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Front. Chem. | doi: 10.3389/fchem.2019.00519

Augmenting Basin-Hopping with Techniques from Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

Ce Zhou1, Christian Ieritano1 and  William S. Hopkins1*
  • 1University of Waterloo, Canada

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 (ML) to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. These same ML techniques can be used as a tool for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections).

Keywords: global optimization, potential energy surface (PES), hierarchical clustering, vibrational spectroscopy, IRMPD, Alanine (Ala), Serine (Ser), Collision cross section (CCS)

Received: 30 Apr 2019; Accepted: 08 Jul 2019.

Edited by:

Emilio Martinez-Nunez, Department of Physical Chemistry, Faculty of Chemistry, University of Santiago de Compostela, Spain

Reviewed by:

Tomás González-Lezana, Spanish National Research Council (CSIC), Spain
Daniel Pelaez, Université de Lille, France  

Copyright: © 2019 Zhou, Ieritano and Hopkins. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. William S. Hopkins, University of Waterloo, Waterloo, Canada, shopkins@uwaterloo.ca