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:
Weighted ensemble simulations of long-timescale dynamics: From chemical reactions to SARS-CoV-2
Prof. Lillian Chong (University of Pittsburgh)
Abstract: The weighted ensemble (WE) path sampling strategy orchestrates multiple simulations in parallel with rigorous statistical resampling at fixed time intervals to maintain rigorous kinetics. WE simulations can be orders of magnitude more efficient than standard simulations in generating unbiased, atomically detailed pathways for rare events such as protein binding/unbinding and protein folding processes. The WE strategy can be applied at any scale with any type of stochastic dynamics engine – from ab initio simulations to cell-scale simulations and beyond. I will present our recent applications of the WE strategy as well as challenges that remain in tackling long-timescale kinetics.
Speaker Bio: Lillian Chong, Ph.D. is an associate professor at the University of Pittsburgh where her research involves the development and application of molecular simulation approaches to model a variety of biophysical processes. Dr. Chong has pioneered work on enhanced sampling methods, including the weighted ensemble method for simulating rare events such as protein binding, protein switching and chemical reactions. In addition, she has made advances in force fields, specifically the Amber ff15ipq protein force field, including an expansion of this force field (ff15ipq-m) released in 2020 for modeling protein mimetics. Dr. Chong has also developed robust scalable software tools for large-scale simulations, including Weighted Ensemble Simulation Toolkit with Parallelization and Analysis (WESTPA).
Chong’s honors and awards include the National Science Foundation CAREER Award, Carnegie Science Emerging Female Scientist Award, Hewlett-Packard Outstanding Junior Faculty Award, Frank M. Goyan Graduate Research Award in Physical Chemistry at UCSF, Burroughs Wellcome Graduate Research Fellowship and National Science Foundation Graduate Research Fellowship.
Dr. Chong earned a bachelor of science degree in chemistry at the Massachusetts Institute of Technology in Cambridge, Mass., a doctor of philosophy degree in biophysics from the University of California in San Francisco and completed postdoctoral work at Stanford University in Stanford, Calif. and IBM Almaden Research Center in San Jose, Calif.
Predictive theoretical framework for dynamic control of bio-inspired hybrid nanoparticle self-assembly
Dr. Xi Qi (University of Washington)
Abstract: Nature-inspired hybrid organic-inorganic systems are promising materials for hierarchical structures with at-will tailoring of configurations and properties. An engineered homobifunctional silica-binding protein framework, sfGFP::Car9-Car9, has been shown to induce cyclic and reversible assembly/disassembly of silica nanoparticles between pH 7.5 and 8.5. Yet, a detailed understanding of the interplay of physiochemical interactions over a wide range of scales is still lacking, thus hindering further advancements towards a more systematic and precise control. To unravel the underlying physics, we develop a multi-scale theoretical framework where molecular and macroscopic interactions are obtained from colloidal theory and atomistic molecular dynamic simulations and integrated into a coarse-grained rigid-body model. Our predictive coarse-grained model has successfully captured the pH-dependent reversible assembly and agrees well with ultra-small angle x-ray scattering experiments at collective scales. Our work highlights the connection between the collective outcomes and the detailed interactions that contain information on synthesis conditions, and provides a path to achieve programmable control through reverse design.
Speaker Bio: Dr. Xin Qi is a postdoc researcher in Jim Pfaendtner’s group in the Chemical Engineering department at the University of Washington. She completed her Ph.D. degree in Chemical Engineering at the Pennsylvania State University, advised by Professor Kristen Fichthorn. Her Ph.D. projects focused on using atomistic molecular dynamic simulations and theories to investigate the growth mechanisms in shape-controlled metal nanocrystal syntheses. She currently uses enhanced sampling methods, colloidal theory, and high-throughput simulation algorithms to probe hybrid organic-inorganic nanoparticle assembly at the colloidal level and achieve the simulation-guided design of functional proteins in hierarchical nanomaterial fabrication