Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
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| Format: | Preprint |
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2024
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| _version_ | 1866913561141313536 |
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| author | Trizio, Enrico Rizzi, Andrea Piaggi, Pablo M. Invernizzi, Michele Bonati, Luigi |
| author_facet | Trizio, Enrico Rizzi, Andrea Piaggi, Pablo M. Invernizzi, Michele Bonati, Luigi |
| contents | Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external potential to accelerate them is a popular approach to address this problem. In particular, $\texttt{PLUMED}$ is a community-developed library that implements several methods for CV-based enhanced sampling. This chapter discusses two recent developments that have gained popularity in recent years. The first is the On-the-fly Probability Enhanced Sampling (OPES) method as a biasing scheme. This provides a unified approach to enhanced sampling able to cover many different scenarios: from free energy convergence to the discovery of metastable states, from rate calculation to generalized ensemble simulation. The second development concerns the use of machine learning (ML) approaches to determine CVs by learning the relevant variables directly from simulation data. The construction of these variables is facilitated by the $\texttt{mlcolvar}$ library, which allows them to be optimized in Python and then used to enhance sampling thanks to a native interface inside $\texttt{PLUMED}$. For each of these methods, in addition to a brief introduction, we provide guidelines, practical suggestions and point to examples from the literature to facilitate their use in the study of the process of interest. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_18019 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables Trizio, Enrico Rizzi, Andrea Piaggi, Pablo M. Invernizzi, Michele Bonati, Luigi Computational Physics Biological Physics Chemical Physics Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external potential to accelerate them is a popular approach to address this problem. In particular, $\texttt{PLUMED}$ is a community-developed library that implements several methods for CV-based enhanced sampling. This chapter discusses two recent developments that have gained popularity in recent years. The first is the On-the-fly Probability Enhanced Sampling (OPES) method as a biasing scheme. This provides a unified approach to enhanced sampling able to cover many different scenarios: from free energy convergence to the discovery of metastable states, from rate calculation to generalized ensemble simulation. The second development concerns the use of machine learning (ML) approaches to determine CVs by learning the relevant variables directly from simulation data. The construction of these variables is facilitated by the $\texttt{mlcolvar}$ library, which allows them to be optimized in Python and then used to enhance sampling thanks to a native interface inside $\texttt{PLUMED}$. For each of these methods, in addition to a brief introduction, we provide guidelines, practical suggestions and point to examples from the literature to facilitate their use in the study of the process of interest. |
| title | Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables |
| topic | Computational Physics Biological Physics Chemical Physics |
| url | https://arxiv.org/abs/2410.18019 |