Deep Dynamic Graphical Models for Molecular Kinetics

Visa enkel post
dc.contributor.authorGao, Wenli
dc.contributor.authorSu, Enmin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineeringen
dc.contributor.examinerDamaschke, Peter
dc.contributor.supervisorOlsson, Simon
dc.date.accessioned2023-12-15T15:51:34Z
dc.date.available2023-12-15T15:51:34Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractWith 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.coursecodeDATX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307434
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectMachine learning
dc.subjectMolecular Simulation Analysis
dc.subjectDynamic Graphic Models
dc.subjectVariational Approach to Markov Processes
dc.titleDeep Dynamic Graphical Models for Molecular Kinetics
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeData science and AI (MPDSC), MSc
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen