The Guenza Lab

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The structure and dynamics of complex macromolecular fluids are characterized by an extended range of length scales and time scales in which relevant phenomena take place. Because of the limitations of simulations, reduced representations of a system are needed in order to speed up the computation to access the properties of interest. The goal of our research is the design and implementation of theoretical approaches that coarse-grain structure and dynamics of molecular liquids. Our theoretical models are based on statistical mechanics and liquid state theory, and are applied to study a number of key systems and related questions in material science and biophysics.


In complex liquids, relevant properties interest length scales from the atomistic to the macroscopic, with phenomena that involve phase separation, de-mixing, self-assembly, and more. Understanding the physics of these phenomena is important but also a real challenge: reliable coarse-grained representations are needed to address the relationship between molecular structure and multi-scale properties at the selected thermodynamic conditions.


E. Beyerle and M. G. Guenza, “Kinetic Analysis of Ubiquitin Local Fluctuations with Markov State Modeling of the LE4PD Normal Modes” The Journal of Chemical Physics, 151, 164119(1-13) (2019) DOI:10.1063/1.5123513.

M. Dinpajooh and M. G. Guenza, “Can Pure Polymer Liquids Be Represented at Two Different Resolutions Simultaneously?” The Journal of Chemical Physics Communication, 151, 061102(1-5) (2019) DOI:10.1063/1.5115791.

M. G. Guenza, M. Dinpajooh, J. McCarty, I. Y. Lyubimov “Accuracy, Transferability, and Efficiency of Coarse-Grained Models of Molecular Liquids” The Journal of Physical Chemistry B, 122(45), 10257-10278 (2018). DOI: 10.1021/acs.jpcb.8b06687. Publication Selected as Editor’s Pick and Feature Article.

M. Dinpajooh, M. G. Guenza “Coarse-graining simulation approaches for polymer melts: the effect of potential range on computational efficiency” Soft Matter, 14, 7126-7144 (2018). Cover paper.

M. Dinpajooh, and M. G. Guenza “On the Density Dependence of the Integral Equation Coarse-Graining Effective Potential” The Journal of Physical Chemistry B, 122(13), 3426-3440 (2018). DOI:10.1021/acs.jpcb.7b10494.

P. G. Romano and M. G. Guenza “GRadient Adaptive Decomposition (GRAD) Method: Optimized Refinement Along Macrostate Borders in Markov State Models” Journal of Chemical Information and Modeling 57, 2729-2740 (2017). DOI: 10.1021/acs.jcim.7b00261.

M. G. Guenza “Thermodynamically Consistent Coarse-Graining of Polymers” in “Coarse-Grained Modeling of Biomolecules” (Series in Computational Biophysics by Tylor & Francis Publisher, G. Papoian Ed. 2017) arXiv:1509.08546.

J. Copperman, M. Dinpajooh, E. Beyerle, and M. G. Guenza  “Universality and specificity in protein fluctuation dynamics” Physical Review Letters, 119, 158101 (2017).

M. Dinpajooh, M. G. Guenza “Thermodynamic Consistency in the Structure-based Integral Equation Coarse-Grained Method” Polymers, 117, 282-286 (2017).



Our research is supported in many ways by the National Science Foundation through the Chemistry Division (Theoretical Chemistry), the Division of Physics (Condensed Matter: Material Theory), and the computational effort is supported by the XSEDE program of NSF.

Our research has also been supported by the Petroleum Research Fund of the American Chemical Society and the University of Oregon.