ◽  
High Performance ◽  
Material Design ◽  
Data Driven ◽  
Future Prospects ◽  
Current Scenario ◽  
Review Current

In this review, current research status about the machine learning use in organic solar cell research is reviewed. We have discussed the challenges in anticipating the data driven material design.

Computational Theories Used in the Study of Quantum Dots

2021 ◽  
pp. 113-144
Keyword(s):  
Quantum Dots ◽  
Quantum Mechanics ◽  
Electrical Properties ◽  
Molecular Mechanics ◽  
Quantum Confinement ◽  
Comprehensive Analysis ◽  
Optical And Electrical Properties ◽  
Computational Approaches ◽  
Commercial Applications

Quantum dots (QDs) are intriguing semiconductors with remarkable quantum confinement, optical and electrical properties which avails for various industrial and commercial applications to revolutionize our world. However, their optimal utilization hinges on the understanding of their properties and computational theories are imperative to explore both existing and new QDs properties. This chapter gives a comprehensive analysis of molecular mechanics and quantum mechanics computational approaches used in the study of the QDs properties.

Optical and Electrical Properties of Organic Conducting Polymers

2014 ◽  
Vol 8 (1) ◽  
pp. 1457-1463
Author(s):  
Salah Abdulla Hasoon
Keyword(s):  
Electrical Properties ◽  
Conjugated Polymer ◽  
Polymeric Materials ◽  
Quantum Mechanical ◽  
Optical And Electrical Properties ◽  
Electrically Conducting ◽  
Temperature Ranges ◽  
Lithium Tetrafluoroborate ◽  
Absorbance Peak ◽  
Polymerization Method

Novel electrically conducting polymeric materials are prepared in this work. Polythiophene (PT) and poly (3-Methelthiophene) (P3MT) films were prepared by electro-polymerization method using cyclic voltammetry in acetonitrile as a solvent and lithium tetrafluoroborate as the electrolyte on a gold electrode. Electrical properties of P3MT have been examined in different environments using UV-Vis absorption spectroscopy and quantum mechanical ab initio calculations, The observed absorption peaks at 314 and 415 nm, were attributed to the n-π* and π-π* transitions, respectively in the conjugated polymer chain, in contrast, the observed absorbance peak at 649 nm, is responsible for electric conduction. The temperature dependence of the conductivity can be fitted to the Arrhenius and the VTF equations in different temperature ranges.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Effect of Gamma Radiation on Structural, Optical and Electrical Properties of nanostructured CdHgTe Thin Films

2018 ◽  
Vol 1 (1) ◽  
pp. 26-31 ◽  
Author(s):  
B Babu ◽  
K Mohanraj ◽  
S Chandrasekar ◽  
N Senthil Kumar ◽  
B Mohanbabu
Keyword(s):  
Thin Films ◽  
Electrical Conductivity ◽  
Electrical Properties ◽  
Gamma Radiation ◽  
Glass Substrate ◽  
Gamma Ray ◽  
Optical And Electrical Properties ◽  
Deposition Technique ◽  
Polycrystalline Structure ◽  
Dc Electrical Conductivity

CdHgTe thin films were grown onto glass substrate via the Chemical bath deposition technique. XRD results indicate that a CdHgTe formed with a cubic polycrystalline structure. The crystallinity of CdHgTe thin films is gradually deteriorate with increasing the gamma irradiation. EDS spectrums confirms the presence of Cd, Hg and Te elements. DC electrical conductivity results depicted the conductivity of CdHgTe increase with increasing a gamma ray dosage

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