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This is the data and assosiated in-house code for the paper: Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning Sander Roet, Christopher D. Daub, and Enrico Riccardi Journal of Chemical Theory and Computation 2021 17 (10), 6193-6202 DOI: 10.1021/acs.jctc.1c00458
simulation data
simulation data
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 22 | |
| downloads | 1 |

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Downloads provided by UsageCounts