Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning
Anbu Selvam Venkatachalam, Loren Greenman, Joshua Stallbaumer, Artem Rudenko, Daniel Rolles, Huynh Van Sa Lam · 2025
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Description
Anbu Venkatachalam and five co-authors use in 2025 machine learning to read Coulomb explosion patterns and tell molecular structures apart. Blasting a molecule into ionic shards and catching up to eight in coincidence maps how its atoms move during ultrafast reactions. The data are rich but high-dimensional and hard to eyeball.
Learning algorithms mine the correlations among fragments, turning them into structural fingerprints. The team imaged and separated two forms of dichloroethylene, molecules with the same atoms in different arrangements. The automated pipeline scales to bigger molecules and weaker reaction channels. Such tools could let chemists watch bond-breaking movies and assign each frame to the right pathway.
Catalog record
| CREATOR | Anbu Selvam Venkatachalam, Loren Greenman, Joshua Stallbaumer, Artem Rudenko, Daniel Rolles, Huynh Van Sa Lam |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |