Optimization of force fields for molecular dynamics

A technology for optimization of potential parameters from condensed phase simulations (POP) is discussed and illustrated. It is based on direct calculations of the derivatives of macroscopic observables with respect to the potential parameters. The derivatives are used in a local minimization scheme, comparing simulated and experimental data. In particular, we show that the Newton Trust-Region protocol allows for accurate and robust optimization. POP is illustrated for a toy problem of alanine dipeptide and is applied to folding of the peptide WAAAH. The helix fraction is highly sensitive to the potential parameters while the slope of the melting curve is not. The sensitivity variations make it difficult to satisfy both observations simultaneously. We conjecture that there is no set of parameters that reproduces experimental melting curves of short peptides that are modeled with the usual functional form of a force field. We then apply the newly developed technology to study the liquid mixture of tert-butanol and water. We are able to obtain, after 4 iterations, the correct phase behavior and accurately predict the value of the Kirkwood Buff (KB) integrals. We further illustrate that a potential that is determined solely by KB information, or the pair correlation function, is not necessarily unique.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computational and Applied Mathematics

Grantor

The University of Texas at Austin

Year dc:date.issued

2014

Author dc:creator

• Di Pierro, Michele

Advisor dc:contributor.advisor

• Elber, Ron

• Molecular dynamics
• Optimization
• Force field
• Peptide
• Tert-butanol
• Butyl alcohol
• Tert-butyl
• Parameters refinement
• Molecular mechanics
• Melting curve
• Newton Method
• Simulation
• Biophysics
• Kirkwood-Buff
• Trust region

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/2152/28355

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/28355