Andrew White

Assistant Professor
University of Washington, PhD, 2013

208C Gavett Hall
(585) 276-7395
Fax: (585.) 273-1348


Selected Honors & Awards

Institute for Biophysics Dynamics Yen Fellow, 2013
Runstead Fellow, 2008-2009


ChE 116: Numerical Methods and Stats
ChE 477: Advanced Numerical Methods: Theory to Implementation

Recent Publications

Complete Publication List

Freeman, GM.; Drennen, TE.; White, AD. "Can Parked Cars and Carbon Taxes Create a Profit? The Economics of Vehicle-to-Grid Storage for Peak Reduction." Energy Policy. 2017, 106:183-190.

White, AD.; Knight, C.; Hocky, GM.; Voth, GA. "Communication: Improved ab initio Molecular Dynamics by Minimally Biasing wih Experimental Data." The Journal of Chemical Physics. 2017, 146:041102-5.

Dannenhaoffer-Lafage, T.; White, AD.; Voth, GA. "A Direct method of Incorporating Experimental Data into Multiscale Coarse-Grained Models." Journal of Chemical Theory and Computation2016, 12(5):2144-2153.

White, AD.; Dama, JF,; Voth, GA. "Designing Free Energy Surfaces that Match Experimental Data with Metadynamics." Journal of Chemical Theory and Computation2015, 11:2451-2460.

White, AD.; Voth, GA. "An Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data." Journal of Chemical Theory and Computation. 2014, 10:3023-3030.

Research Overview

My group uses experiments, molecular simulations, and machine-learning to design new materials. Experiments answer the essential question of if and how well a material works for a particular application. Molecular simulation provides the molecular insight into why a material works. Machine-learning provides the tool to optimize a material so that it works best. Members of my group apply these three techniques to craft new materials for biomedical devices and lithium ion batteries. One of the main class of materials we study is peptides, which are derived from the constituent amino acids that make up proteins. Peptides have a great chemical diversity yet can be controlled on the near atomic scale.