A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes
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arXiv
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| Format: | Preprint |
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2024
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| author | Pan, Zhijun Li, Maodong Chen, Dechin Yang, Yi Isaac |
| author_facet | Pan, Zhijun Li, Maodong Chen, Dechin Yang, Yi Isaac |
| contents | To address the time-scale limitations in molecular dynamics (MD) simulations, numerous enhanced sampling methods have been developed to expedite the exploration of complex free energy landscapes. A commonly employed approach accelerates the sampling of degrees of freedom associated with pre-defined collective variables (CVs), which typically tends to traverse the entire CV range. However, in many scenarios, the focus of interest is on specific regions within the CV space. This paper introduces a novel "sinking" approach that enables enhanced sampling of arbitrary areas within the CV space. We begin by proposing a gridded convolutional approximation that productively replicates the effects of metadynamics, a powerful CV-based enhanced sampling technique. Building on this, we present the SinkMeta method, which "sinks" the interior bias potential to create restraining potential "cliffs" at the grid edges. This technique can confine the exploration of CVs in MD simulations to a preset area. Our experimental results demonstrate that SinkMeta requires minimal sampling steps to estimate the free energy landscape for CV subspaces of various shapes and dimensions, including irregular two-dimensional regions and one-dimensional pathways between metastable states. We believe that SinkMeta will pioneer a new paradigm for sampling partial phase spaces, especially offering an efficient and flexible solution for sampling minimum free energy paths in high-dimensional spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes Pan, Zhijun Li, Maodong Chen, Dechin Yang, Yi Isaac Computational Physics Statistical Mechanics Chemical Physics To address the time-scale limitations in molecular dynamics (MD) simulations, numerous enhanced sampling methods have been developed to expedite the exploration of complex free energy landscapes. A commonly employed approach accelerates the sampling of degrees of freedom associated with pre-defined collective variables (CVs), which typically tends to traverse the entire CV range. However, in many scenarios, the focus of interest is on specific regions within the CV space. This paper introduces a novel "sinking" approach that enables enhanced sampling of arbitrary areas within the CV space. We begin by proposing a gridded convolutional approximation that productively replicates the effects of metadynamics, a powerful CV-based enhanced sampling technique. Building on this, we present the SinkMeta method, which "sinks" the interior bias potential to create restraining potential "cliffs" at the grid edges. This technique can confine the exploration of CVs in MD simulations to a preset area. Our experimental results demonstrate that SinkMeta requires minimal sampling steps to estimate the free energy landscape for CV subspaces of various shapes and dimensions, including irregular two-dimensional regions and one-dimensional pathways between metastable states. We believe that SinkMeta will pioneer a new paradigm for sampling partial phase spaces, especially offering an efficient and flexible solution for sampling minimum free energy paths in high-dimensional spaces. |
| title | A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes |
| topic | Computational Physics Statistical Mechanics Chemical Physics |
| url | https://arxiv.org/abs/2411.09385 |