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

research	deep learning, molecular dynamics, molecular design, ar/vr
pi	@andrewwhite01 in chem eng
phd	@gandhi_heta, @GWellawatte, @ZiyueYang37, @SamCox822, @MehradAnsari, @jamestuplets, shane michtavy, wei zhu
postdoc	opening
contact	andrew.white@rochester.edu
acknowledgements	nsf che #1764415, nsf cbet #1751471, nsf iis #2029095, nsf dmr #2103553, nih #R35GM137966

🧪.🐕 | ⚗️💤 | ⚡ | 🥳⚗️ | 🥳🧮 | 📎⚗️ | 🐦🎥 | 🌶️🌊

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phd opening

2022

overview: We have openings for PhD students in Fall 2022 on all topics in our research overview. Interested candidates should apply directly to the chemical engineering, biophysics, chemistry, or materials science programs at University of Rochester. After admission, please contact Prof. Andrew White for details on rotations and projects.

rochester: University of Rochester is a private university located in Western New York state. You can find out more about the university at rochester.edu/about and living in Rochester at rochester.edu/about/community.

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postdoc opening

Deadline 2/15/2022

overview PI Andrew White at University of Rochester Chemical Engineering is looking for a postdoctoral researcher to contribute to a multi-year NIH-funded project on "Learning to learn in structural biology with deep neural networks."

what you'll do We are looking for a postdoctoral researcher who is an expert in molecular simulation, deep learning in chemistry, or computational structural biology. Under mentorship, you will develop and apply deep learning methods to molecular simulation and modeling of proteins and protein-protein interactions. Expertise in deep learning is not required, though is a strength. This is a multi-year project with a long-term goal towards advancing ML and AI methods for deep learning and meta-learning in structural biology. These methods reduce the amount of data necessary to apply supervised learning to applications where there are only tens to hundreds of data points. Examples include developing peptide drugs, antibody design, multifunctional biomaterials, and resolving intrinsically disordered protein conformations. As a postdoctoral researcher, you will mentor graduate students, lead independent projects, and collaborate in research planning. You can find examples of our recent work on our publications page. You will work with an interdisciplinary team with backgrounds in computer science, molecular simulation, and biochemistry.

requirements Candidates should have a PhD degree in chemical engineering, chemistry, physics, computer science or related area. You should have multiple peer-reviewed publications in addition to a PhD. You should have demonstrated expertise in statistical mechanics. Expertise in biochemistry and/or deep learning is a strength. Candidate should have experience in scientific software best-practices, including version control, testing, and software collaboration. A GitHub account or similar project portfolio is required.

details This position is open immediately. Starting date is negotiable, and candidates will be evaluated until the position is filled. The initial appointment will be 1 year and is renewable. Salary is set by NIH guidelines on postdoctoral research salary. All nationalities are welcome to apply and will be considered. Minority and women candidates are especially encouraged to apply. To apply, please send an email to Andrew White with the subject "Postdoc Search 2022" and describe your interest in the position and attach (1) curriculum vitae, (2) an example of a recent publication authored by you and (3) an example of a scientific software, preferably your GitHub username.

rochester University of Rochester is a private university located in Western New York state. You can find out more about the university at rochester.edu/about and living in Rochester at rochester.edu/about/community.

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papers

1. 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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2. 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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3. 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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4. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. arXiv preprint arXiv:2108.13360 (2021).
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5. 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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6. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. (2021).
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7. 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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8. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021).
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9. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. arXiv preprint arXiv:2104.09668 (2021).
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10. 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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11. 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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12. 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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13. 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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14. 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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15. 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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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. 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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18. Bonomi, M. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019).
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19. 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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20. 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).
21. Barrett, R., Jiang, S. & White, A. D. Classifying antimicrobial and multifunctional peptides with bayesian network models. Peptide Science 110, e24079 (2018).
22. 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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23. 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).
24. 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).
25. 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).
26. 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).
27. 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).
28. 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).
29. 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).
30. 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).
31. Nowinski, A. K., White, A. D., Keefe, A. J. & Jiang, S. Biologically inspired stealth peptide-capped gold nanoparticles. Langmuir 30, 1864–1870 (2014).
32. 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).
33. 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).
34. 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).
35. White, A. D., Huang, W. & Jiang, S. Role of nonspecific interactions in molecular chaperones through model-based bioinformatics. Biophysical Journal 103, 2484–2491 (2012).
36. 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).
37. 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).
38. 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).
39. 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).
40. 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).
41. 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).
42. 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).
43. 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).