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Andrew D. White

  • Associate Professor of Chemical Engineering

PhD, University of Washington, 2013

4001 Wegmans Hall
(585) 276-7395
Fax: (585) 273-1348


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


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

Recent Publications

Complete Publication List

Yang, Z.; Chakraborty, M.; White, A.D., "Predicting Chemical Shifts with Graph Neural Networks," Chemical Science, 2021, Early Release.  DOI 10.1039/d1sc01895g 

Barrett, R.; White, A.D., "Investigating Active Learning and Meta-Learning for Iterative Peptide Design," Journal of Chemical Information & Modeling, 2021, 61, 1, 95-105. DOI: 10.1021/acs.jcim.0c00946   

Amirkulova, D.B.; White, A.D., "Combining Enhanced Samples with Experiment-Directed Simulation of the GYG Peptide (vol17, 184007,2018)," Journal of Theoretical & Computational Chemistry, 2020, 19, 8, 2092002. DOI: 10.1142/S0219633620920029 

Gandhi, H.A.; Jakymiw, S.; Barrett, R.; Mahaseth, H.; White, A.D., "Real-Time Interactive Simulation and Visualization of Organic Molecules," Journal of Chemical Education2020, 97, 11, 4189-4195.  DOI: 10.1021/acs.jchemed.9b01161 

Amirkulova, D.B.; Chakraborty, M.;White, A.D., "Experimentally Consistent Simulation of A beta(21-30) Peptides with a Minimal NMR Bias, Journal of Physical Chemistry B, 2020, 124, 38, 8266-8277. DOI: 10.1021/acs.jpcb.0c07129 

Li, Z.; Wellawatte, G.P.; Chakraborty, M.; Gandhi, H.A.; Xu, C.; White, A.D., "Graph Neural Network Based Coarse-Grained Mapping Prediction," Chemical Science, 2020, 11, 35, 9524-9531. DOI: 10.1039/d0sc02458a  

Chakraborty, M.; Xu, J.; White, A.D., " Is Preservation of Symmetry Necessary for Coarse-Graining?" Physical Chemistry Chemical Physics, 2020, 22, 26, 14998-15005. DOI: 10.1039/d0cp02309d

Chakraborty, M; Ziatdinov, M.; Dyck, O.; Jesse, S. White, A.D.; Kalinin, S.V., "Reconstruction of the Interatomic Forces from Dynamic Scanning Transmission Electron Microscopy Data," Journal Applied Physics, 2020, 127, 22, 224301. DOI:10.1063/5.0009413

Amirkulova, D.B.; White, A.D., "Recent Advances in Maximum Entropy Biasing Techniques for Molecular Dynamics," Molecular Simulation, 2019, 45, 14-15. DOI:10.1080/08927022.2019.1608988.

Barrett, R.; Jiang, S.; White, A.D., "Classifying Antimocrobial and Multifunctional Peptides with Bayesina Network Models," Peptide Science, 2018, 110, 4, e24079.  DOI:10.1002/pep2.24079

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 Theoretical & 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 Increase 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 Graphics," Chemical Physics, 2018, 149, 13, 10.1063.

Barrett, R.; Jiang, S.; White, A.D., "Classifying anatomic and multifunctional peptides with Bayesian network models,"  Peptide Science,  2018, DOI:10,1002. 

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.