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: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