BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//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:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250919T140000
DTEND;TZID=America/New_York:20250919T150000
DTSTAMP:20260602T005915
CREATED:20250829T143727Z
LAST-MODIFIED:20250829T143727Z
UID:20947-1758290400-1758294000@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: Enabling computationally efficient first-principles kinetic simulations in nanoporous catalysts using machine learning and data science with Brandon Bukowski
DESCRIPTION:Machine learning tools have tremendous potential to accelerate computationally complex physics-based simulations. One example is the need to accelerate materials discovery through first-principles Density Functional Theory (DFT) calculations. \nThis seminar will encompass how machine learning interatomic potentials accelerate the discovery of crystalline nanoporous solids such as zeolites and metal-organic frameworks (MOFs) that are employed in a wide range of catalytic processes due in part to their tunable micro-environments. Kinetics at intracrystalline sites can be modified by changing pore size\, pore architecture\, or polarity. These environments impart shape-selectivity that preferentially stabilizes transition states\, but the large design space including pore architecture\, polarity\, and catalytic active site identity preclude comprehensive kinetic studies. DFT describes the electronic states of reactive intermediates and transition states but cannot access the longer length scales necessary to quantify the fluxionality of coadsorbed species or solvents. Classical simulations can accurately simulate these conformational changes but require parameterized values. Machine learning interatomic potentials have emerged as a technique to derive parameterized models from DFT data\, and our aim is to adapt these models to predict the entropy and diffusion of reactive intermediates in nanoporous catalysts.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-enabling-computationally-efficient-first-principles-kinetic-simulations-in-nanoporous-catalysts-using-machine-learning-and-data-science-with-brandon-bukowski/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
END:VCALENDAR