2023
DOI: 10.1039/d3dd00020f
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Implementation of rare isotopologues into machine learning of the chemical inventory of the solar-type protostellar source IRAS 16293-2422

Abstract: Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. In interstellar sources further along the path of star formation, such...

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Cited by 4 publications
(6 citation statements)
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“…Hence, quantum chemistry will long have a place in astrochemistry especially as the accuracy of the methods and sizes of the molecules studied to such accuracies increase. While modern techniques like machine learning may have impacts in various areas of astrochemistry, the future of such methods remains to be seen. It will likely be utilized in conjunction with existing quantum chemical techniques to push the generation of astrochemical reference data in new and beneficial directions.…”
Section: Discussionmentioning
confidence: 99%
“…Hence, quantum chemistry will long have a place in astrochemistry especially as the accuracy of the methods and sizes of the molecules studied to such accuracies increase. While modern techniques like machine learning may have impacts in various areas of astrochemistry, the future of such methods remains to be seen. It will likely be utilized in conjunction with existing quantum chemical techniques to push the generation of astrochemical reference data in new and beneficial directions.…”
Section: Discussionmentioning
confidence: 99%
“…ML techniques have been brought to bear on modeling such environments in the case of protostars. Novel molecular targets have now been predicted in reaction networks providing impetus for subsequent observation . Additionally, ML has shown that it competes with traditional modeling needed to provide column densities (and, hence, emission peak heights) in the detection of C 10 H – , the first interstellar anion observed in more than a dozen years. , …”
Section: The 2050 Horizonmentioning
confidence: 99%
“…Novel molecular targets have now been predicted in reaction networks providing impetus for subsequent observation. 69 Additionally, ML has shown that it competes with traditional modeling needed to provide column densities (and, hence, emission peak heights) in the detection of C 10 H − , the first interstellar anion observed in more than a dozen years. 70,71 As such, the ML appears to be on the cusp of securing a place in how chemical reaction networks are processed.…”
Section: Modeling By 2050mentioning
confidence: 99%
“…Recently, as an attempt to predict strong molecular candidates for detection in the Class 0 protostellar source IRAS 16293 −2422B (hereafter referred to as IRAS 16293B), Fried et al (2023) trained a machine-learning model to map the molecular identities of the detected species to their observed column densities. The trained regressors could then be used to predict the column density of any other molecule in this region of interstellar space.…”
Section: Introductionmentioning
confidence: 99%
“…The rotational spectrum of this molecule has not been measured beyond ∼40 GHz. This, however, is necessary for observations in the star-forming regions, where Fried et al (2023) and others predict potential detection, as at the warmer excitation temperatures (ranging to several hundred kelvin in these sources) the strongest transitions of these species arise in the millimeter and submillimeter regimes.…”
Section: Introductionmentioning
confidence: 99%