Path Sampling for Rare Events Boosted by Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918323930791936 |
|---|---|
| author | Minh, Porhouy Sarupria, Sapna |
| author_facet | Minh, Porhouy Sarupria, Sapna |
| contents | The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05167 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Path Sampling for Rare Events Boosted by Machine Learning Minh, Porhouy Sarupria, Sapna Computational Physics Soft Condensed Matter Statistical Mechanics Machine Learning Chemical Physics The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations. |
| title | Path Sampling for Rare Events Boosted by Machine Learning |
| topic | Computational Physics Soft Condensed Matter Statistical Mechanics Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2602.05167 |