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All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search — arXiv:2611.03699

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03699
License (as archived): CC0-1.0
Archived: 2026-11-04T18:55:45.079Z
SHA-256 of the archived text: 53e82dacf95b62bc3b8766d39554dc7fd986956079ce108035db7686861341c3
Stable URL of this page: https://metraipolis.com/documento/dc_320226996337

How to cite

All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search — arXiv:2611.03699 — arXiv (en), https://arxiv.org/abs/2611.03699 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_320226996337 (Metraipolis archive dc_320226996337, sha256 53e82dacf95b62bc3b8766d39554dc7fd986956079ce108035db7686861341c3)

BibTeX

@misc{dc320226996337,
  title = {All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search — arXiv:2611.03699},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03699},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_320226996337; sha256 53e82dacf95b62bc3b8766d39554dc7fd986956079ce108035db7686861341c3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03699. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax\_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation resolves candidate channels and records sampled chain maxima. Single-search benchmarks on three two-dimensional potentials recovered an intermediate and competing routes without transition-state guesses. For eight of nine gas-phase reactions on GFN2-xTB, sampled chain maxima differed from independently refined saddle energies by less than 0.76~kJ\,mol$^{-1}$. Alanine dipeptide instead revealed incomplete conformational relaxation, showing that route connectivity can persist despite structural and energetic errors. Neural committor learning and local straddling-pair sampling connect the resulting paths to transition-region sampling, although molecular shooting diagnostics indicate limited committor calibration. These results show how energy-only exploration and continuation recover candidate pathways, while distinguishing route connectivity from geometric localization, barrier resolution and finite-temperature transition statistics.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Chemical Physics (physics.chem-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.03699 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:35:37.507Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).