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X-WR-CALDESC:Events for 
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20250527T100000
DTEND;TZID=UTC:20250527T133000
DTSTAMP:20260901T104807
CREATED:20250519T105039Z
LAST-MODIFIED:20250519T105039Z
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SUMMARY:AI for Science: from molecules to materials
DESCRIPTION:The Aalto University House of AI organizes the first AI for Science event in the Otaniemi campus\, with the collaboration of OCAMM. The events serves to highlight the research carried out at Aalto at the interface between AI and the chemical sciences. For more information\, visit the official website of the event: https://www.aalto.fi/en/events/ai-4-science-from-molecules-to-materials
URL:https://ocamm.fi/event/ai-for-science-from-molecules-to-materials/
LOCATION:Undergraduate Center\, Otakaari 1\, Espoo\, Uusimaa\, 02150\, Finland
CATEGORIES:Networking,Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260325T101500
DTEND;TZID=UTC:20260325T110000
DTSTAMP:20260901T104807
CREATED:20260213T125711Z
LAST-MODIFIED:20260225T063525Z
UID:455-1774433700-1774436400@ocamm.fi
SUMMARY:Seminar by Volker Deringer
DESCRIPTION:Prof. Volker Deringer from the University of Oxford will deliver an invited seminar on Wednesday 25 March 2025 at 10:15-11:00 in hall A304 (Ke2) of the Aalto University School of Chemical Engineering main building\, Kemistintie 1\, Espoo. \nMachine-learned interatomic potentials for materials chemistry \nVolker Deringer \nDepartment of Chemistry\, University of Oxford \nMachine-learned interatomic potentials (MLIPs) are now widely used in atomistic simulations\, giving access to length and time scales that are otherwise inaccessible to first-principles methods. Their reliability in practice\, however\, depends crucially on the quality and coverage of the training data. In this talk\, I will discuss data-efficient approaches to constructing MLIPs\, including ML-accelerated first-principles molecular dynamics for generating reference configurations\, and automated de novo exploration of relevant configurational spaces using the autoplex software. I will also highlight model-distillation strategies (specifically\, a teacher–student approach) using accurate but computationally costly models to train fast\, task-specific MLIPs for downstream applications. I will illustrate these ideas using examples of structurally complex materials: amorphous silicon\, graphene oxide\, and phase-change materials that are used in data storage and neuromorphic computing.
URL:https://ocamm.fi/event/seminar-by-volker-deringer/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2023/09/volker-deringer.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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