Formation Thermodynamics, Stability, and Decomposition Pathways of CsPbX3 (X = Cl, Br, I) Photovoltaic Materials

Transport phenomena in photosynthetic systems have attracted a great deal of attention due to their potential role in devising novel photovoltaic materials. In particular, energy transport in light-harvesting complexes is considered quite efficient due to the balance between coherent quantum evolution and decoherence, a phenomenon coined Environment-Assisted Quantum Transport (ENAQT). Although this effect has been extensively studied, its behavior is typically described in terms of the decoherence’s strength, namely weak, moderate or strong. Here, we study the ENAQT in terms of quantum correlations that go beyond entanglement. Using a subsystem of the Fenna–Matthews–Olson complex, we find that discord-like correlations maximize when the subsystem’s transport efficiency increases, while the entanglement between sites vanishes. Our results suggest that quantum discord is a manifestation of the ENAQT and highlight the importance of beyond-entanglement correlations in photosynthetic energy transport processes.

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7-Bromo-[1,2,5]selenadiazolo[3,4-d]pyridazin-4(5H)-one

Molbank ◽

10.3390/m1229 ◽

2021 ◽

Vol 2021 (2) ◽

pp. M1229

Author(s):

Timofey N. Chmovzh ◽

Oleg A. Rakitin

Keyword(s):

Mass Spectrometry ◽

Heterocyclic System ◽

High Resolution Mass Spectrometry ◽

Uv Spectroscopy ◽

Hydrolysis Product ◽

Selenium Dioxide ◽

Biologically Active ◽

Photovoltaic Materials ◽

Organic Photovoltaic Materials ◽

Ir And Uv Spectroscopy

New heterocyclic systems containing 1,2,5-chalcogenadiazoles are of great interest for the creation of organic photovoltaic materials and biologically active compounds. In this communication, 3,6-dibromopyridazine-4,5-diamine was investigated in reaction with selenium dioxide in order to obtain 4,7-dibromo-[1,2,5]selenadiazolo[3,4-d]pyridazine. We found that 7-bromo-[1,2,5]selenadiazolo[3,4-d]pyridazin-4(5H)-one, the first representative of the new heterocyclic system, was isolated as a hydrolysis product of the corresponding 4,7-dibromoderivative. The structure of the newly synthesized compound was established by means of elemental analysis, high-resolution mass spectrometry, 1H, 13C NMR, IR and UV spectroscopy, and mass spectrometry.

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A machine learning platform for the discovery of materials

Journal of Cheminformatics ◽

10.1186/s13321-021-00518-y ◽

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.

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