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BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.18.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Penn Engineering Events X-ORIGINAL-URL:https://seasevents.nmsdev7.com X-WR-CALDESC:Events for Penn Engineering Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20201120T140000 DTEND;TZID=America/New_York:20201120T150000 DTSTAMP:20201110T132115Z CREATED:20201110T132115Z LAST-MODIFIED:20201110T132115Z UID:1478-1605880800-1605884400@seasevents.nmsdev7.com SUMMARY:PICS Colloquium: "Designing energy conversion materials with ab-initio and active machine learning computations of electron-phonon and ion dynamics" DESCRIPTION:Abstract: Accurate atomistic computations of transport and reaction dynamics are an important challenge and an opportunity for designing materials for energy conversion and storage. In the context of thermoelectric materials\, we develop new automatable computational methods for describing electron-phonon scattering dynamics. By predicting electrical transport properties\, we computationally discovered several new low-cost thermoelectric alloys with record device performance. In the context of solid-state batteries\, computations of ionic transport reveal how strong ionic interactions lead to disorder and surprising collective phenomena in amorphous polymer electrolyte materials and enable us to design new electrolyte chemistries. \nHigh-fidelity ab-initio simulations of atomistic dynamics are limited to small systems and short times\, and development of surrogate machine learning models for force fields is an emerging promising direction to access long-time large-scale dynamics of complex materials systems. However\, the main challenges are high accuracy\, reliability\, and computational efficiency of these models\, which critically depend on the training data sets. We develop ML interatomic potential models that are interpretable and uncertainty-aware\, and orders of magnitude faster than reference quantum methods. Principled uncertainty quantification built into these models enables the construction of autonomous data acquisition schemes using active learning. We demonstrate on-the-fly learning of machine learning force fields and use them to gain insights into previously inaccessible physical and chemical phenomena in ion conductors\, catalytic surface reactions\, 2D materials phase transformations\, and shape memory alloys. URL:https://seasevents.nmsdev7.com/event/pics-colloquium-designing-energy-conversion-materials-with-ab-initio-and-active-machine-learning-computations-of-electron-phonon-and-ion-dynamics/ LOCATION:Zoom – email kathom@seas.upenn.edu CATEGORIES:Colloquium ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu END:VEVENT END:VCALENDAR