The Atomistic Reconstruction of Coarse-Grained Polymeric Systems via Machine Learning Techniques

dc.contributor.authorOlsen, Jake Dylan
dc.date.accessioned2020-09-29T22:08:01Z
dc.date.available2020-09-29T22:08:01Z
dc.date.issued2020
dc.description46 pages
dc.description.abstractThe development of a statistically accurate backmapping procedure, coupled with an accurate coarse-graining (CG) method, is necessary as it would allow a system to freely transform between varying degrees of CG. This ability allows for the computational gain of CG with the resolution of atomistic simulations. Therefore, using state-of-the-art machine learning techniques coupled with atomistic simulation data, we have developed a backmapping procedure for CG polymeric systems. Specifically, we used a gated recurrent unit (GRU) to learn the atomistic structure within a single CG site of a polyethylene system. A categorical cross-entropy loss function was used to allow for more flexibility in the model. The model’s training yielded consistent loss and validation loss demonstrating that the model did not overfit the data. Furthermore, the model was able to accurately reproduce a variety of structural quantities, such as the bond angle, bond length, dihedral angle, mean-square internal distance (MSID), end-to-end distance, and distribution about the center-of-mass.en_US
dc.identifier.urihttps://hdl.handle.net/1794/25793
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.subjectPhysical Chemistryen_US
dc.subjectChemistryen_US
dc.subjectCourse-grainingen_US
dc.subjectBackmappingen_US
dc.subjectMachine Learningen_US
dc.subjectMolecular Dynamicsen_US
dc.subjectPolymersen_US
dc.titleThe Atomistic Reconstruction of Coarse-Grained Polymeric Systems via Machine Learning Techniques
dc.typeThesis/Dissertation

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