Statistical Thermodynamics & Molecular Simulations (STMS) Seminar Series: Prof. Scott Shell (UCSB) and Dr. Eric Beyerle (Maryland)
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. Since early 2020, the coronavirus pandemic has disrupted many large in-person scientific gatherings, including conferences and department seminars. 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 can either be nominated by their advisors or can self-nominate themselves by sending a CV to the organizers. During 2022 we expect to hold two seminar per month, and the events will take place in the last two Fridays of each month, from 10:45 AM-12:00 PM Eastern Standard Time (EST):
This event's talks:
Water structure and the design of water-mediated solute-surface interactions
Prof. M. Scott Shell (University of California, Santa Barbara)
Abstract: Water-mediated interactions constitute fundamental driving forces in a profound range of synthetic and natural materials. Decades of work has thought about how these interactions are influenced, or even controlled, by the manner in which water structures and dynamically responds near solutes and surfaces. Here, we use molecular simulations and theory to address a related but distinct question: how can solutes and surfaces be engineered to program water structure in predictable ways that manipulate the functional behavior of materials? We analyze a wide range of simulated systems and use statistical feature selection to identify metrics for water structure that are predictive of functional properties, and to quantify how many measures of water structure are independent and relevant. We then develop an optimization workflow coupling molecular simulations to machine-learning algorithms that systematically designs complex aqueous interfaces to manipulate water structure and effect new material properties, such as solute binding and selectivity in transport through porous materials. These computational efforts identify novel ways to leverage water’s distinct responses to hydrophobic, hydrophilic, and charged surface groups to chemically organize high-performing surfaces and suggest new synthetic targets for experimental materials design.
Speaker Bio: Prof. M. Scott Shell is the Myers Founders Chair Professor and Graduate Vice Chair of Chemical Engineering at the University of California Santa Barbara. He earned his B.S. in Chemical Engineering at Carnegie Mellon in 2000 and his Ph.D. in Chemical Engineering from Princeton in 2005, followed by a postdoc in the Department of Pharmaceutical Chemistry at UC San Francisco from 2005-07. Prof. Shell’s group develops novel molecular simulation, multiscale modeling, and statistical thermodynamic approaches to address problems in contemporary soft condensed matter and biophysics. Recent areas of interest include protein self-assembly and aggregation, water structure and water-mediated interactions, membrane design, and complex polymer formulations. He is the recipient of a Dreyfus Foundation New Faculty Award (2007), an NSF CAREER Award (2009), a Hellman Family Faculty Fellowship (2010), a Northrop-Grumman Teaching Award (2011), a Sloan Research Fellowship (2012), a UCSB Academic Senate Distinguished Teaching Award (2014), the CoMSEF Impact Award from AIChE (2017), and a UCSB Academic Senate Graduate Mentor Award (2022).
Learning thermodynamics for rare events in biology
Dr. Eric Beyerle (University of Maryland, College Park)
Abstract: Learning good reaction coordinates (RCs) to describe rare events at the atomistic level is difficult due to the presence of large free-energy barriers and the subsequent lack of sampling on both sides of the barrier. Furthermore, the relative thermodynamic contributions from the energy and entropy to the free-energy barrier along these RCs are difficult to determine. These challenges can be tackled simultaneously by a judicious combination of enhanced sampling molecular dynamics (MD) and appropriate machine learning (ML) framework. In this talk, I will describe how to train a specific ML model using inputs from atomistic MD simulations biased using well-tempered metadynamics to learn RCs for two systems: benzoic acid permeation through a lipid bilayer and hydrophobic ligand dissociation. By decomposing the free-energy profiles along the learned RCs, we discover that the processes of benzoic acid entry into the bilayer and hydrophobic ligand dissociation are both dictated by large entropy barriers and that solute hydration plays a major role at the transition state of hydrophobic ligand unbinding.
Speaker Bio: I’m from Louisville, KY and graduated from Centre College in 2015 with a B.S. in chemical physics. From 2015 to 2021, I was a graduate in the chemistry department at the University of Oregon, where I was advised by Prof. Marina Guenza. I received my PhD in chemistry in September 2021, after which I joined Prof. Pratyush Tiwary’s group as a postdoctoral associate in the Institute for Physical Science and Technology at the University of Maryland, College Park. I was recently awarded one of the Spring 2024Wiley Computers in Chemistry outstanding postdoc award from the ACS COMP division, and I am currently applying for assistant professor faculty positions in chemistry.