Statistical mechanical theory, computer simulation and machine learning will be used to study a wide variety of phenomena in soft matter including phase equilibrium, self-assembly and aggregation in biological materials [1-2], polymers [3], colloids and other complex fluids. Theory and computer simulation efforts include developing sampling methods and coarse-grained multi-scale modeling to compare with experimental measurements. Machine learning aspects of the project include training on simulation data, conducting simulations with machine-learned potentials and obtaining models from experimental data to accelerate these objectives.

[1] "Role of Domain-Domain Interactions on the Self-association and Physical Stability of Monoclonal Antibodies: Effect of pH and Salt" A. Y. Xu, M. A. Blanco, M. M. Castellanos, C. W. Meuse, K. Mattison, I. Karageorgos, H. W. Hatch, V. K. Shen, J. E. Curtis, J. Phys. Chem. B, 127, 39, 8344-8357, 2023. https://doi.org/10.1021/acs.jpcb.3c03928

[2] "Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean" Nathan A. Mahynski, Jared M. Ragland, Stacy S. Schuur, and Vincent K. Shen, Environ. Sci. Technol., 56, 20, 14361-14374, 2022. https://doi.org/10.1021/acs.est.2c01894

[3] "pH response of sequence-controlled polyampholyte brushes" X. Yuan, H. W. Hatch, J. C. Conrad, A. B. Marciel, J. C. Palmer, Soft Matter, 19, 4333-4344, 2023. https://doi.org/10.1039/D3SM00447C

key words

Molecular simulation, Machine learning, Computational chemistry, Statistical mechanics, Neural networks, Material science

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants