Bartok, AP and Gillan, MJ and Manby, FR and Csanyi, G Machine learning for predictive condensed-phase simulation. (Unpublished)Full text not available from this repository.
We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.
|Uncontrolled Keywords:||cond-mat.mtrl-sci cond-mat.mtrl-sci|
|Divisions:||Div C > Applied Mechanics|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:34|
|Last Modified:||25 Feb 2017 00:18|