Selected Honors & Awards
Gleasen Young Eng. of the Year, runner-up, Rochester Eng. Soc., 2021
Outstanding Investigator Award (R35, MIRA), NIH, 2020
Young Investigator Award, AIChE COMSEF 2020
Institute for Pure & Applied Mathematics Senior Fellow, 2019
Curtis Award for Nontenured Faculty Teaching, 2019
NSF Career Award, 2018
Yen Fellow, Postdoctoral Fellowship at Inst. for Biophys. Dyn., 2013
Graduate Student Award, AIChE COMSEF, 2012
Best Speaker, Univ. of Wash. Chemical Engineering Symposium, 2012
Best Poster, Int'l. Congress on Marine Fouling and Corrosion, 2012
Best Poster, Gordon Research Conf.: Water and Aq. Soln., 2010
ChE 116: Numerical Methods and Stats
ChE 477: Advanced Numerical Methods: Theory to Implementation
Zhu, W.; White, A.D.; Luo, J., "Federated Learning of Molecular Properties in a Heterogeneous Setting," arXiv preprint, 2021, arXiv:2109.07258
Hocky, G.M.; White, A.D., "Natural language processing models that automate programming will transform chemistry research and teaching," arXiv preprint, 2021, arXiv:2108.13360.
Ansari, M.; Gandhi, H.A..; Foster, D.G.; White, A.D., "Iterative symbolic regression for learning transport equations," arXiv preprint , 2021, arXiv:2108.03293.
Wellawatte, G.P.; Seshadri, A.; White, A.D.,"Model agnostic generation of counterfactual explanations for molecules," 2021. doi.org/10.33774/chemrxiv-2021-4qkg8
Gandhi, H.A.; White, A.D., "City-wide modeling of vehicleto-grid economics to understand effects of battery performance," arXiv preprint, 2021, arXiv:2108.05837.
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/abs/10.1021/acs.jcim.0c00946
Gandhi, H.A.; Jakymiw, S.; Barrett, R.; Mahaseth, H.; White, A.D., "Real-Time Interactive Simulation and Visualization of Organic Molecules," Journal of Chemical Education. 2020, 97, 11, 4189-4195. DOI/abs/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/abs/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.org/10.1039/D0CP02309D
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. DOI/10.1021/jacs.7b11463
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. DOI.org/10.1142/S0219633618400072
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. DOI/10.1021/acs.jchemed.8b00212
Chakraborty, M.; Xu, C.; White, A.D., "Encoding and Selecting Coarse-Grain Mapping Operators with Hierarchical Graphics," Chemical Physics, 2018, 149, 13, 10.1063.https://arxiv.org/abs/1804.04997v1
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.