Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.632283
Title: Polarizable multipolar electrostatics driven by kriging machine learning for a peptide force field : assessment, improvement and up-scaling
Author: Fletcher, Timothy
ISNI:       0000 0004 5360 1061
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2014
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Abstract:
Typical, potential-driven force fields have been usefully applied to small molecules for decades. However, complex effects such as polarisation, π systems and hydrogen bonding remain difficult to model while these effects become increasingly relevant. In fact, these complex electronic effects become crucial when considering larger biological molecules in solution. Instead, machine learning can be used to recognise patterns in chemical behaviour and predict them, sacrificing computational efficiency for accuracy and completeness of the force field. The kriging machine learning method is capable of taking the geometric features of a molecule and predicting its electrostatic properties after being trained using ab initio data of the same system. We present significant improvements in functionality, application and understanding of the kriging machine learning as part of an electrostatic force field. These improvements are presented alongside an up-scaling of the problems the force field is applied to. The force field predicts electrostatic energies for all common amino acids with a mean error of 4.2 kJmol-1 (1 kcal mol-1), cholesterol with a mean error of 3.9 kJmol-1 and a 10-alanine helix with a mean error of 6.4 kJmol-1. The kriging machine learning has been shown to work identically with charged systems, π systems and hydrogen bonded systems. This work details how different chemical environments and parameters affect the kriging model quality and assesses optimal methods for computationally-efficient kriging of multipole moments. In addition to this, the kriging models have been used to predict moments for atoms they have had no training data for with little loss in accuracy. Thus, the kriging machine learning has been shown to produce transferable models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.632283  DOI: Not available
Keywords: QCT ; QTAIM ; Multipoles ; Kriging ; Force Fields ; Machine Learning ; Electrostatics
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