Understanding Reaction Mechanisms from Start to Finish
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917437969006592 |
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| author | Breebaart, Rik S. Lazzeri, Gianmarco Covino, Roberto Bolhuis, Peter G. |
| author_facet | Breebaart, Rik S. Lazzeri, Gianmarco Covino, Roberto Bolhuis, Peter G. |
| contents | Understanding mechanisms of rare but important events in complex molecular systems, such as protein folding or ligand (un)binding, requires accurately mapping transition paths from an initial to a final state. The committor is the ideal reaction coordinate for this purpose, but calculating it for high-dimensional, nonlinear systems has long been considered intractable. Here, we introduce an iterative path sampling strategy for computing the committor function for systems with high free energy barriers. We start with an initial guess to define isocommittor interfaces for transition interface sampling. The resulting path ensemble is then reweighted and used to train a neural network, yielding a more accurate committor model. This process is repeated until convergence, effectively solving the long-standing circular problem in enhanced sampling where a good reaction coordinate is needed to generate efficient sampling, and vice-versa. The final, converged committor model can be interrogated to extract mechanistic insights. We demonstrate the power of our method on a benchmark 2D potential and a more complex host-guest (un)binding process in explicit solvent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Reaction Mechanisms from Start to Finish Breebaart, Rik S. Lazzeri, Gianmarco Covino, Roberto Bolhuis, Peter G. Chemical Physics Statistical Mechanics Computational Physics Understanding mechanisms of rare but important events in complex molecular systems, such as protein folding or ligand (un)binding, requires accurately mapping transition paths from an initial to a final state. The committor is the ideal reaction coordinate for this purpose, but calculating it for high-dimensional, nonlinear systems has long been considered intractable. Here, we introduce an iterative path sampling strategy for computing the committor function for systems with high free energy barriers. We start with an initial guess to define isocommittor interfaces for transition interface sampling. The resulting path ensemble is then reweighted and used to train a neural network, yielding a more accurate committor model. This process is repeated until convergence, effectively solving the long-standing circular problem in enhanced sampling where a good reaction coordinate is needed to generate efficient sampling, and vice-versa. The final, converged committor model can be interrogated to extract mechanistic insights. We demonstrate the power of our method on a benchmark 2D potential and a more complex host-guest (un)binding process in explicit solvent. |
| title | Understanding Reaction Mechanisms from Start to Finish |
| topic | Chemical Physics Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2507.04052 |