Show simple item record

dc.contributor.authorZhou, Ce
dc.contributor.authorIeritano, Christian
dc.contributor.authorHopkins, William Scott 13:45:15 (GMT) 13:45:15 (GMT)
dc.descriptionPublished by 'Frontiers in Chemistry' at 10.3389/fchem.2019.00519.en
dc.description.abstractEvolutionary 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.sponsorshipWH 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.rightsAttribution 4.0 International*
dc.subjectserine dimeren
dc.subjectcollision cross sectionen
dc.subjecthierarchical clusteringen
dc.subjectpotential energy surfaceen
dc.subjectglobal optimizationen
dc.subjectvibrational spectroscopyen
dc.titleAugmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobilityen
dcterms.bibliographicCitationZhou 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.2019.00519en
uws.contributor.affiliation1Faculty of Scienceen

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages