Refining coarse-grained molecular topologies: a Bayesian optimization approach
Abstract Molecular Dynamics (MD) simulations are vital for predicting the physical and chemical properties of molecular systems across various ensembles. While All-Atom (AA) MD provides high accuracy, its computational cost has spurred the development of Coarse-Grained MD (CGMD), which simplifies mo...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01729-9 |
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| _version_ | 1849234915972874240 |
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| author | Pranoy Ray Adam P. Generale Nikhith Vankireddy Yuichiro Asoma Masataka Nakauchi Haein Lee Katsuhisa Yoshida Yoshishige Okuno Surya R. Kalidindi |
| author_facet | Pranoy Ray Adam P. Generale Nikhith Vankireddy Yuichiro Asoma Masataka Nakauchi Haein Lee Katsuhisa Yoshida Yoshishige Okuno Surya R. Kalidindi |
| author_sort | Pranoy Ray |
| collection | DOAJ |
| description | Abstract Molecular Dynamics (MD) simulations are vital for predicting the physical and chemical properties of molecular systems across various ensembles. While All-Atom (AA) MD provides high accuracy, its computational cost has spurred the development of Coarse-Grained MD (CGMD), which simplifies molecular structures into representative beads to reduce expense but sacrifice precision. CGMD methods like Martini3, calibrated against experimental data, generalize well across molecular classes but often fail to meet the accuracy demands of domain-specific applications. This work introduces a Bayesian Optimization-based approach to refine Martini3 topologies—specifically the bonded interaction parameters within a given coarse-grained mapping—for specialized applications, ensuring accuracy and efficiency. The resulting optimized CG potential accommodates any degree of polymerization, offering accuracy comparable to AA simulations while retaining the computational speed of CGMD. By bridging the gap between efficiency and accuracy, this method advances multiscale molecular simulations, enabling cost-effective molecular discovery for diverse scientific and technological fields. |
| format | Article |
| id | doaj-art-92684ee46eb34fee8e09dd6584fc76e5 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-92684ee46eb34fee8e09dd6584fc76e52025-08-20T04:02:56ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111110.1038/s41524-025-01729-9Refining coarse-grained molecular topologies: a Bayesian optimization approachPranoy Ray0Adam P. Generale1Nikhith Vankireddy2Yuichiro Asoma3Masataka Nakauchi4Haein Lee5Katsuhisa Yoshida6Yoshishige Okuno7Surya R. Kalidindi8George W. Woodruff School of Mechanical Engineering, Georgia Institute of TechnologyGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of TechnologyMultiscale Technologies Inc.Resonac CorporationResonac CorporationResonac CorporationResonac CorporationResonac CorporationGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of TechnologyAbstract Molecular Dynamics (MD) simulations are vital for predicting the physical and chemical properties of molecular systems across various ensembles. While All-Atom (AA) MD provides high accuracy, its computational cost has spurred the development of Coarse-Grained MD (CGMD), which simplifies molecular structures into representative beads to reduce expense but sacrifice precision. CGMD methods like Martini3, calibrated against experimental data, generalize well across molecular classes but often fail to meet the accuracy demands of domain-specific applications. This work introduces a Bayesian Optimization-based approach to refine Martini3 topologies—specifically the bonded interaction parameters within a given coarse-grained mapping—for specialized applications, ensuring accuracy and efficiency. The resulting optimized CG potential accommodates any degree of polymerization, offering accuracy comparable to AA simulations while retaining the computational speed of CGMD. By bridging the gap between efficiency and accuracy, this method advances multiscale molecular simulations, enabling cost-effective molecular discovery for diverse scientific and technological fields.https://doi.org/10.1038/s41524-025-01729-9 |
| spellingShingle | Pranoy Ray Adam P. Generale Nikhith Vankireddy Yuichiro Asoma Masataka Nakauchi Haein Lee Katsuhisa Yoshida Yoshishige Okuno Surya R. Kalidindi Refining coarse-grained molecular topologies: a Bayesian optimization approach npj Computational Materials |
| title | Refining coarse-grained molecular topologies: a Bayesian optimization approach |
| title_full | Refining coarse-grained molecular topologies: a Bayesian optimization approach |
| title_fullStr | Refining coarse-grained molecular topologies: a Bayesian optimization approach |
| title_full_unstemmed | Refining coarse-grained molecular topologies: a Bayesian optimization approach |
| title_short | Refining coarse-grained molecular topologies: a Bayesian optimization approach |
| title_sort | refining coarse grained molecular topologies a bayesian optimization approach |
| url | https://doi.org/10.1038/s41524-025-01729-9 |
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