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DTSTART;TZID=UTC:20240311T100000
DTEND;TZID=UTC:20240311T110000
DTSTAMP:20260420T142518
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LAST-MODIFIED:20240124T131104Z
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SUMMARY:Seminar by Prof. Janine George on automation and workflows in atomistic simulation
DESCRIPTION:Save the date! OCAMM presents a special seminar by Prof. Janine George on automation and workflows in atomistic simulation. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 11 March 2024 @ 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 learn more about how high-throughput calculations of molecules and materials can be made efficient and tractable! \nTitle\nData-Driven Chemical Understanding with Bonding Analysis \nAbstract\nBonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties [1]. Implementations and algorithms (e.g.\, ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available [2\,3]. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale [3\,4]. The lecture will demonstrate how our tools\, that assess local atomic environments and perform automatic bonding analysis\, help to develop new machine learning models and a new intuitive understanding of materials [5\,6]. Furthermore\, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed [7–10]. \nReferences \n\nJ. George\, G. Hautier\, Trends Chem. 2021\, 3\, 86–95.\nD. Waroquiers\, J. George\, M. Horton\, S. Schenk\, K. A. Persson\, G.-M. Rignanese\, X. Gonze\, G. Hautier\, Acta Cryst B 2020\, 76\, 683–695.\nJ. George\, G. Petretto\, A. Naik\, M. Esters\, A. J. Jackson\, R. Nelson\, R. Dronskowski\, G.-M. Rignanese\, G. Hautier\, ChemPlusChem 2022\, 87\, e202200123.\n“LobsterPy\,” can be found under https://github.com/JaGeo/LobsterPy\, 2022.\nA. A. Naik\, C. Ertural\, N. Dhamrait\, P. Benner\, J. George\, Sci Data 2023\, 10\, 610.\nK. Ueltzen\, A. Naik\, C. Ertural\, P. Benner\, J. George\, Article in Preparation 2024.\nJ. George\, Trends Chem. 2021\, 3\, 697–699.\nA. Ganose\, et al.\, “atomate2\,” can be found under https://github.com/materialsproject/atomate2\, 2023.\nA. S. Rosen\, M. Gallant\, J. George\, J. Riebesell\, H. Sahasrabuddhe\, J.-X. Shen\, M. Wen\, M. L. Evans\, G. Petretto\, D. Waroquiers\, G.-M. Rignanese\, K. A. Persson\, A. Jain\, A. M. Ganose\, Journal of Open Source Software 2024\, 9\, 5995.\nI. Batatia\, P. Benner\, Y. Chiang\, A. M. Elena\, D. P. Kovács\, J. Riebesell\, X. R. Advincula\, M. Asta\, W. J. Baldwin\, N. Bernstein\, A. Bhowmik\, S. M. Blau\, V. Cărare\, J. P. Darby\, S. De\, F. Della Pia\, V. L. Deringer\, R. Elijošius\, Z. El-Machachi\, E. Fako\, A. C. Ferrari\, A. Genreith-Schriever\, J. George\, R. E. A. Goodall\, C. P. Grey\, S. Han\, W. Handley\, H. H. Heenen\, K. Hermansson\, C. Holm\, J. Jaafar\, S. Hofmann\, K. S. Jakob\, H. Jung\, V. Kapil\, A. D. Kaplan\, N. Karimitari\, N. Kroupa\, J. Kullgren\, M. C. Kuner\, D. Kuryla\, G. Liepuoniute\, J. T. Margraf\, I.-B. Magdău\, A. Michaelides\, J. H. Moore\, A. A. Naik\, S. P. Niblett\, S. W. Norwood\, N. O’Neill\, C. Ortner\, K. A. Persson\, K. Reuter\, A. S. Rosen\, L. L. Schaaf\, C. Schran\, E. Sivonxay\, T. K. Stenczel\, V. Svahn\, C. Sutton\, C. van der Oord\, E. Varga-Umbrich\, T. Vegge\, M. Vondrák\, Y. Wang\, W. C. Witt\, F. Zills\, G. Csányi\, 2023\, DOI 10.48550/arXiv.2401.00096.\n\nAbout the speaker\nJanine George received her Bachelor of Science in Chemistry and Master of Science (summa cum laude) also in Chemistry\, both from RWTH Aachen University\, in 2011 and 2013\, respectively. She then obtained a Doctorate (Dr. rer. nat\, summa cum laude) in Computational Solid-State Chemistry under the supervision of Prof. Richard Dronskowski\, RWTH Aachen University in 2017. During 2018-2021 she held a Post-Doc position in the groups of Prof. Geoffroy Hautier at the Université catholique de Louvain (now at Darthmouth College) and Prof. Gian-Marco Rignanese also at the Université catholique de Louvain. Since 2021 she is Junior Group Leader of the Group “Computational Materials Design” at the Federal Institute for Materials Research and Testing (Department Materials Chemistry) in Berlin and\, since 2023\, she holds a joint appointment as Professor for Materials Informatics between the latter and the FSU Jena (Institute of Condensed Matter Theory and Optics).
