BEGIN:VCALENDAR VERSION:2.0 PRODID:-//chikkutakku.com//RDFCal 1.0//EN X-WR-CALDESC:GoogleカレンダーやiCalendar形式情報を共有シェ アしましょう。近所のイベントから全国のイベントま で今日のイベント検索やスケジュールを決めるならち っくたっく X-WR-CALNAME:ちっくたっく X-WR-TIMEZONE:UTC BEGIN:VEVENT SUMMARY:March Break DTSTART;VALUE=DATE:20260316 DTEND;VALUE=DATE:20260321 UID:339339313574 DESCRIPTION:March Break LOCATION: END:VEVENT BEGIN:VEVENT SUMMARY:Registration Advising Begins DTSTART;VALUE=DATE:20260323 DTEND;VALUE=DATE:20260324 UID:305618367302 DESCRIPTION:Registration Advising Begins LOCATION: END:VEVENT BEGIN:VEVENT SUMMARY:ChE Seminar: Elizabeth Biddinger\, CUNY\, “Investigating Elec tro-organic Reactions for Sustainable Applications” DTSTART;VALUE=DATE-TIME:20260331T153000Z DTEND;VALUE=DATE-TIME:20260331T163000Z UID:849647127039 DESCRIPTION:Host:  Zhu ChenAbstractAs the renewable electricity contribut ion to the grid continues to grow and electricity prices in some areas con tinue to drop\, electrochemically synthesizing chemicals becomes favorable . Electrochemical synthesis methods offer opportunities to perform reactio ns under benign reaction conditions (at or near room temperature and press ure)\, use less harmful or waste-generating reaction steps\, and perform s elective reactions. In electroreduction reactions\, externally-suppl ied hydrogen gas that is generally needed for reduction is not requi red.  Rather\, electrons\, frequently paired with the electrolyte\, are the reducing agents. Powerful oxidizing agents are also replaced with elec trons when using electrooxidation reactions. Additionally\, electrooxidati on reactions can be paired with the hydrogen evolution reaction to generat e two valuable products. This presentation will provide perspectives rangi ng from carbon footprint analyses of electroorganic reactions to the unrav eling of reaction mechanisms and kinetics. Electrochemical hydrogenation a nd hydrogenolysis (ECH) will be presented as an example of replacing a traditional heterogeneously-catalyzed synthesis with an electrochemical s ynthesis. The specific ECH reaction that will be illustrated is furfural\, a biomass-derived species that is commercially produced today on the scal e of ~400\,000 tons/year. The desired products are furfuryl alcohol an d 2-methyl furan\, a resin intermediate and a fuel alternative\, respectiv ely. By tuning the reaction conditions\, the desired products can be forme d and the undesired products minimized. Another example to be included is that of electrochemical cycling of liquid organic hydrogen carriers (LOHCs ). The conditions in which the LOHC electrochemical cycling are competitiv e with classic thermochemical cycling will be presented and example reacti ons probing key selectivity challenges will be examined.    BioElizabet h J. Biddinger is a Professor of Chemical Engineering at The City College of New York\, the Deputy Director of the Center for Decarbonizing Chemical Manufacturing Using Sustainable Electrification (DC-MUSE) and an Associat e Editor for ACS Sustainable Chemistry &\; Engineering. Her research in terests are in electrochemical reaction engineering for green chemistry an d energy. In particular\, she is interested in the electrification of ch emical processes that transform wastes or renewable resources into valuabl e materials\, chemicals and fuels for sustainability\, and in alternative electrolytes for battery safety and performance. Professor Biddinger has b een recognized with the 2022 Ohio State College of Engineering Texnikoi Al umni Award\, 2018 US Department of Energy Early Career Award\, 2016-2017 E lectrochemical Society - Toyota Young Investigator Fellowship\, and 2014 C UNY Junior Faculty Research Award in Science and Engineering (J-FRASE) spo nsored by the Sloan Foundation. Professor Biddinger has held several leade rship roles with professional organizations including The Electrochemical Society (ECS) Industrial Electrochemistry &\; Electrochemical Engineeri ng (IE&\;EE) Division Vice Chair\, Secretary/Treasurer and Student & \; Early Career Awards Chair\, and an American Institute of Chemical Engin eers (AIChE) Catalysis and Reaction Engineering Division Director. She als o spent her sabbatical in the Chemical Process Development group at Bristo l-Myers Squibb in 2023. Prior to joining City College in August 2012\, Pro f. Biddinger was a Post-doctoral Fellow at the Georgia Institute of Techno logy.  