Deep Dynamic Graphical Models for Molecular Kinetics
dc.contributor.author | Gao, Wenli | |
dc.contributor.author | Su, Enmin | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
dc.contributor.examiner | Damaschke, Peter | |
dc.contributor.supervisor | Olsson, Simon | |
dc.date.accessioned | 2023-12-15T15:51:34Z | |
dc.date.available | 2023-12-15T15:51:34Z | |
dc.date.issued | 2023 | |
dc.date.submitted | 2023 | |
dc.description.abstract | With the massive growth of molecular dynamics simulation results comes a great demand for efficient analysis methods to distill essential information from simulation and enable quantitative characterization of molecular properties. Dynamic Graphical Model (DGM) is currently the most data-efficient method towards this goal. However, DGMs rely on extensive manual intervention by experts: division of molecules into smaller subsystems and their discretization into an unknown number of states. We aim to automate this expert-guided procedure using a deep-learning approach and make an end-to-end learning system. To achieve this, we examine the Variational Approach to Markov Processes (VAMP), and its ability to detect meta-stable subsystems in molecular systems, and to decide the number of states for each subsystem. We put forward a model which uses VAMP to learn subsystem states via a deep neural network and DGM to connect the subsystems by modeling their time-correlated dynamics. The model is trained in an end-to-end manner and optimized using a weighted sum of VAMP loss, DGM loss, and regularizer. We also introduce a pruning-based algorithm to automatically decide the number of states per subsystem. Our results show that VAMP is suitable for enumerating subsystems of a molecular system, however, VAMP alone cannot decide the number of states for each subsystem. This thesis sheds light on how an end-to-end learning system may be built with DGMs to analyze molecular dynamics and outlines possible future extensions of this work. | |
dc.identifier.coursecode | DATX05 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12380/307434 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Machine learning | |
dc.subject | Molecular Simulation Analysis | |
dc.subject | Dynamic Graphic Models | |
dc.subject | Variational Approach to Markov Processes | |
dc.title | Deep Dynamic Graphical Models for Molecular Kinetics | |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.degree | Master's Thesis | en |
dc.type.uppsok | H | |
local.programme | Data science and AI (MPDSC), MSc | |
local.programme | Computer science – algorithms, languages and logic (MPALG), MSc |
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