URL:https://ocamm.fi/event/seminar-by-dr-janine-george-on-automation-and-workflows-in-atomistic-simulation/
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/janine.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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BEGIN:VEVENT
DTSTART;TZID=UTC:20240207T141500
DTEND;TZID=UTC:20240207T151500
DTSTAMP:20260420T142518
CREATED:20240131T134025Z
LAST-MODIFIED:20240131T153714Z
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SUMMARY:Seminar by Tamas Stenczel on machine-learning accelerated ab-initio molecular dynamics
DESCRIPTION:Save the date! OCAMM presents a special seminar by Tamas Stenczel on accelerating ab initio molecular dynamics with on-the-fly machine learning. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 7 February 2024 @ 14:15 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 learn more about how you can make molecular dynamics orders of magnitude faster while retaining ab initio accuracy! \nTitle\nML Acceleration for Dynamics in Ab-Initio Modelling \nAbstract\nShowcase of existing methods and ongoing work on accelerating various dynamical simulations in ab-initio codes with modern ML. Replacing the ab-initio energy model where possible with an on-the-fly updated ML (GAP/ACE/MACE) one\, we will visit methods and frameworks making materials science & chemical ML models easy to access for all modelling needs. Next generation methods\, frameworks\, scientific challenges\, and opportunities for using and contributing to efforts in the field will be presented\, starting from recently published advances in the CASTEP code\, going to a general framework currently being implemented in a range of simulation tools. \nAbout the speaker\nTamás K. Stenczel is a researcher at the University of Cambridge\, with ties in academia and in various industrial realms. Amongst heading a research and software team at a family office\, conducting open academic research\, and everyday life\, he is a skiier and biker\, always striving to see and understand more of the world.
URL:https://ocamm.fi/event/seminar-by-tamas-stenczel-on-machine-learning-accelerated-ab-initio-molecular-dynamics/
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/2024/01/tamas.jpeg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20231113T140000
DTEND;TZID=Europe/Helsinki:20231113T150000
DTSTAMP:20260420T142518
CREATED:20230907T113618Z
LAST-MODIFIED:20230907T115837Z
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SUMMARY:Seminar by Prof. Rocío Mercado on artificial intelligence in biomolecular modeling
DESCRIPTION:OCAMM presents a special seminar by Prof. Rocío Mercado from Chalmers University of Technology\, Sweden on “Deep generative models for biomolecular engineering“. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 13 November 2023 @ 14:00 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 for a great talk on how artificial intelligence and molecular modeling are impacting the fields of biomolecular design and drug discovery. \nTitle\nDeep generative models for biomolecular engineering \nAbstract\nAI is transforming our approach to molecular engineering. Driven by the goal of accelerating drug development\, our aim is to develop AI-driven molecular engineering methods which will enhance our approach to biomolecular discovery\, such as drug discovery\, drug repurposing\, and chemical probe identification. This entails the development of generative and predictive tools that can learn from biochemical data\, such as molecular structures\, chemical reactions\, and biomedical data. While AI can be applied to a range of molecular engineering tasks\, one ideal area is de novo molecular design. De novo design is the concept of designing molecules with desired properties from scratch so as to minimize experimental screening\, and is poised to allow scientists to more efficiently traverse chemical space in search of optimal molecules\, and delegate error-prone decisions to computers via the use of predictive and generative models. In drug development\, de novo design methods can aid medicinal chemists in the design and selection of drug candidates\, with the added advantage that they can learn from datasets of billions of molecules in minutes and be constantly updated with new data. Deep molecular generative models are a particular approach to de novo design which uses deep neural networks to generate new molecules in silico\, and works by proposing atom-by-atom (or fragment-by-fragment) modifications to an initial graph structure to generate compounds predicted to achieve a certain property profile. Such models can be applied to a range of therapeutic modalities. \nIn this talk\, I will discuss the development of deep generative models for various molecular engineering tasks relevant to early-stage drug discovery. These include a model for synthesizability-constrained molecular generation\, a reinforcement learning framework for molecular graph optimization\, and recent applications from our group to the design of large modalities for targeted protein degradation. \nAbout the speaker\nRocío is a tenure-track assistant professor in the Data Science and AI division at Chalmers since January 2023. She heads the AI Laboratory for Biomolecular Engineering (AIBE) in the Department of Computer Science and Engineering. \nPreviously\, she was a postdoctoral associate in the Coley group at MIT\, as well as an industrial postdoc in the Molecular AI team at AstraZeneca. Throughout her postdoctoral career\, she worked on the development of deep generative models for small molecule drug discovery. Before AstraZeneca\, she was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. She received her PhD in Chemistry from UC Berkeley in August 2018\, and her BSc in Chemistry from Caltech in June 2013.
