(731i) New Methods for Combining Experimental Data and Molecular Simulations into Hybrid Models
AIChE Annual Meeting
Thursday, November 12, 2015 - 5:15pm to 5:30pm
Creating models that are consistent with experimental data is an essential task in computational modeling. This is generally done by iteratively tuning the input parameters of a simulation to match experimental data. A new, alternative method is to bias a simulation to match experimental data leading to a hybrid model composed of the original model and biasing terms derived from experimental data. This second approach has been shown to be the unique minimal bias and I will describe methods to achieve this in molecular simulation. It can be done by modifying average values of collective variables or morphing the PMF of a simulation into a desired target. This new approach has been designed for cases where experimental data provides partial information but can be integrated with simulations for a more complete picture. The example systems considered in this talk are Lennard-Jones fluids, electrolyte solutions, and DFT simulations of water with solvated protons. The sources of experimental data for these systems comes from quantum chemistry calculations, bioinformatics data, and experiments.