Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning
Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-t...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144719 |
| _version_ | 1811071264703381504 |
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| author | Fu, Xiang |
| author2 | Jaakkola, Tommi S. |
| author_facet | Jaakkola, Tommi S. Fu, Xiang |
| author_sort | Fu, Xiang |
| collection | MIT |
| description | Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-time MD simulation. In this paper, we adopt a different machine learning approach where we coarse-grain a physical system using graph clustering, and model the system evolution with a very large time-integration step using graph neural networks. Despite only trained with short MD trajectory data, our learned simulator can generalize to unseen novel systems and simulate for much longer than the training trajectories. Properties requiring 10-100 ns level long-time dynamics can be accurately recovered at several-orders-of-magnitude higher speed than classical force fields. We demonstrate the effectiveness of our method on two realistic complex systems: (1) single-chain coarse-grained polymers in implicit solvent; (2) multi-component Li-ion polymer electrolyte systems. |
| first_indexed | 2024-09-23T08:48:26Z |
| format | Thesis |
| id | mit-1721.1/144719 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:48:26Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1447192022-08-30T04:01:56Z Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning Fu, Xiang Jaakkola, Tommi S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-time MD simulation. In this paper, we adopt a different machine learning approach where we coarse-grain a physical system using graph clustering, and model the system evolution with a very large time-integration step using graph neural networks. Despite only trained with short MD trajectory data, our learned simulator can generalize to unseen novel systems and simulate for much longer than the training trajectories. Properties requiring 10-100 ns level long-time dynamics can be accurately recovered at several-orders-of-magnitude higher speed than classical force fields. We demonstrate the effectiveness of our method on two realistic complex systems: (1) single-chain coarse-grained polymers in implicit solvent; (2) multi-component Li-ion polymer electrolyte systems. S.M. 2022-08-29T16:07:00Z 2022-08-29T16:07:00Z 2022-05 2022-06-21T19:25:58.873Z Thesis https://hdl.handle.net/1721.1/144719 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Fu, Xiang Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title_full | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title_fullStr | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title_full_unstemmed | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title_short | Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning |
| title_sort | simulate time integrated coarse grained molecular dynamics with geometric machine learning |
| url | https://hdl.handle.net/1721.1/144719 |
| work_keys_str_mv | AT fuxiang simulatetimeintegratedcoarsegrainedmoleculardynamicswithgeometricmachinelearning |