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Andrew D. White

  • Associate Professor of Chemical Engineering

PhD, University of Washington, 2013

Office Location
4401 Wegmans Hall
(585) 276-7395
(585) 273-1348
Web Address

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

Recent Publications

Complete Publication List

White, A.D., "The future of chemistry is language," Nature Reviews Chemistry, 2023. DOI: 10.1038/s41570-023-00502-0

Ansari, M,; White, A.D., "Serverless Prediction of Peptide Properties with Recurrent Neural Networks," Journal of Chemical Information & Modeling, 2023, 63, 8, 2546-2553. DOI: 10.1021/acs.jcim.2c01317

Wellawatte, G.P.; Gandhi, H.A.; Seshadri, A.; White, A.D., "A Perspective on Explanations of Molecular Prediction Models," Journal of Chemical Theory and Computation, 2023, DOI: 10.1021/acs.jctc.2c01235.

Ansari, M.; Soriano-Panos, D.; Ghoshal, G.; White, A.D., "Inferring spatial source of disease outbreaks using maximum entropy," Physical Review E, 2022, 106, 1. DOI: 10.1103/PhysRevE.106.014306

Zhu, W.; Luo, J.; White, A.D., "Federated learning of molecular properties with graph neural networks in a heterogeneous setting," Patterns, 2022, 3, 6. DOI: 10.1016/j.patter.2022.100521

Cox, S.; White, A.D., "Symmetric Molecular Dynamics," Journal of Chemical Theory & Computation2022. DOI: 10.1021/acs.jctc.2c00401.

Barrett, R.; Ansari, M.; Ghoshal, G.; White, A.D., "Simulation-based inference with approximately correct parameters via maximum entropy," Machine Learning-Science & Technology,  2022, 3, 2, 025006. DOI: 10.1088/2632-2153/ac6286.

Ansari, M.; Gandhi, H.A.; Foster, D.G.;  White, A.D., "Iterative Symbolic Regression for Learning Transport Equations," AIChE Journal, 2022, e17695. DOI 10.1002/aic.17695

Wellawatte, G.P.; Seshadri, A.; White, A.D., "Model Agnostic Generation of Counterfactual Explanations for Molecules," Chemical Science, 2022, Early Access. DOI 10.1039/d1sc05259d

Hamsici, S.; White, A.D.; Acar, H., "Peptide Framework for Screening the Effects of Amino Acids on Assembly," Science Advances, 2022, 8, 3, eabj0305. DOI: 10.1126/sciadv.abj0305

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, 2021arXiv: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,"

Gandhi, H.A.; White, A.D., "City-wide modeling of vehicleto-grid economics to understand effects of battery performance," arXiv preprint, 2021

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/abs/10.1021/acs.jcim.0c009461.

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/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.

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. 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.

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.

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.

Research Interests

  • AI Directive Design of Materials
  • Molecular Simulations
  • Augmented-Reality in Chemical Engineering Education
  • Grid-Energy Storage Simulations