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Improving Molecular Dynamics Simulations with Machine Learning

Published on October 13, 2017 by

NCASD Postdoc Gianmarc Grazioli, Lab PI Carter Butts, and Prof. Ioan Andricioaei from the UCI Chemistry Department have published new results showing how the performance of molecular dynamics simulations can be improved with a little help from machine learning.  These results are contained in their forthcoming paper, “Automated Placement of Interfaces in Conformational Kinetics Calculations Using Machine Learning,” to appear in the Journal of Chemical Physics. Their new technique employs a machine learning approach known as a Support Vector Machine  to automatically define high dimensional reaction coordinates for calculating chemical kinetics.  This approach dramatically reduces the cost of studying the complex configurational changes of large biomolecules, such as proteins and DNA, as well as the cost of simulating high-dimensional systems such as those associated with complex chemical reactions. Understanding the complex motions of biomolecules and the kinetics of chemical reactions is essential not only for a deeper fundamental understanding of the molecular machinery that makes life possible, but also for such applications as the computational design of drug molecules and novel materials.

 
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