Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728186
Title: An interacting quantum atoms approach to constructing a conformationally dependent biomolecular force field by Gaussian process regression : potential energy surface sampling and validation
Author: Cardamone, Salvatore
ISNI:       0000 0004 6498 5267
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
The energetics of chemical systems are quantum mechanical in origin and dependent upon the internal molecular conformational degrees of freedom. "Classical force field" strategies are inadequate approximations to these energetics owing to a plethora of simplifications- both conceptual and mathematical. These simplifications have been employed to make the in silico modelling of molecular systems computationally tractable, but are also subject to both qualitative and quantitative errors. In spite of these shortcomings, classical force fields have become entrenched as a cornerstone of computational chemistry. The Quantum Chemical Topological Force Field (QCTFF) has been a central re search theme within our group for a number of years, and has been designed to ameliorate the shortcomings of classical force fields. Within its framework, one can undertake a full spatial decomposition of a chemical system into a set of finite atoms. Atomic properties are subsequently obtained by a rigorous quantum mechanical treatment of the resultant atomic domains through the theory of Interacting Quantum Atoms (IQA). Conformational dependence is accounted for in theQCTFF by use of Gaussian Process Regression, a machine learning technique. In so doing, one constructs an analytical function to provide a mapping from a molecular conformation to a set of atomic energetic quantities. One can subsequently conduct dynamics with these energetic quantities. The notion of "conformational sampling" is shown to be of key importance to the proper construction of the QCTFF. Conformational sampling is a key theme in this work, and a subject that we will expatiate. We suggest a novel conformational sampling scheme, and attempt a number of conformer subset selection strategies to construct optimal machine learning models. The QCTFF is then applied to carbohydrates for the first time, and shown to produce results well within the commonly invoked threshold of "chemical accuracy"- O(β^{-1}), where β is the thermodynamic beta. Finally, we present a number of methodological developments to aid in both the accuracy and tractability of predicting ab initio vibrational spectroscopies.
Supervisor: Popelier, Paul Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.728186  DOI: Not available
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