papers | scholar | code | che@ur | book | class | talk

research	deep learning, molecular dynamics, molecular design, ar/vr
phd	heta gandhi <hgandhi@ur.rochester.edu>, geemi wellawatte <gwellawa@ur.rochester.edu>, ziyue yang <zyang43@ur.rochester.edu>, sam cox <swrig30@ur.rochester.edu>, mehrad ansari <mehrad.ansari@rochester.edu>, james varner <jvarner2@ur.rochester.edu>, shane smictavy <smichtav@che.rochester.edu>, wei zhu <wzhu15@ur.rochester.edu> quintina campbell <qcampbe2@ur.rochester.edu>
postdoc
pi	andrew white <andrew.white@rochester.edu>, he/him
acknowledgements	nsf cbet #1751471, nsf dge # 1922591, nsf dmr #2103553, nih #R35GM137966. previous: nsf che #1764415, nsf iis #2029095
bio	Andrew White is an associate of professor at University of Rochester. He received his PhD in chemical engineering from University of Washington and did a postdoc in chemistry at University of Chicago. White's research group studies the self-assembly and structure of biomacromolecules with coarse-grained simulation and deep learning. He and his group are leaders in the adaption of deep learning to chemistry and materials, with research on graph neural networks, explaining deep learning models, and AI-directed iterative experiments.
twitter	@andrewwhite01 @gandhi_heta, @GWellawatte, @ZiyueYang37, @SamCox822, @MehradAnsari, @jamestuplets, @MichtavyShane @weizhuatgalaxy