She received her PhD in 2010 in Chemical Engineering from The Ohi o State University (Columbus\, OH) and her BS in 2005 in Chemical Engineer ing from Ohio University (Athens\, OH). LOCATION:LGRT 201 END:VEVENT BEGIN:VEVENT SUMMARY:Registration Advising Ends DTSTART;VALUE=DATE:20260403 DTEND;VALUE=DATE:20260404 UID:868335116633 DESCRIPTION:Registration Advising Ends LOCATION: END:VEVENT BEGIN:VEVENT SUMMARY:ChE Seminar: John Kitchin\, Carnegie Mellon University\, "Large ma chine-learned potentials for materials design with catalysis and energy ap plications" DTSTART;VALUE=DATE-TIME:20260407T153000Z DTEND;VALUE=DATE-TIME:20260407T163000Z UID:148344059178 DESCRIPTION: Sponsored by the Department of Chemical and Biomolecular Eng ineeringHost: AlexandraZagalskayaazagalskaya@umass.edu 413-545-7114John Ki tchin Carnegie Mellon University “Large machine-learnedpotentials for m aterials design with catalysis and energy applications” Tuesday\, April 7\, 2026\, 11:30 a.m.201 LGRT\, UMass Amherst(Refreshments at 11:15 a.m.)  Abstract:Designing multicomponent materials ischallenging with density f unctional theory (DFT) because it computationallyexpensive to evaluate all the ways that elements may combine and react. It isalso difficult to esti mate free energy contributions to reactions and to locatereaction barriers with DFT. The Open Catalyst Project is developing machinelearned potentia ls (MLP) to mitigate these challenges. These MLPs are trainedon 100M+ DFT calculations spanning 55 different elements and 80+ adsorbatesthat are rel evant in catalysis and energy applications. Nominally these modelswere tra ined to predict energy and forces\, and from these one can derivereaction energies. We will show in this talk\, however\, that these models alsoshow great utility in computing reaction barriers\, and in estimating freeener gy contributions to reactions. This opens the door to a post-scaling era o fcomputational catalysis where reaction barriers can be computed in comple xreaction networks with near DFT accuracy rather than relying on less accu ratelinear scaling relations. We will show some case studies of results an d discussfuture research directions in this area. Finally\, I will discuss the growingrole of generative AI in scientific research with examples fro m our most recentwork. Bio: John Kitchin completed his B.S. in Chemistry at North Carolina State University. He completed a M.S. in Materials Scien ceand a PhD in Chemical Engineering at the University of Delaware in 2004 underthe advisement of Dr. Jingguang Chen and Dr. Mark Barteau. He receive d anAlexander von Humboldt postdoctoral fellowship and lived in Berlin\, G ermany for1 ½ years studying alloy segregation with Karsten Reuter and Ma tthias Schefflerin the Theory Department at the Fritz Haber Institut. Prof essor Kitchin began atenure-track faculty position in the Chemical Enginee ring Department atCarnegie Mellon University in January of 2006. He is cur rently the John E.Swearingen Professor. At CMU\, Professor Kitchin works i n the areas of alloycatalysis and molecular simulation. He was awarded a D OE Early Career award in2010 to investigate multifunctional oxide electroc atalysts for the oxygenevolution reaction in water splitting using experim ental and computationalmethods. He received a Presidential