Omar Farha is the Charles E and Emma H. Morrison Professor in Chemistry at Northwestern University. His research seeks to solve exciting problems in chemistry and materials science ranging from energy and environment related applications to challenges in national defense by employing atomically precise functional materials.  By exploiting the modular nature of metal–organic frameworks (MOFs) and porous organic polymers (POPs), we work to fundamentally understand the role of three-dimensional architecture in modifying a material’s function for applications in gas storage and separation, catalysis, water remediation and detoxification of chemical warfare agent simulants.

Chemists always strive to find new ways of manipulating atoms to create molecules and materials with the intention of improving the lives of the earth and its inhabitants. In 2001, Barry Sharpless discovered a new method of snapping stable molecules to yield new compounds and coined the term “click chemistry”. Morten Meldal separately, discovered the best snapping molecules containing azide and alkyne functionalities. Caroline Bertozzi then envisioned that the same reaction can be conducted in living cells without any interference to their normal activities and thus gave birth to bioorthogonal chemistry to investigate various stages of physiological and pathological developments in living cells.  

The present talk will explain the chemistry behind Click reaction and Bioorthogonal utility of this reaction and why this discovery deserved the most prestigious recognition in science. 

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New opportunities for chemical processing industries in so-called “upstream” and “downstream” hydrocarbon process sectors are emerging, thanks to now abundant natural gas resources.  Upstream processes refer to production of raw materials, while downstream processes refer to those closer to the end user or consumer.  Although current technology is effective in both sectors, it still relies primarily upon energy-intensive processes for key separations with large CO2 footprints. This presentation will explain why advanced polymer-derived membranes, in asymmetric hollow fiber forms, can provide significant positive changes across the separation spectrum to reduce energy intensity and carbon dioxide emissions. I will consider practical approaches to achieve such changes based on a strategy that merges fundamental science and engineering principles to introduce such membranes into large-scale processes.

Registration link here. 

Biography

Mohammad Azhar Mehfooz (Ph.D. candidate Indiana University, Bloomington) completed his B.S. in Chemistry in 2017 under the supervision of Professor Basit Yameen. His senior year project was titled “Synthesis and functionalization of Gold nanoparticles for the early detection Lung cancer biomarkers” The advisors for this project were Professor Basit Yameen and Professor Habib-ur-Rehman. He was also trained by Professor Rahman Shah Zaib Saleem and Professor Ghayoor Abbas Chotana in Summer research projects. 

He joined Indiana University Chemistry department in Fall of 2017 where he is currently a Ph.D. candidate. He has completed a major in Materials Chemistry with an Analytical Chemistry minor. He works in the research labs of Professor Jeffrey Zaleski who specializes in Enediyne Chemistry and Bergman Cyclization reactions. For his thesis, he has received training in Microbiology from our collaborators Dr. Feng Guo (Intelligent Systems Engineering, IU) and Dr. Joel Ybe (School of Public Health, IU).

At Indiana University Chemistry, he have had the opportunity to teach several undergraduate classes as an Associate Instructor, including C-103 Lab, C- 103 Discussion, C-127 Lab, C-127 Discussion, C118 Lab and P364 Discussion. During the course of graduate studies he has specialized in several analytical techniques including UV-Vis spectroscopy, Zeta Potential, FTIR, Mass Spec, Raman Spectroscopy, TEM, SEM, STM, Flowcytometry and Fluorescence microscopy.

Deep learning has begun a renaissance in chemistry and materials. We can devise and fit models to predict molecular properties in a few hours and deploy them in a web browser. We can create novel generative models that were previously PhD theses in an afternoon. In my group, we’re exploring deep learning in soft materials and molecules. We are focused on two major problems: interpretability and data scarcity. Now that we can make deep learning models to predict any molecular property ad naseum, what can we learn? I will discuss our recent efforts on interpreting deep learning models through symbolic regression and counterfactuals. Data scarcity is a common problem in chemistry: how can we learn new properties without significant expense of experiments? One method is in judicious choice of experiments, which can be done with active learning. Another approach is self-supervised learning and constraining symmetries, which both try to exploit structure in data. I will cover recent progress in these areas. Finally, one consequence of the state of deep learning is that you can just make cool things in chemistry with minimal effort. I’ll review a few fun projects, including making molecules by banging on the keyboard, doing math with emojis, and doing molecular dynamics constrained on space groups.

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Meeting ID: 936 2999 1597 

Passcode: 830219 

Scott Cushing is an Assistant Professor at Caltech. His expertise lies in fundamental research in developing new spectroscopy, such as table-top XUV / soft x-ray pulses with down-to-attosecond time resolution. His lab is developing few-femtosecond, few-photon spectroscopy with entangled photons that can probe rare and intermediate events in the excited state. His lab also uses spectroscopy to explore ultrafast light-matter interactions, entangled-classical interactions, and multi-component spectroscopy of working devices. For more information go to cushinglab.caltech.edu Before joining Caltech, Dr. Cushing was a post-doctoral researcher at UC Berkeley. He holds a Ph.D. from West Virginia University.

Sijia Dong is an assistant professor in the Department of Chemistry and Chemical Biology at Northeastern University. Sijia is passionate about accelerating science using computation and automation. She received her Ph.D. in Chemistry from the California Institute of Technology in 2017, advised by Prof. William A. Goddard III, with whom and Dr. Ravinder Abrol she developed a first-principles-based and data-driven computational method to predict the structures of proteins that are crucial drug targets for many diseases. She carried out her postdoctoral research at the University of Minnesota with Prof. Donald G. Truhlar and Prof. Laura Gagliardi, and then at Argonne National Laboratory with Prof. Giulia Galli. Her postdoctoral work was to use and develop quantum chemical methods and workflows to study the photochemistry of molecules and materials in light-harvesting systems and to use machine learning to accelerate quantum chemical methods. Research in the Dong Lab focuses on developing and applying physics-based and data-driven computational methods to understand multiscale processes, from electronic structures to emergent properties, and to design molecules, materials, and processes for renewable energy, biomedicine, and beyond.

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Meeting ID: 942 4035 1473 

Password: 303240

Dr. Brenda M. Rubenstein is a Joukowsky Family Professor of Chemistry at Brown University. She completed her Ph.D. from Columbia University and held postdoctoral positions at Lawrence Livermore before joining Brown University. The Rubenstein Group is a theoretical chemistry group that applies analytical and computational tools to explore a variety of problems in quantum chemistry, quantum physics, and biophysics. They currently focus on developing new methodologies for studying problems in quantum and statistical mechanics, molecular and quantum computing, and biophysical simulation.

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Meeting ID: 996 6445 3011

Passcode: 525657

Heather J. Kulik is an American computational materials scientist and chemist who is an associate professor at the Massachusetts Institute of Technology. Her research considers the computational design of new materials and the use of artificial intelligence to predict material properties. She received her B.E. in Chemical Engineering from Cooper Union in 2004 and her Ph.D. in Materials Science and Engineering from MIT in 2009. She completed postdocs at Lawrence Livermore (2010) and Stanford (2010−2013), prior to returning to MIT as a faculty member in 2013 and receiving tenure in 2021. In this talk, she will give us a broad overview of her research and some interesting developments in the field.

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Meeting ID: 947 6889 8894

Passcode: 247347