Name: Richard Messerly
Type: User
Company: Los Alamos National Laboratory
Bio: My expertise is molecular simulation, computational chemistry, and machine learning. I received my PhD in Chemical Engineering from Brigham Young University.
Location: Los Alamos, NM
Richard Messerly's Projects
Abstracts for AIChE 2018
My first repository
Submission to Industrial Fluid Properties Simulation Challenge 10
Merging the MBAR and ITIC methodologies to optimize a TraPPE force switch model
This repository is designed to compare the MBAR results that are obtained for the Lennard-Jones representation of methane using configurations from 92 states and from just a single state.
Files used to analyze Mess-UP force-field
Codes used for determining the uncertainty in the regression parameters from GEMC.
Publication focusing on a Bayesian inference analysis of the Mie potenial for high pressures.
Reversible Jump MCMC for the Lennard-Jones fluid when predicting Heat of Vaporization
Manuscript for Special Issue to Fluid Phase Equilibria due August 15th, 2018