A renormalization-group-inspired scale-splitting algorithm generates hierarchical formulas for dynamics in large dilute chemical reaction networks, illustrated on the formose reaction.
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The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.
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Hierarchical models for large chemical reaction networks
A renormalization-group-inspired scale-splitting algorithm generates hierarchical formulas for dynamics in large dilute chemical reaction networks, illustrated on the formose reaction.
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Astrochemical Study of Early Embedded Disks
The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.