Statistical Thermodynamics & Molecular Simulations (STMS) Seminar Series

​These seminar series are aimed at providing a virtual platform for sharing scientific research in the area of statistical mechanics, molecular simulations, and computational materials science. In recent months, the coronavirus pandemic has stopped all large in-person scientific gatherings, including conferences and department seminars, and it is not clear that the situation will improve any time soon. STMS is aimed at filling this gap, and provide a venue for dissemination of research findings and exchange of ideas in the age of COVID.  This model is being currently used by several other scientific communities, and can potentially continue even beyond the pandemic if successful. 

​Each seminar will be a 60-minute event and will comprise of a long-form (30-minute) talk by a principal investigator or a senior research scientists from academia or industry and a short-form (15-minute) presentation by a graduate student or a postdoc. The remainder of the event will be dedicated to Q&A (10 minutes for the PI, 5 minutes for the student/postdoc). Long-form speakers will be chosen by the STMS Organizing Committee, while we encourage suggestions from the community at large. Student and postdoctoral speakers, however, need to be nominated by their advisors.  Seminars will take place on Fridays, from 11 AM-12 PM. During 2021, we expect to hold two seminar per month, at the last two Fridays of each month.This event's talks:

​Fluctuations and Hydrophobic Effects

​Prof. Hank Ashbaugh (Tulane University)

​Abstract: The adage “oil and water do not mix” provides a high-level description of the insolubility on non-polar species in aqueous solution. Thermodynamic analysis of the dissolution of non-polar species in water reveals not only a large positive free energy of hydration (hence low solubility), but that hydration at room temperature is favored by a significant negative enthalpy and opposed by an even stronger negative entropy. These thermodynamic properties are also significantly temperature dependent as marked by a large positive heat capacity increment. Compared to most other solvents, these thermodynamic properties are distinct and comprise what are thought of as signatures of “hydrophobic hydration.” Given the importance of hydrophobic hydration to many assembly processes in water it has been a subject of intense scrutiny. Early theoretical descriptions implicated the formation of ice-like patches about non-polar species as a result of their inability to hydrogen-bond with water as the origin of hydrophobic hydration. More recent descriptions, like scaled-particle and information theories, have enjoyed success examining the process of forming empty voids in water to gain a molecular-level view. Here we reexamine the information theory formulation of non-polar solute hydration. Analysis of the Gaussian fluctuation of water occupation states within a solute-sized observation volume, we derive an analytical theoretical description that captures accurate describe Gaussian occupation fluctuations over all potential solute sizes. This description of solvent fluctuations is subsequently shown to quantitatively predict the signatures of hydrophobic hydration for solutes up to the size of krypton utilizing information only on the equation-of-state of water and its effective diameter. Armed with this new description of hydrophobic hydration we consider dissolution of gases in water in the deeply super-cooled regime where previous simulations predictions have found the heat capacity of hydrophobic hydration could change its sign and cold-denatured proteins may potentially refold. We demonstrate Interpolated Gaussian Fluctuation Theory (IGFT) accurately captures the reversal in the temperature dependence of hydrophobic hydration in supercooled water. IGFT ascribes this reversal to the occurrence of a maximum in water’s compressibility, which itself can be traced to the proposed second critical point in supercooled water. The success of IGFT suggests that beyond water’s unique equation-of-state and size, solute induced structuring is unnecessary to predict the curious properties of hydrophobic hydration. 

​Speaker Bio: Hank Ashbaugh is a Professor of Chemical and Biomolecular Engineering at Tulane University. He obtained a Bachelor’s Degree in chemical engineering from North Carolina State University in 1992, and a Ph.D. in chemical engineering from the University of Delaware in 1998. Following post-doctoral study at Lund University, Princeton University, and Los Alamos National Laboratory, he began his independent research position at Tulane University in 2004. His research focuses on the simulation and theory of hydration phenomena and self-assembly processes. 

​Faithfully Transferring Thermodynamic Information Across Scales: Integrating Particle and Field Theoretic Approaches to Soft Matter Simulation

​Dr. Kevin Shen (University of California, Santa Barbara)

​ABstract: Accurate de-novo modeling of soft matter formulations is a challenging multiscale modeling problem. Resolving self-assembled structures in formulations requires well-equilibrated, large scale simulations, but the sensitivity of self-assembly to chemical details necessitates high-resolution, chemically accurate approaches. We present a new strategy to tackle this problem by combining the strengths of coarse-grained field theoretic and higher-resolution, particle-based, all-atom simulations.

​Existing bottom-up coarse-graining frameworks offer conceptually rigorous ways to parameterize lower-resolution models from higher-resolution ones. However, these frameworks often poorly reproduce thermodynamic properties. This begs the question, what kind of information is being lost by bottom-up coarse graining, and how can it be recovered? We propose that the coarse-graining ensemble plays a critical role in the thermodynamic faithfulness of resulting coarse-grained models. The quality of a coarse-grained ensemble can be formalized using the Fisher information metric, leading to the notion of "informative ensembles". We demonstrate that ensemble informativeness is intimately tied to the sampling of physical conjugates of thermodynamic variables, can be enhanced through applied potentials, and optimized using the Fisher information metric. Using this approach, we show that the chemical detail embodied in all atom simulations can be efficiently transferred to coarse-grained models and quantitatively reproduce activity coefficients.

​Finally, for suitably coarse-grained models, we exploit exact mathematical transformations to obtain statistical field theories, which are able to rapidly equilibrate large molecules and study self-assembly, but traditionally lack predictive power due to the challenge of obtaining emergent (e.g. chi) parameters. Taken as a whole, we are able to de novo parameterize field theories from all-atom models and combine the strengths of both methods. Using sodium dodecyl sulfate as a model formulation component, we make de-novo predictions of self-assembled structures.

​Speaker Bio: Kevin Shen received his B.S. in Chemical Engineering from Yale University, where he worked with Prof. Michael Loewenberg on droplet self assembly dynamics. Following graduation, Kevin worked at the Academia Sinica in Taiwan with Prof. Yeng-Long Chen where Kevin discovered his interest in polymers and thermodynamics. Subsequently, Kevin obtained his Ph.D. in Chemical Engineering from Caltech, working with Prof. Zhen-Gang Wang on theories of polyelectrolyte self-assembly. At Caltech, Kevin was recognized with Outstanding Graduate Teaching Assistant Awards twice and also helped secure a Union of Concerned Scientists grant for funding science policy education at Caltech. Currently, Kevin is a postdoctoral fellow at UCSB working on an industry collaboration between BASF and Profs. Glenn Fredrickson and Scott Shell on integrated multiscale simulations for formulation design.