Machine Learning with and for Molecular Dynamics Simulations
From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | deu |
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Swiss Chemical Society
2019-12-01
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| Series: | CHIMIA |
| Subjects: | |
| Online Access: | https://www.chimia.ch/chimia/article/view/1354 |
| _version_ | 1828863810414313472 |
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| author | Sereina Riniker Shuzhe Wang Patrick Bleiziffer Lennard Böselt Carmen Esposito |
| author_facet | Sereina Riniker Shuzhe Wang Patrick Bleiziffer Lennard Böselt Carmen Esposito |
| author_sort | Sereina Riniker |
| collection | DOAJ |
| description |
From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular
dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up,
interpretation, as well as accuracy of MD simulations.
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| first_indexed | 2024-12-13T03:52:05Z |
| format | Article |
| id | doaj.art-6f4d2f471146465d9d82a4cd5086eee2 |
| institution | Directory Open Access Journal |
| issn | 0009-4293 2673-2424 |
| language | deu |
| last_indexed | 2024-12-13T03:52:05Z |
| publishDate | 2019-12-01 |
| publisher | Swiss Chemical Society |
| record_format | Article |
| series | CHIMIA |
| spelling | doaj.art-6f4d2f471146465d9d82a4cd5086eee22022-12-22T00:00:41ZdeuSwiss Chemical SocietyCHIMIA0009-42932673-24242019-12-01731210.2533/chimia.2019.1024Machine Learning with and for Molecular Dynamics SimulationsSereina Riniker0Shuzhe Wang1Patrick Bleiziffer2Lennard Böselt3Carmen Esposito4Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 Zurich;, Email: sriniker@ethz.chLaboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 ZurichLaboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 ZurichLaboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 ZurichLaboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 Zurich From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up, interpretation, as well as accuracy of MD simulations. https://www.chimia.ch/chimia/article/view/1354Machine learningMolecular dynamics |
| spellingShingle | Sereina Riniker Shuzhe Wang Patrick Bleiziffer Lennard Böselt Carmen Esposito Machine Learning with and for Molecular Dynamics Simulations CHIMIA Machine learning Molecular dynamics |
| title | Machine Learning with and for Molecular Dynamics Simulations |
| title_full | Machine Learning with and for Molecular Dynamics Simulations |
| title_fullStr | Machine Learning with and for Molecular Dynamics Simulations |
| title_full_unstemmed | Machine Learning with and for Molecular Dynamics Simulations |
| title_short | Machine Learning with and for Molecular Dynamics Simulations |
| title_sort | machine learning with and for molecular dynamics simulations |
| topic | Machine learning Molecular dynamics |
| url | https://www.chimia.ch/chimia/article/view/1354 |
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