首页|Data on Machine Learning Reported by Betsy M. Rice and Colleagues (Predicting Hy drocarbon Strain Energy via a Group Equivalent Machine Learning Approach)
Data on Machine Learning Reported by Betsy M. Rice and Colleagues (Predicting Hy drocarbon Strain Energy via a Group Equivalent Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Aberdeen Provi ng Ground, Maryland, by NewsRx journalists, research stated, "Strain energy is a fundamental measure of the steric and configurational properties of organic mol ecules. The ability to estimate strain energy through quantum chemical simulatio ns requires at minimum the knowledge of an initial set of nuclear coordinates." The news reporters obtained a quote from the research, "In general, such knowled ge is not categorically known when screening or generating large numbers of mole cule candidates in the context of molecular design. We present a machine learnin g approach to predict hydrocarbon strain energies using Benson group equivalents . A featurization strategy is crafted by concatenating the molecule group equiva lent counts with easily computable molecular fingerprints. The data are obtained from electronic structure calculations we performed on a set of 166 previously synthesized strained hydrocarbons. These data are provided and include gas phase enthalpies of formation and associated optimized atomic coordinates."
Aberdeen Proving GroundMarylandUnite d StatesNorth and Central AmericaCyborgsEmerging TechnologiesHydrocarbon sMachine LearningOrganic Chemicals
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