Selected Honors & Awards

NSF Career Award 2018
Yen Postdoctoral Fellowship at Inst. for Biophys. Dyn. at University of Chicago, 2013
COMSEF Graduate Student Award, AICHE COMSEF division, 2012
Best Poster, Int’l. Congress on Marine Fouling and Corrosion, 2012
Best Poster, Gordon Research Conf.: Water and Aq. Soln., 2010
Institute for Biophysics Dynamics Yen Fellow, 2013
Runstead Fellow, 2008-2009

Course

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

Recent Publications

Complete Publication List

Mayer, H.B.; Lee, S.; White, A.D.; Voth, G.A.; Swanson, J.M.J., "Multiscale Kinetic Modeling Reveals an Ensemble of Cl-/H+ Exchange Pathways in ClC-ec1 Antiporter," Journal of the American Chemical Society,  2018, 140, 5, 1793-1804.

Amirkulova, D.B.; White, A.D., "Combining enhanced samples with experiment-directed simulation of the GYG peptide," Journal of Theroretical & Computational Chemistry, 2018, 17, 03, 1840007

Barrett, R.; Gandhi, H.A.; Naganathan, A.; Daniels, D.; Zhang, Y.; Onwunaka, C.; Luehmann, A.; White. A.D., Social and Tactile Mixed Reality Increaese Student Engagement in Undergraduate Lab Activities," J. Chem. Educ., 2018, 10, 1021.

Chakraborty, M.; Xu, C.; White, A.D., "Encoding and Selecting Coarse-Grain Mapping Operators with Hierarchical Graphis," Chemical Physics, 2018, 149, 13, 10.1063.https://arxiv.org/abs/1804.04997v1

Barrett, R.; Jiang, S.; White, A.D., "Classifying anatomic and multifunctional peptides with Bayesian network models,"  Peptide Science,  2018, DOI:10,1002.    https://onlinelibrary.wiley.com/doi/abs/10.1002/pep2.24079 

Freeman, G.M.; Drennen, T.E.; White, A.D. "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, A.D.; Knight, C.; Hocky, G.M.; Voth, G.A., "Communication: Improved ab initio Molecular Dynamics by Minimally Biasing with Experimental Data," The Journal of Chemical Physics, 2017, 146:041102-5.

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.