scholarly journals On Prediction of a Novel Chiral Material Y2H3O(OH): A Hydroxyhydride Holding Hydridic and Protonic Hydrogens

Materials ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 994
Author(s):  
Aleksandr Pishtshev ◽  
Evgenii Strugovshchikov ◽  
Smagul Karazhanov
Keyword(s):  
Chemical Space ◽  
Charge Ordering ◽  
Optical Systems ◽  
Hydroxyl Groups ◽  
Chemical Transformations ◽  
Structure Property ◽  
Microscopic Mechanism ◽  
Structure Property Relationships ◽  
Dft Modeling ◽  
Oxygen Atoms

Examination of possible pathways of how oxygen atoms can be added to a yttrium oxyhydride system allowed us to predict new derivatives such as hydroxyhydrides possessing the composition M2H3O(OH) (M = Y, Sc, La, and Gd) in which three different anions (H-, O2−, and OH-) share the common chemical space. The crystal data of the solid hydroxyhydrides obtained on the base of DFT modeling correspond to the tetragonal structure that is characterized by the chiral space group P 4 1 . The analysis of bonding situation in M2H3O(OH) showed that the microscopic mechanism governing chemical transformations is caused by the displacements of protons which are induced by interaction with oxygen atoms incorporated into the crystal lattice of the bulk oxyhydride. The oxygen-mediated transformation causes a change in the charge state of some adjacent hydridic sites, thus forming protonic sites associated with hydroxyl groups. The predicted materials demonstrate a specific charge ordering that is associated with the chiral structural organization of the metal cations and the anions because their lattice positions form helical curves spreading along the tetragonal axis. Moreover, the effect of spatial twisting of the H- and H+ sites provides additional linking via strong dihydrogen bonds. The structure–property relationships have been investigated in terms of structural, mechanical, electron, and optical features. It was shown that good polar properties of the materials make them possible prototypes for the design of nonlinear optical systems.

scholarly journals A general approach for retrosynthetic molecular core analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
J. Jesús Naveja ◽  
B. Angélica Pilón-Jiménez ◽  
Jürgen Bajorath ◽  
José L. Medina-Franco
Keyword(s):  
Single Molecule ◽  
Chemical Space ◽  
Significant Proportion ◽  
Bipartite Network ◽  
Data Sets ◽  
Structure Property ◽  
Data Set ◽  
Chemical Structures ◽  
Structure Property Relationships ◽  
Analog Series

Abstract Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

scholarly journals Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

2019 ◽  
Vol 73 (12) ◽  
pp. 1028-1031 ◽  
Author(s):  
Anders S. Christensen ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Chemical Space ◽  
Structure Property ◽  
Operator Formalism ◽  
Response Properties ◽  
Quantum Properties ◽  
Structure Property Relationships ◽  
Quantum Machine Learning ◽  
Common Response ◽  
Data Efficiency

The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.

scholarly journals Simultaneous Optimization of Donor/acceptor Pairs and Device Specifications for Non-Fullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors

2021 ◽  
Author(s):  
Yaping Wen ◽  
Bohan Yan ◽  
Theophile Gaudin ◽  
Jing Ma ◽  
Haibo Ma
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
Chemical Space ◽  
Simultaneous Optimization ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Donor Acceptor ◽  
New Framework

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>

scholarly journals Simultaneous Optimization of Donor/acceptor Pairs and Device Specifications for Non-Fullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors

2021 ◽  
Author(s):  
Yaping Wen ◽  
Bohan Yan ◽  
Theophile Gaudin ◽  
Jing Ma ◽  
Haibo Ma
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
Chemical Space ◽  
Simultaneous Optimization ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Donor Acceptor ◽  
New Framework

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>

scholarly journals Exploring structure-property relationships in magnesium dissolution modulators

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Tim Würger ◽  
Di Mei ◽  
Bahram Vaghefinazari ◽  
David A. Winkler ◽  
Sviatlana V. Lamaka ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Chemical Space ◽  
Light Metal ◽  
Structure Property ◽  
Small Organic Molecules ◽  
Structure Property Relationships ◽  
Structure Property Relationship ◽  
Sketch Maps ◽  
Metal Materials ◽  
Automated Discovery

AbstractSmall organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.

