The seeds of the future are in the present: A blind exploration of metastable states
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916878004256768 |
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| author | Devergne, Timothée Kostic, Vladimir Pontil, Massimiliano Parrinello, Michele |
| author_facet | Devergne, Timothée Kostic, Vladimir Pontil, Massimiliano Parrinello, Michele |
| contents | In this work, we present a novel type of molecular dynamics simulation that aims at discovering, in a blind way, new metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural networks to generalize. Contrary to most other enhanced sampling methods, which need previous knowledge of the reactive process, we are able to discover in a blind way new metastable states, overcoming otherwise insuperable kinetic bottlenecks. We illustrate the workings of the method with a number of instructive examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The seeds of the future are in the present: A blind exploration of metastable states Devergne, Timothée Kostic, Vladimir Pontil, Massimiliano Parrinello, Michele Chemical Physics In this work, we present a novel type of molecular dynamics simulation that aims at discovering, in a blind way, new metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural networks to generalize. Contrary to most other enhanced sampling methods, which need previous knowledge of the reactive process, we are able to discover in a blind way new metastable states, overcoming otherwise insuperable kinetic bottlenecks. We illustrate the workings of the method with a number of instructive examples. |
| title | The seeds of the future are in the present: A blind exploration of metastable states |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2508.01477 |