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:
Expanding the Simulation Toolkit – Learning More from the Shape of DataProf. Aurora Clark (Washington State UniversityProf. Valeria Molinero (University of Utah)
Abstract: The complexity of simulation data is rapidly increasing as more efficient algorithms, sampling methods, and physical representations of interactions, all conspire toward realistic modeling paradigms that account for many-body effects across length and timescales. Sub-ensemble analyses that identify all local environments, methods that identify multidimensional correlation of variables, and tools that can probe organization across scale and identify the characteristic timescales of phenomena, are all needed to overcome limitations in our own chemical intuition and to provide new insight that can be feedback into fundamental theories. Within such complex data sets, advances to computational topology and graph theoretical representations of data are providing a mathematical framework to quantify the spatiotemporal patterns in simulation data. This work will demonstrate how characterizing the shape of simulation data is creating a new paradigm for understanding the multivariable and collective phenomena that underpin separations and purifications methods of highly non-ideal multicomponent solutions, as in solvent extraction. These tools are transferrable across a broad domain of applications and are becoming more accessible where the barriers to adoption are being minimized.
Speaker Bio: : Aurora Clark is a Professor in the Chemistry Department at Washington State University, an affiliate faculty in the WSU Voiland School of Chemical Engineering and Bioengineering, and a joint appointee and Laboratory Fellow at Pacific Northwest National Laboratory. Her research focuses upon the integration of applied mathematics methods and computational chemistry, where she focuses upon extracting new correlating relationships across length and timescales in complex chemical systems using tools from computational topology, graph theory, geometric measure theory, and others. As an expert on the modeling and simulation of non-ideal, multicomponent solutions and their interfaces, she has served in several advisory roles, including the 2019 NAS consensus committee on a New Era of Separations Science, and as an organizer of BES funded workshops including the 2020 “At the Tipping Point: A Future of Fused Chemical and Data Science”. She is a Fellow of the American Chemical Society and American Association for the Advancement of Science.
Spontaneous Electrokinetic Magnus Effect
Dr. Zachary Sherman (University of Texas, Austin)
ABstract: Advances in synthetic capabilities have enabled the usage of anisotropic building blocks as a handle for materials fabrication. However, despite a large repository of simulations and experiments, no governing theory exists that enables an a priori prediction of colloidal self-assembly as a function of relevant design parameters such as core shape, ligand type, or solvent conditions. Here, we present one such framework that has been validated for both entropically and enthalpically driven systems. We first present a microscopic theory for predicting assemblies resulting from entropy maximization, including those of remarkable complexity. We then explicitly consider the effect of grafting moieties employed for directed interaction between interacting particles and show how surface modifications can drastically alter assembly behaviors. Our combined thermodynamic framework is shown to predict the experimentally observed morphologies for both hard particle crystallization as well as dendrimer and DNA-mediated assemblies, providing a robust tool for use in the inverse design of novel materials.
Speaker Bio: Zach Sherman received his B.S. in Chemical Engineering from Cornell University, where he was a Merrill Presidential Scholar. He worked with Prof. Abraham Stroock investigating the thermodynamic properties of supercooled water. Zach then received his Ph.D. in Chemical Engineering from MIT, working with Prof. James Swan on the assembly and transport of colloidal dispersions in electric and magnetic fields. While at MIT, Zach received the MIT School of Engineering Graduate Student Award for Extraordinary Teaching and Mentoring and the MIT Department of Chemical Engineering Outstanding Graduate Teaching Assistant Award. He is currently a postdoctoral fellow at UT Austin, working with Prof. Thomas Truskett on the self-assembly and inverse design of functional nanoparticle materials. In 2020, Zach was awarded an Arnold O. Beckman Postdoctoral Fellowship in Chemical Sciences.