Designing a machine learning potential for molecular simulation of liquid alkanes

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.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2018

Author dc:creator

• Veit, Max

Advisor dc:contributor.advisor

• Csanyi, Gabor

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

Statement dc:rights

• Chapter 4, section 4.1 (pp. 69-93) adapted with permission from: Max Veit, Sandeep Kumar Jain, Satyanarayana Bonakala, Indranil Rudra, Detlef Hohl, and Gábor Csányi, "Equation of state of fluid methane from first principles with machine learning potentials", Journal of Chemical Theory and Computation, DOI: 10.1021/acs.jctc.8b01242 , in press. Copyright 2019 American Chemical Society.

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/06414972-e9d2-43df-a758-d831b0ed371c/download
• https://creativecommons.org/licenses/by-sa/4.0/

Language dc:language

en

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.37522

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/290295