Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.02707 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911060943962112 |
|---|---|
| author | Ray, Pranoy Generale, Adam P. Vankireddy, Nikhith Asoma, Yuichiro Nakauchi, Masataka Lee, Haein Yoshida, Katsuhisa Okuno, Yoshishige Kalidindi, Surya R. |
| author_facet | Ray, Pranoy Generale, Adam P. Vankireddy, Nikhith Asoma, Yuichiro Nakauchi, Masataka Lee, Haein Yoshida, Katsuhisa Okuno, Yoshishige Kalidindi, Surya R. |
| contents | Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature ensembles. However, the high computational costs associated with All-Atom (AA) MD simulations have led to the development of Coarse-Grained Molecular Dynamics (CGMD), providing a lower-dimensional compression of the AA structure into representative CG beads, offering reduced computational expense at the cost of predictive accuracy. Existing CGMD methods, such as CG-Martini (calibrated against experimental data), aim to generate an embedding of a topology that sufficiently generalizes across a range of structures. Detrimentally, in attempting to specify parameterization with applicability across molecular classes, it is unable to specialize to domain-specific applications, where sufficient accuracy and computational speed are critical. This work presents a novel approach to optimize derived results from CGMD simulations by refining the general-purpose Martini3 topologies specifically the bonded interaction parameters within a given coarse-grained mapping - for domain-specific applications using Bayesian Optimization methodologies. We have developed and validated a CG potential applicable to any degree of polymerization, representing a significant advancement in the field. Our optimized CG potential, based on the Martini3 framework, aims to achieve accuracy comparable to AAMD while maintaining the computational efficiency of CGMD. This approach bridges the gap between efficiency and accuracy in multiscale molecular simulations, potentially enabling more rapid and cost-effective molecular discovery across various scientific and technological domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach Ray, Pranoy Generale, Adam P. Vankireddy, Nikhith Asoma, Yuichiro Nakauchi, Masataka Lee, Haein Yoshida, Katsuhisa Okuno, Yoshishige Kalidindi, Surya R. Chemical Physics Materials Science Machine Learning Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature ensembles. However, the high computational costs associated with All-Atom (AA) MD simulations have led to the development of Coarse-Grained Molecular Dynamics (CGMD), providing a lower-dimensional compression of the AA structure into representative CG beads, offering reduced computational expense at the cost of predictive accuracy. Existing CGMD methods, such as CG-Martini (calibrated against experimental data), aim to generate an embedding of a topology that sufficiently generalizes across a range of structures. Detrimentally, in attempting to specify parameterization with applicability across molecular classes, it is unable to specialize to domain-specific applications, where sufficient accuracy and computational speed are critical. This work presents a novel approach to optimize derived results from CGMD simulations by refining the general-purpose Martini3 topologies specifically the bonded interaction parameters within a given coarse-grained mapping - for domain-specific applications using Bayesian Optimization methodologies. We have developed and validated a CG potential applicable to any degree of polymerization, representing a significant advancement in the field. Our optimized CG potential, based on the Martini3 framework, aims to achieve accuracy comparable to AAMD while maintaining the computational efficiency of CGMD. This approach bridges the gap between efficiency and accuracy in multiscale molecular simulations, potentially enabling more rapid and cost-effective molecular discovery across various scientific and technological domains. |
| title | Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach |
| topic | Chemical Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2501.02707 |