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DTSTART;TZID=UTC:20250507T131500
DTEND;TZID=UTC:20250507T140000
DTSTAMP:20260902T144337
CREATED:20250220T073202Z
LAST-MODIFIED:20250430T115351Z
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SUMMARY:AI in CHEM Seminar Series: Albert Bartók (Warwick)
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 7 May 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Albert Bartók\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nMaterials modelling across the scales \nAlbert P. Bartók \nSchool of Engineering and Department of Physics\, University of Warwick\, UK \nThe past two decades have seen a transformative change in atomistic modelling with the development of machine-learned interatomic potentials\, which allow quantum-accurate simulations at an affordable computational cost. While the formalism of these models has converged\, there still remain open questions about the optimal way to generate training databases as well as about the reliability of potentials. In this talk\, I will present our efforts to automatically generate atomic databases using a combination of active learning and advanced sampling methods and how the resulting potential results in exceptionally accurate potential energy surface for Mg at a pressure range of 0-600 GPa. I will also show how fast and accurate potentials can help us discover novel phenomena\, illustrated by our observation on how helium affects dislocation mobility in tungsten. Finally\, I will report how transfer learning may be used to fine-tune foundation models using a little amount of data\, resulting in accurate\, but application-specific potentials. \nAbout the speaker\nAlbert Bartók-Pártay is an Associate Professor at the University of Warwick. He earned his Ph.D. degree in physics from the University of Cambridge in 2010\, his research having been on developing interatomic potentials based on ab initio data using machine learning. He was a Junior Research Fellow at Magdalene College\, Cambridge\, and later a Leverhulme Early Career Fellow. Before taking up his current position\, he was a Research Scientist at the Science and Technology Facilities Council. His research focuses on developing theoretical and computational tools to understand atomistic processes.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-albert-bartok/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ocamm.fi/wp-content/uploads/2025/02/ABartokPartay.png
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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BEGIN:VEVENT
DTSTART;TZID=UTC:20250527T100000
DTEND;TZID=UTC:20250527T133000
DTSTAMP:20260902T144337
CREATED:20250519T105039Z
LAST-MODIFIED:20250519T105039Z
UID:384-1748340000-1748352600@ocamm.fi
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:20260902T144337
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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