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Abstract Bond dissociation energy prediction for the carbon halogen (C–X) bond is quite important in chemistry, due to range applications of C–X bond in the drug design, reaction mechanism and material sciences fields. In the present research, a robust machine learning workflow was explored, to accurately predict the bond dissociation energy values of C–X bond.
ABSTRACT Metal-organic frameworks (MOF) serve as versatile platforms for the development of multifunctional heterogeneous catalysts, enabling synergistic and tandem reactions owing to their diverse compositions, tunable pore sizes, and high porosities. MOF can accommodate multiple catalytic centers, either during synthesis or via post-synthetic modification, employing open metal sites, functionalized linkers, or encapsulated guest species as active sites.
ABSTRACT The advent of Web 2.0 allows users to generate multimedia content, leading to exponentially growing user-generated multimedia content. Subsequently, the users' information needs are transmuted from converse lookup to diverse exploration and discovery-oriented search activities. Despite being a gateway to accomplishing search tasks, search engines are inadequate in supporting the discovery of multimedia results.
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