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DTSTART;TZID=UTC:20250115T131500
DTEND;TZID=UTC:20250115T140000
DTSTAMP:20260420T092442
CREATED:20250110T104214Z
LAST-MODIFIED:20250110T105327Z
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SUMMARY:AI in CHEM Seminar Series: Lab Automation for Scattering Analysis with Andy Anker
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 15 January 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 Andy Anker\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nMachine learning experimental scattering data analysis: concept\, practice\, and a future with automated laboratories \nAndy S. Anker1\,2 \n\nDepartment of Energy\, Danish Technical University\, Denmark\, ansoan@dtu.dk\nDepartment of Chemistry\, University of Oxford\, United Kingdom\, andy.anker@chem.ox.ac.uk\n\nThe rapid growth of materials chemistry data has outpaced conventional data analysis and modelling methods\, which can require enormous manual effort. To effectively analyse this wealth of information\, we are using machine learning (ML) models trained on extensive datasets of physics-based simulations for analysis of experimental scattering data [1\,2]. Yet\, relying solely on a single experimental technique often fails to provide sufficient information for resolving complex material structures. To overcome these limitations\, we are integrating diverse datasets into unified ML pipelines. Building on these methodological advances\, we look towards developing automated laboratories capable of accelerating materials synthesis. \nReferences \n\nAndy S. Anker\, Keith T. Butler\, Raghavendra Selvan\, Kirsten M. Ø. Jensen\, Chemical Science 2024\, 48\, 14003–14019.\nEmil T. S. Kjær\, Andy S. Anker\, Marcus N. Weng\, Simon J. L. Billinge\, Raghavendra Selvan\, Kirsten M. Ø. Jensen\, Digital Discovery 2023\, 1\, 69–80.\n\nAbout the speaker\nSee Andy’s Github page for more info \nI have recently been awarded a 4 000 000 DKK (~ £500 000) postdoctoral grant to pursue an academic career in the interface of materials chemistry\, machine learning and robotics. Here\, I am building a self-driving laboratory for controlled synthesis of inorganic nanomaterials in collaboration with Prof. Tejs Vegge and the CAPeX center at Technical University of Denmark\, Assoc. Prof. Volker Deringer’s group at Oxford University and Prof. Kasper Støy’s group at the IT University of Copenhagen. In 2024+2025\, I am physically working from Oxford. \nI obtained my PhD in materials chemistry from the Nanostructure Group UPCH\, University of Copenhagen\, supervised by Assoc. Prof. Kirsten Marie Ørnsbjerg Jensen\, where my main interest was to study nanoparticles and structures in solution with Total X-ray Scattering with Pair Distribution Function (PDF) and Small-Angle X-ray Scattering (SAXS). I applied advanced computer modelling\, in Python\, to combine information of both the local order from PDF and the particle order from SAXS\, which overcome problems that the methods cannot overcome individually. During my career\, the research focus has converged towards developing machine learning (ML) methods to analyse chemical data; especially PDF & SAXS\, after I met Assistant Professor Raghavendra Selvan who I had collaborated with since 2019. I have furthermore spent 6 months during my PhD working at Rutherford Appleton Laboratory with Senior Lecturer Keith Tobias Butler and the Scientific Machine Learning Group to develop an general approach to match simulated and experimental data in materials chemistry. During the last period of my PhD\, I have especially focused on using generative models to analyse scattering-\, and spectroscopy data.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-lab-automation-for-scattering-analysis-with-andy-anker/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2025/01/AndySAnker_Portraet-web-e1736506396902.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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DTSTART;TZID=UTC:20250121T100000
DTEND;TZID=UTC:20250121T110000
DTSTAMP:20260420T092442
CREATED:20250110T105843Z
LAST-MODIFIED:20250110T105843Z
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SUMMARY:Special seminar by Prof. Alexandre Tkatchenko
DESCRIPTION:Save the date! OCAMM presents a special seminar by Prof. Alexandre Tkatchenko from the University of Luxembourg\, who will talk about the role of AI in accelerating molecular simulations. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 21 January 2025 @ 10:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to find out how AI can be leveraged to speed up quantum mechanical simulations of atoms and molecules! \nTitle \nRealizing Schrödinger’s dream with AI-enabled molecular simulations \nAbout the speaker \nAlexandre Tkatchenko is a professor at the Department of Physics and Materials Science (and head of this department since January 2020) at the University of Luxembourg\, where he holds a chair in Theoretical Chemical Physics composed of ~35 multidisciplinary scientists. Tkatchenko also holds a distinguished visiting professor position at the Technical University of Berlin. His group develops accurate and efficient first-principles computational and artificial intelligence models to study a wide range of complex materials\, aiming at qualitative understanding and quantitative prediction of their structural\, cohesive\, electronic\, and optical properties at the atomic scale and beyond. He has delivered more than 450 invited talks\, seminars\, and colloquia worldwide\, published 240 articles in prestigious journals (h-index of 89 with more than 45\,000 citations; Top 1% ISI highly cited researcher since 2018 until now)\, and serves on the editorial boards of four society journals: Science Advances (AAAS)\, Physical Review Letters (APS)\, Journal of Physical Chemistry Letters (ACS)\, and Chemical Science (RSC). Tkatchenko has received a number of awards\, including APS Fellow from the American Physical Society\, Fellow of the Royal Society of Chemistry\, Gerhard Ertl Young Investigator Award of the German Physical Society\, Dirac Medal from the World Association of Theoretical and Computational Chemists (WATOC)\, van der Waals prize from ICNI\, Feynman Prize for Nanotechnology from the Foresight Institute\, and five flagship grants from the European Research Council (ERC): a Starting Grant in 2011\, a Consolidator Grant in 2017\, an Advanced Grant in 2022\, and Proof-of-Concept Grants in 2020 and 2023. He is also a co-founder of Quastify GmbH – a start-up that combines quantum and statistical mechanics with machine learning for efficiently exploring chemical spaces.
URL:https://ocamm.fi/event/special-seminar-by-prof-alexandre-tkatchenko/
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/2025/01/deeplearn-tkatchenko.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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