Early Career Aw ard for Scientists andEngineers in 2011. He completed a sabbatical in the Accelerated Science groupat Google learning to apply machine learning to s cientific and engineeringproblems in 2018. LOCATION:LGRT 201 END:VEVENT BEGIN:VEVENT SUMMARY:ChE ExxonMobil Lecture: Ed Maginn\, Notre Dame\, "Keeping it Cool: Leveraging Molecular Simulation and Data-Driven Methods for Next-Generat ion Refrigeration" DTSTART;VALUE=DATE-TIME:20260414T153000Z DTEND;VALUE=DATE-TIME:20260414T163000Z UID:246611100728 DESCRIPTION:Sponsoredby the Department of Chemical and Biomolecular Engine eringHosts: Peng Bai and Clark Chenpengbai@umass.edu and zhuchen@umass.edu 413-545-6189 and 413-545-6145 Edward Maginn University of Notre Dame “ Keeping itCool: Leveraging Molecular Simulation and Data-Driven Methods fo rNext-Generation Refrigeration” Tuesday\, April 14\, 2026\, 11:30 a.m.2 01 LGRT\, UMass Amherst(Refreshmentsat 11:15 a.m.) Abstract:Most refriger ants used today arehydrofluorocarbons (HFCs)\, potent greenhouse gases wit h global-warmingpotentials 2000–4000 times that of CO2. Their environmen tal impactis compounded by high leak rates—nearly 90% of refrigerants ev entually escapeinto the atmosphere—and the massive energy demand of HVAC R systems\, whichaccount for up to 40% of U.S. building electricity usage. Consequently\, theU.S. and international partners are phasing down HFCs u nder agreements like theKigali Amendment and the AIM Act. This creates a t remendous societal challengeto responsibly replace billions of kilograms o f incumbent refrigerants. The NSFERC project EARTH was formed to address t his by developing strategies for bothrepurposing existing refrigerants and discovering sustainable alternatives. In this talk\, I will discuss our effortsto address this challenge by integrating molecular simulations with machinelearning methods. We have used data science methods to develop hig hly accurateintermolecular potentials for a wide class of HFCs\, enabling the prediction ofessential thermophysical properties for refrigerants and their complexmixtures. We leverage these tools to discover new solvents ca pable ofseparating azeotropic mixtures of existing HFCs\, a key step for r ecycling. Weuse active learning to minimize both computational cost and ex perimental time.Finally\, moving beyond existing fluids\, we have combined group contributionapproaches with Gaussian process regression to develop rapid screening methodsfor millions of potential replacements for high-GWP HFCs. These approachesdemonstrate how integrating machine learning with f undamental physics-basedsimulations leads to faster property predictions a nd new design principles forsustainable materials.    Bio: Edward Mag inn is the Keough-Hesburgh Professor in theDepartment of Chemical and Biom olecular Engineering at the University of NotreDame. He also serves as Not re Dame’s Associate Vice President of Research. Hisresearch group develo ps and applies advanced molecular simulation methods tostudy the structure and thermophysical properties of fluids. Maginn was a pioneer in the use ofmolecular simulations to investigate ionic liquids and holds nine patent s inthe field. He has over 270 peer-reviewed publications and has written 10 bookchapters. He is a Fellow of the American Institute of Chemical Engi neers\, theAmerican Association for the Advancement of Science\, and the N ational Academyof Inventors. He is a Trustee and Executive Director of the non-profit CACHECorporation\, which promotes the use of computational met hods in chemicalengineering. He has BS in chemical engineering fromIowa S tate University and a PhD in chemical engineering from the University ofCa lifornia\, Berkeley. He worked for Procter and Gamble from 1987-1990 and h asbeen on the Notre Dame faculty since 1995.   LOCATION:LGRT 201 END:VEVENT END:VCALENDAR