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13 results for “Molecular descriptors”
calculated 3D molecular descriptors for the dopant-free HTMs without PSCs classification
<p><strong><span>The calculated 3D molecular descriptors for the dopant-free HTMs (Without Classification of PSCs) along with their photovoltaic properties.</span></strong></p> <p><strong><span><span>The m</span><span>olecular descriptors for all molecules were calculated using the academically free PADEL online tool (http://www.scbdd.com/padel_desc/index/).</span></span></strong></p>
Combination of 1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs without considering the variability introduced by fabrication methods on PSC efficiency
<p>1D, 2D, and 3D molecular descriptors for all the dopant-free HTMs <strong><span>without considering the variability introduced by fabrication methods on PSC efficiency </span></strong>along with their photovoltaic properties (PCE, JSC, and VOC)</p>
Calculated 1D, and 2D molecular descriptors for the dopant-free HTMs without PSCs classification
<p><strong><span>The calculated 1D, and 2D descriptors for the dopant-free HTMs (Without Classification of PSCs) as well as their photovoltaic properties. </span></strong></p> <p><strong><span>All 1D, and 2D descriptors were calculated using the </span></strong><strong><span><span>academically free PADEL online tool (http://www.scbdd.com/padel_desc/index/)</span></span></strong></p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb. The total disk space for pore Tet6Di12 alone is 1.5Gb.</p> <p>The base structure of all pore topologies are constructed using a metal node of radius ~5 (represented by a Zirconium atom) and a benzene linker, while the functional group is represented by a Nitrogen atom.</p>
Data from: Machine learning-based discovery of molecular descriptors that control polymer gas permeation
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Descriptores de la actividad de revistas académicas en el área de Biología Molecular atendiendo al "SCImago Journal Rank" (SJR)
<p>Para cada una de las revistas del ámbito de Bioquímica, Genética y Biología Molecular y dentro de éste en el área de Biología Molecular en el marco temporal del año 2022 (último año disponible en la actualidad) y el ámbito de formato del "journal" se dispone de datos identificadores (Número, Nombre e ISSN), clasificadores (Publisher y País) y de calificación de la revista (SJR, IndiceH, Art2022, Art3años, Ref2022, Citas3años, CitableDocs3años, CitasPerDocs2años y RefPerDoc2022).</p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb.</p> <p>The total disk space for pore Tet6Di12 alone is 1.5Gb.</p>
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties using Machine Learning
<p>This dataset accompanies a study that investigates the use of machine learning (ML) approaches for predicting seven different physical properties of 140 binary copolymers. Two computational methods were employed: a random forest (RF) model based on molecular descriptors and a Graph Neural Network (GNN) using 2D polymer graphs. These methods were applied in both single- and multi-task settings to explore the strengths of each approach in capturing various polymer properties.</p> <p>The dataset includes two files:</p> <ol> <li> <p><strong>Dataset.xlsx</strong>: Contains the following information for each of the 140 copolymers:</p> <ul> <li>Polymer names</li> <li>SMILES notation of the monomers</li> <li>Fraction of monomers in each copolymer</li> <li>Simulated values (calculated using molecular dynamics simulation) and experimental values of various physical properties, including density, specific heat capacity at constant pressure ad volume ,radius of gyration, linear expansion coefficient, volume expansion coefficient, and bulk modulus .</li> </ul> </li> <li> <p><strong>Descriptors.xlsx</strong>: Provides the molecular descriptors calculated using PaDEL-Descriptor software, which were used as input for the RF model to predict polymer properties.</p> </li> </ol> <p>This work provides insight into the comparative strengths of descriptor- and graph-based representations in machine learning models for predicting material properties. It also highlights the importance of selecting appropriate molecular representations based on the nature of the properties being predicted.</p>
Detailed information of 4179 calculated molecular descriptors from 22 different classes.
<p><strong>Detailed information of 4179 calculated molecular descriptors from 22 different classes.</strong></p>
Data from: Combining and Comparing Morphometric Shape Descriptors with a Molecular Phylogeny: The Case of Fruit Type Evolution in Bornean Lithocarpus (Fagaceae)
Fruit type in the genus Lithocarpus (Fagaceae) includes both classic oak acorns and novel modifications. Bornean taxa with modified fruits can be separated into two sections (Synaedrys and Lithocarpus) based on subtle shape differences. Following strict criteria for homology and representation, this variation in shape can be captured and the sections distinguished using elliptic Fourier or eigenshape analysis. Phenograms of fruit shape, constructed using restricted maximum likelihood techniques and these morphometric descriptors, were incorporated into combined and comparative analyses with molecular sequence data from the internal transcribed spacer (ITS) region of the nuclear rDNA using branch-weighted matrix representation. The combined analysis strongly suggested independent derivation of the novel fruit type in the two sections from different acorn-like ancestors, while the comparative analysis indicated frequent decoupling between the molecular and morphological change as inferred at well-supported nodes. The acorn fruit type has undergone little modification between ingroup and outgroup, despite large molecular distance. Greater morphological than molecular change was inferred at critical transitions between acorn and novel fruit types, particularly for section Lithocarpus. The combination of these two different types of data improved our understanding of the macroevolution of fruit type in this difficult group and the comparative analysis highlighted the significant incongruities in evolutionary pattern between the two datasets.
Data from: Combining and Comparing Morphometric Shape Descriptors with a Molecular Phylogeny: The Case of Fruit Type Evolution in Bornean Lithocarpus (Fagaceae)
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Data related to: MACHINE LEARNING AND QUANTUM MECHANICS APPROACH TO MORE CHEMICALLY-AWARE MOLECULAR DESCRIPTORS FOR MEDICINAL CHEMISTRY APPLICATIONS
<p>DEMIN VS QM ELECTRONIC POTENTIAL CORRELATIONS FOR THE N:= ATOM TYPE AND THE N1 ATOM TYPE</p> <p>dEmin versus H-bond basicity scale and H-bond acidity scale </p>
Morphological and molecular descriptors of the developmental cycle of Babesia divergens parasites in human erythrocytes
GEO Series GSE67677. Babesia bovis; Babesia divergens. 6 samples. Type: Expression profiling by array.
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OpenNeuro
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