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:

​Exploring the limits and generalizability of graph models on large surface science datasets

​Prof. Zachary Ulissi (Carnegie Mellon University)

​Abstract: Machine learning accelerated catalyst discovery efforts has seen much progress in the last few years. Datasets of computational calculations have improved, models to connect surface structure with electronic structure or adsorption energies have gotten more sophisticated, and active learning exploration strategies are becoming routine in discovery efforts. However, there are several large challenges that remain: to date, models have had trouble generalizing to new materials or reaction intermediates and applying these methods requires significant training. I will review and discuss methods in my lab for high-throughput catalyst screening and on-line discovery of interesting materials, resulting in an optimized Cu-Al catalyst for CO2-to-ethylene conversion. I will then introduce the Open Catalyst Project and the Open Catalyst 2020 dataset, a collaborative project to span surface composition, structure, and chemistry and enable a new generation of deep machine learning models for catalysis, with initial results for state-of-the-art deep graph convolutional models. Finally, I will discuss on-going work to develop small ML models to accelerate routine calculations without requiring expert intervention. 

​Speaker Bio: Zachary Ulissi is an Assistant Professor of Chemical Engineering at Carnegie Mellon University. He works on the development and application of high-throughput computational methods in catalysis, machine learning models to predict their properties, and active learning methods to guide these systems. Applications include energy materials, CO2 utilization, fuel cell development, and additive manufacturing. He has been recognized nationally for his work including the 3M Non-Tenured Faculty Award and the AIChE 35-under-35 award among others.

​Role of Free Interfaces in Contact Freezing in Water 

​Mr. Sarwar Hussain (Yale University)

​Abstract: Contact freezing is a mode of atmospheric ice
nucleation in which a collision between a dry ice nucleating particle (INP) and a water droplet results in considerably faster heterogeneous nucleation. The molecular mechanism of such an enhancement is, however, still a mystery. Recent experiments have posited this rate enhancement to be arising from the proximity between the INP and the free-interface, and have suggested a link between Contact Freezing and the ability of a free-interface to enhance homogeneous nucleation, also known as Surface Freezing. In this work, we use Molecular Dynamics (MD) simulations with jumpy forward-flux sampling to compute heterogeneous nucleation rates in INP-supported nano-films of two model water-like tetrahedral liquids, and demonstrate that the interfacial proximity between the INP and the free-interface is indeed sufficient for inducing rate enhancements, but only if the free-interface also supports Surface Freezing. Our findings therefore establish a connection between the surface freezing propensity and kinetic enhancement during contact nucleation. We also observe that proximity-induced faster nucleation proceeds through a non-classical mechanism involving the formation of hourglass-shaped nuclei that have a lower free-energy of formation as a result of the nanoscale proximity and the structural modulation of the free-interface by the INP. 

​Speaker Bio: Sarwar Hussain is a Ph.D. candidate in the Department of Chemical and Environmental Engineering at Yale University. His research combines MD simulations (with Forward-Flux sampling) and theoretical CNT-based modeling to better understand crystal nucleation in non-classical interfacial environments such as inhomogeneous interfaces (patterned-surfaces) and mixed-interfacial confinement. He also works on developing heuristics for the identification of finite-size effects in computational studies of crystal nucleation. Prior to joining Yale, he received a B.Tech. degree in Chemical Engineering from the Indian Institute of Technology (IIT), New Delhi, India.