Structure-property relationships of PE/PP sandwiched films

1987 ◽  
Vol 45 ◽  
pp. 454-455
Author(s):  
J. Petermann ◽  
G. Broza ◽  
U. Rieck ◽  
A. Jaballah ◽  
A. Kawaguchi
Keyword(s):  
Mechanical Tests ◽  
Polymer Materials ◽  
Ionic Crystals ◽  
Structure Property ◽  
Polymer Systems ◽  
Structure Property Relationships ◽  
Oriented Films ◽  
Materials Used ◽  
Polymer Laminates ◽  
Heated Glass

Oriented overgrowth of polymer materials onto ionic crystals is well known and recently it was demonstrated that this epitaxial crystallisation can also occur in polymer/polymer systems, under certain conditions. The morphologies and the resulting physical properties of such systems will be presented, especially the influence of epitaxial interfaces on the adhesion of polymer laminates and the mechanical properties of epitaxially crystallized sandwiched layers.Materials used were polyethylene, PE, Lupolen 6021 DX (HDPE) and 1810 D (LDPE) from BASF AG; polypropylene, PP, (PPN) provided by Höchst AG and polybutene-1, PB-1, Vestolen BT from Chemische Werke Hüls. Thin oriented films were prepared according to the method of Petermann and Gohil, by winding up two different polymer films from two separately heated glass-plates simultaneously with the help of a motor driven cylinder. One double layer was used for TEM investigations, while about 1000 sandwiched layers were taken for mechanical tests.

Approaches for TEM imaging of cavitation in toughened nylon

1989 ◽  
Vol 47 ◽  
pp. 352-353
Author(s):  
Barbara A. Wood
Keyword(s):  
Morphological Characteristics ◽  
Structure Property ◽  
Polymer Systems ◽  
Selective Staining ◽  
Structure Property Relationships ◽  
Rubber Toughened ◽  
Shear Yield ◽  
Controversial Topic ◽  
Selection Of ◽  
Diamond Knife

A controversial topic in the study of structure-property relationships of toughened polymer systems is the internal cavitation of toughener particles resulting from damage on impact or tensile deformation.Detailed observations of the influence of morphological characteristics such as particle size distribution on deformation mechanisms such as shear yield and cavitation could provide valuable guidance for selection of processing conditions, but TEM observation of damaged zones presents some experimental difficulties.Previously published TEM images of impact fractured toughened nylon show holes but contrast between matrix and toughener is lacking; other systems investigated have clearly shown cavitated impact modifier particles. In rubber toughened nylon, the physical characteristics of cavitated material differ from undamaged material to the extent that sectioning of heavily damaged regions by cryoultramicrotomy with a diamond knife results in sections of greater than optimum thickness (Figure 1). The detailed morphology is obscured despite selective staining of the rubber phase using the ruthenium trichloride route to ruthenium tetroxide.

Catalyst Halogenation Enables Rapid and Efficient Polymerizations with Visible to Near-Infrared Light

2020 ◽  
Author(s):  
Alex Stafford ◽  
Dowon Ahn ◽  
Emily Raulerson ◽  
Kun-You Chung ◽  
Kaihong Sun ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transient Absorption ◽  
Reaction Times ◽  
Infrared Light ◽  
Structure Property ◽  
Light Emitting ◽  
Structure Property Relationships ◽  
Nir Light ◽  
Light Intensities ◽  
Emission Quenching

Driving rapid polymerizations with visible to near-infrared (NIR) light will enable nascent technologies in the emerging fields of bio- and composite-printing. However, current photopolymerization strategies are limited by long reaction times, high light intensities, and/or large catalyst loadings. Improving efficiency remains elusive without a comprehensive, mechanistic evaluation of photocatalysis to better understand how composition relates to polymerization metrics. With this objective in mind, a series of methine- and aza-bridged boron dipyrromethene (BODIPY) derivatives were synthesized and systematically characterized to elucidate key structure-property relationships that facilitate efficient photopolymerization driven by visible to NIR light. For both BODIPY scaffolds, halogenation was shown as a general method to increase polymerization rate, quantitatively characterized using a custom real-time infrared spectroscopy setup. Furthermore, a combination of steady-state emission quenching experiments, electronic structure calculations, and ultrafast transient absorption revealed that efficient intersystem crossing to the lowest excited triplet state upon halogenation was a key mechanistic step to achieving rapid photopolymerization reactions. Unprecedented polymerization rates were achieved with extremely low light intensities (< 1 mW/cm<sup>2</sup>) and catalyst loadings (< 50 μM), exemplified by reaction completion within 60 seconds of irradiation using green, red, and NIR light-emitting diodes.

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