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Designing a machine learning potential for molecular simulation of liquid alkanes


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Type

Thesis

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Authors

Abstract

Molecular simulation is applied to understanding the behaviour of alkane liquids with the eventual goal of being able to predict the viscosity of an arbitrary alkane mixture from first principles. Such prediction would have numerous scientific and industrial applications, as alkanes are the largest component of fuels, lubricants, and waxes; furthermore, they form the backbones of a myriad of organic compounds. This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical potential energy surface using machine learning. This approximation has the advantage of producing forces and energies of nearly quantum mechanical accuracy at a tiny fraction of the usual cost. It enables accurate simulation of the large systems and long timescales required for accurate prediction of properties such as the density and viscosity. The approach is developed and tested on methane, the simplest alkane, and investigations are made into potentials for longer, more complex alkanes. The results show that the approach is promising and should be pursued further to create an accurate machine learning potential for the alkanes. It could even be extended to more complex molecular liquids in the future.

Description

Date

2018-09-14

Advisors

Csanyi, Gabor

Keywords

machine learning, many-body dispersion, alkanes, liquid simulation, viscosity, DFT, molecular dynamics

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Sponsorship
EPSRC (1602415)
First-year training funded by the EPSRC as part of the centre for doctoral training in computational methods for materials science (CDT CMM) under grant number EP/L015552/1. PhD studentship funding by Shell Global Solutions International B.V. Computer time provided by ARCHER (http://archer.ac.uk) under the UKCP Consortium, EPSRC grant number EP/P022596/1.