A generative graph machine learning method for transition state sampling in molecular systems

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dc.contributor.authorBokul, Saffat
dc.contributor.examiningcommitteeDavis, Rebecca (Chemistry)
dc.contributor.examiningcommitteeWang, Yang (Computer Science)
dc.contributor.supervisorLivi, Lorenzo
dc.date.accessioned2023-08-02T19:36:13Z
dc.date.available2023-08-02T19:36:13Z
dc.date.issued2023-07-28
dc.date.submitted2023-07-28T16:27:21Zen_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)
dc.description.abstractTransition states (TS) are important molecular conformations with applications in drug development, materials science and biology. The transient nature of these conformations makes studying them extremely challenging. Simulating these states requires expensive computational algorithms whose convergence is never guaranteed. Machine learning (ML) has led to significant improvements in various domains, including computational chemistry. In this thesis, we propose a novel ML pipeline for sampling TS in molecular systems. We combine the expressive capability of graph neural networks to encode graph representations of molecular conformations which is then used for training, with generative models that are able to predict TS structure as output. Using our proposed method, we successfully demonstrate the sampling of minimum energy pathways and conformational TS in alanine dipeptide in one-shot without the need for any example TS or reaction coordinates. Our unsupervised novel ML approach shows promise in TS search and could lead the path for further innovations and analysis of more complex chemical systems.
dc.description.noteOctober 2023
dc.identifier.urihttp://hdl.handle.net/1993/37442
dc.language.isoeng
dc.rightsopen accessen_US
dc.subjectMachine Learning
dc.subjectGraph Machine Learning
dc.subjectGenerative Networks
dc.subjectTransition States
dc.subjectArtificial Intelligence
dc.subjectComputational Chemistry
dc.subjectMolecular Dynamics
dc.subjectMinimum Energy Pathway
dc.titleA generative graph machine learning method for transition state sampling in molecular systems
dc.typemaster thesisen_US
local.subject.manitobano
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