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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:20201002T140000 DTEND;TZID=America/New_York:20201002T150000 DTSTAMP:20200914T132816Z CREATED:20200914T132816Z LAST-MODIFIED:20200914T132816Z UID:3360-1601647200-1601650800@seasevents.nmsdev7.com SUMMARY:PICS Seminar: "Fusing machine learning and atomistic simulations for materials design" DESCRIPTION:Data-driven approaches match or outperform humans at a number of tasks\, including pattern recognition in images and text or planning and strategy in rule-based games. The application of machine learning techniques is also promising for accelerating materials design. However\, experimental data for training is typically scarce and sparse. The interplay between physics-based simulations and data-driven models is particularly advantageous. It allows relying on transferable laws rather than only fitting data in a black box fashion. Meanwhile\, learning from data\nprovides a unique opportunity to parameterize and augment physics-based models\, or completely replace them. \nModels can be built that map the structure and composition of materials to their properties. With such models\, it is\nthen possible to rapidly screen libraries of candidate materials for a desired application before going to the lab. Generative models go one step further and allow tackling the inverse problem: given the desired property\, automatically suggesting a new optimal material that achieves it. \nHow to represent matter so that it can be read into or written by a computer program is key for these coupled tasks of property prediction and materials optimization. Strategies are needed to represent materials in a machine-readable way that is data-efficient\, expressive\, respectful of physical invariants and\, ideally\, invertible. \nHere\, we will discuss our current efforts in building bottom-up atom-level representations for materials design. These include variational autoencoders for dimensionality reduction and inverse design in molecules and polymers\,\nrepresentation and unsupervised learning for graphs and sequences in crystals and polymers\, generative models to\naccelerate Monte Carlo simulations of alloy phase diagrams or end-to-end differentiable simulations. \n  URL:https://seasevents.nmsdev7.com/event/pics-seminar-fusing-machine-learning-and-atomistic-simulations-for-materials-design/ 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