arXiv:2606.06295v1 Announce Type: new Abstract: Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce Flux Matching, a framework that learns two complementary objects directly from reactive trajectory data: a current velocity $u(z)$, whose streamlines trace the dominant reaction pathways, and a scalar potential $h(z)$, obtained from a weighted Helmholtz-Hodge decomposition of the reactive current, that serves as a data-driven reaction coordinate. Both minimize quadratic
The continuous advancements in AI and machine learning are enabling more sophisticated methods for analyzing complex scientific data, pushing the boundaries of discovery.
This development offers a novel, data-driven approach to accelerate mechanism discovery in fields like chemistry and biology, potentially unlocking new materials, drugs, and understanding of fundamental processes.
The ability to automatically identify dominant reaction pathways and key reaction coordinates from reactive trajectory data shifts how complex scientific problems are approached, reducing reliance on manual intuition.
- · Pharmaceuticals
- · Materials science
- · AI/ML research labs
- · Biotechnology
- · Traditional experimental trial-and-error methods
Accelerated discovery of novel biological and chemical processes and materials.
Reduced R&D costs and timelines for drug development and advanced materials engineering.
Emergence of new industries based on previously undiscoverable chemical or biological routes.
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