dc.contributor.author | Zhou, Ce | |
dc.contributor.author | Ieritano, Christian | |
dc.contributor.author | Hopkins, William Scott | |
dc.date.accessioned | 2020-10-27 13:45:15 (GMT) | |
dc.date.available | 2020-10-27 13:45:15 (GMT) | |
dc.date.issued | 2019-08-07 | |
dc.identifier.uri | https://doi.org/10.3389/fchem.2019.00519 | |
dc.identifier.uri | http://hdl.handle.net/10012/16474 | |
dc.description | Published by 'Frontiers in Chemistry' at 10.3389/fchem.2019.00519. | en |
dc.description.abstract | Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful for difficult non-linear optimization problems with multiple modalities and variables. Applications of these algorithms range from characterization of molecular states in statistical physics and molecular biology to geometric packing problems. A key feature of BH is the fact that one can generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima. These results can then be utilized to gain insights into molecular dynamics and thermodynamic properties. Here we describe how one can employ concepts from unsupervised machine learning to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. Specifically, we introduce the concepts of similarity indices, hierarchical clustering, and multidimensional scaling to the BH methodology. These same machine learning techniques can be used as tools for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections). We exemplify this in two case studies: (1) assigning the infrared multiple photon dissociation spectrum of the protonated serine dimer and (2) determining the temperature-dependent collision cross-section of protonated alanine tripeptide. | en |
dc.description.sponsorship | WH acknowledges funding from the Natural Sciences and Engineering Research Council (NSERC) of Canada in the form of a Discovery Grant and from the Province of Ontario in the form of an Early Researcher Award (ERA). CI acknowledges funding from NSERC in the form of a post graduate scholarship. | en |
dc.language.iso | en | en |
dc.publisher | Frontiers | en |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | serine dimer | en |
dc.subject | polyalanine | en |
dc.subject | collision cross section | en |
dc.subject | IRMPD | en |
dc.subject | hierarchical clustering | en |
dc.subject | potential energy surface | en |
dc.subject | global optimization | en |
dc.subject | vibrational spectroscopy | en |
dc.title | Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility | en |
dc.type | Article | en |
dcterms.bibliographicCitation | Zhou C, Ieritano C and Hopkins WS (2019) Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility. Front. Chem. 7:519. doi: 10.3389/fchem.2019.00519 | en |
uws.contributor.affiliation1 | Faculty of Science | en |
uws.contributor.affiliation2 | Chemistry | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Reviewed | en |
uws.scholarLevel | Faculty | en |
uws.scholarLevel | Post-Doctorate | en |
uws.scholarLevel | Graduate | en |