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papers

1. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022).
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2. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022).
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3. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022).
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4. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science advances 8, eabj0305 (2022).
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5. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. arXiv preprint arXiv:2204.00056 (2022).
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6. Cox, S. & White, A. D. Symmetric molecular dynamics. arXiv preprint arXiv:2204.01114 (2022).
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7. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022).
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8. Gandhi, H. A. & White, A. D. Explaining structure-activity relationships using locally faithful surrogate models. (2022).
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9. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. bioRxiv (2022).
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10. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021).
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11. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021).
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12. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. arXiv preprint arXiv:2108.03293 (2021).
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13. Zhu, W., White, A. & Luo, J. Federated learning of molecular properties in a heterogeneous setting. arXiv preprint arXiv:2109.07258 (2021).
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14. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. arXiv preprint arXiv:2110.03846 (2021).
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15. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020).
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16. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, 224301 (2020).
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17. 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 11, 9524–9531 (2020).
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18. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020).
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19. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020).
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20. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020).
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21. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020).
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22. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020).
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23. Amirkulova, D. B. & White, A. D. Recent advances in maximum entropy biasing techniques for molecular dynamics. Molecular Simulation 45, 1285–1294 (2019).
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24. Bonomi, M., Bussi, G., Camilloni, C., Tribello, G. A., Banáš, P., Barducci, A., Bernetti, M., Bolhuis, P. G., Bottaro, S., Branduardi, D., et al. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019).
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25. Mayes, H. B., Lee, S., White, A. D., Voth, G. A. & Swanson, J. M. Multiscale kinetic modeling reveals an ensemble of cl–/h+ exchange pathways in ClC-ec1 antiporter. Journal of the American Chemical Society 140, 1793–1804 (2018).
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26. Amirkulova, D. B. & White, A. D. Combining enhanced sampling with experiment-directed simulation of the GYG peptide. Journal of Theoretical and Computational Chemistry 17, 1840007 (2018).
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27. Chakraborty, M., Xu, C. & White, A. D. Encoding and selecting coarse-grain mapping operators with hierarchical graphs. The Journal of Chemical Physics 149, 134106 (2018).
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28. Barrett, R., Jiang, S. & White, A. D. Classifying antimicrobial and multifunctional peptides with bayesian network models. Peptide Science 110, e24079 (2018).
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29. Barrett, R., Gandhi, H. A., Naganathan, A., Daniels, D., Zhang, Y., Onwunaka, C., Luehmann, A. & White, A. D. Social and tactile mixed reality increases student engagement in undergraduate lab activities. Journal of Chemical Education 95, 1755–1762 (2018).
30. 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 146, 041102 (2017).
31. Freeman, G. M., Drennen, T. E. & White, A. D. Can parked cars and carbon taxes create a profit? The economics of vehicle-to-grid energy storage for peak reduction. Energy Policy 106, 183–190 (2017).
32. Dannenhoffer-Lafage, T., White, A. D. & Voth, G. A. A direct method for incorporating experimental data into multiscale coarse-grained models. Journal of chemical theory and computation 12, 2144–2153 (2016).
33. White, A. D., Dama, J. F. & Voth, G. A. Designing free energy surfaces that match experimental data with metadynamics. Journal of Chemical Theory and Computation 11, 2451–2460 (2015).
34. Shao, Q., White, A. D. & Jiang, S. Difference of carboxybetaine and oligo (ethylene glycol) moieties in altering hydrophobic interactions: A molecular simulation study. The Journal of Physical Chemistry B 118, 189–194 (2014).
35. Nowinski, A. K., White, A. D., Keefe, A. J. & Jiang, S. Biologically inspired stealth peptide-capped gold nanoparticles. Langmuir 30, 1864–1870 (2014).
36. Mi, L., White, A. D., Shao, Q., Setlow, P., Li, Y. & Jiang, S. Chemical insights into dodecylamine spore lethal germination. Chemical Science 5, 3320–3324 (2014).
37. White, A. D. & Voth, G. A. Efficient and minimal method to bias molecular simulations with experimental data. Journal of chemical theory and computation 10, 3023–3030 (2014).
38. White, A. D., Keefe, A. J., Nowinski, A. K., Shao, Q., Caldwell, K. & Jiang, S. Standardizing and simplifying analysis of peptide library data. Journal of chemical information and modeling 53, 493–499 (2013).
39. Brault, N. D., White, A. D., Taylor, A. D., Yu, Q. & Jiang, S. A directly functionalizable surface platform for protein arrays in undiluted human blood plasma. Analytical Chemistry 85, 1447–1453 (2013).
40. Keefe, A. J., Caldwell, K. B., Nowinski, A. K., White, A. D., Thakkar, A. & Jiang, S. Screening nonspecific interactions of peptides without background interference. Biomaterials 34, 1871–1877 (2013).
41. White, A. D., Keefe, A. J., Ella-Menye, J.-R., Nowinski, A. K., Shao, Q., Pfaendtner, J. & Jiang, S. Free energy of solvated salt bridges: A simulation and experimental study. The Journal of Physical Chemistry B 117, 7254–7259 (2013).
42. Nowinski, A. K., Sun, F., White, A. D., Keefe, A. J. & Jiang, S. Sequence, structure, and function of peptide self-assembled monolayers. Journal of the American Chemical Society 134, 6000–6005 (2012).
43. White, A. D., Nowinski, A. K., Huang, W., Keefe, A. J., Sun, F. & Jiang, S. Decoding nonspecific interactions from nature. Chemical Science 3, 3488–3494 (2012).
44. Shao, Q., He, Y., White, A. D. & Jiang, S. Different effects of zwitterion and ethylene glycol on proteins. The Journal of chemical physics 136, 06B607 (2012).
45. White, A. D., Huang, W. & Jiang, S. Role of nonspecific interactions in molecular chaperones through model-based bioinformatics. Biophysical Journal 103, 2484–2491 (2012).
46. White, A. & Jiang, S. Local and bulk hydration of zwitterionic glycine and its analogues through molecular simulations. The Journal of Physical Chemistry B 115, 660–667 (2011).
47. Shao, Q., He, Y., White, A. D. & Jiang, S. Difference in hydration between carboxybetaine and sulfobetaine. The Journal of Physical Chemistry B 114, 16625–16631 (2010).
48. Yang, W., Zhang, L., Wang, S., White, A. D. & Jiang, S. Functionalizable and ultra stable nanoparticles coated with zwitterionic poly (carboxybetaine) in undiluted blood serum. Biomaterials 30, 5617–5621 (2009).