URL:https://ocamm.fi/event/seminar-by-prof-rocio-mercado-on-artificial-intelligence-in-biomolecular-modeling/
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/rocio-scaled.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231106
DTEND;VALUE=DATE:20231111
DTSTAMP:20260420T142518
CREATED:20230817T151020Z
LAST-MODIFIED:20230818T091336Z
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SUMMARY:Machine Learning Interatomic Potential School for Young & Early Career Researchers
DESCRIPTION:For up-to-date and more complete information\, please visit the official website: https://www.mlip-workshop.xyz/home\n\n\nMLIP 2023 is a CECAM & Psi-k hybrid school aimed at young and early-career researchers who are interested in using machine learning interatomic potentials (MLIP) in their research. The two main goals of this school are:  \n\n\nto give researchers a solid introduction to the basic scientific techniques of designing\, fitting\, and validating MLIPs for chemical/material systems \n\n\nto provide a platform for those interested in using MLIPs to connect with those involved in MLIP development to accelerate the adoption of ML techniques in the wider atomistic simulation community \n\n\nMLIP 2023 is the second edition of the MLIP 2021 workshop\, and will physically take place at Aalto University in Espoo\, Finland\, between 06–10 November 2023. Remote\, online participation is also possible. \nThe school will consist of keynote lectures on different topics of MLIP as well as hands-on tutorials that will allow the participants to apply the explained concepts to relevant toy cases and also their own research. The invited speakers are leading scientists in the field of MLIP\, at various career stages\, who are well–equipped to share their experience with those getting started in the field. \nInvited speakers\n\n\n\n\n\n\n\n\n\n\n\nAlice Allen\, Los Alamos National Laboratory\, USA\nNongnuch Artrith\, Utrecht University\, the Netherlands\nJörg Behler\, Ruhr-Universität Bochum\, Germany\nMichele Ceriotti\, École polytechnique fédérale de Lausanne\, Switzerland\nRose Cersonsky\, University of Winsconsin-Madison\, USA\nCecilia Clementi\, Freie Universität Berlin\, Germany\nZheyong (Bruce) Fan\, Bohai University\, China and Aalto University\, Finland\nGuillaume Fraux\, École polytechnique fédérale de Lausanne\, Switzerland\nAndrea Grisafi\, École Normale Supérieure\, France\nDávid Kovács\, University of Cambridge\, UK\nChris Pickard\, University of Cambridge\, UK\nMartin Uhrin\, Université Grenoble Alpes\, France\nSander Vandenhaute\, Ghent University\, Belgium\nChuck Witt\, University of Cambridge\, UK\nLinfeng Zhang\, AI for Science Institute and DP Technology\, China\n\n\n\n\n\n\n\n\n\n\n\nSchedule\nPlease visit https://www.mlip-workshop.xyz/schedule for an up-to-date schedule. \nRegistration\nPlease see https://www.mlip-workshop.xyz/participate for detailed instructions. However\, applications should still be submitted via the CECAM website (using the “Participate” tab). The registration deadline is 22 September 2023. \nVenue\nAalto University\, Finland. Check https://www.mlip-workshop.xyz/practical-info for more details on practicalities. \nOrganizers\n\nChiheb Ben Mahmoud\, University of Oxford (UK)\nMiguel Caro\, Aalto University (Finland)\nSanggyu Chong\, EPFL (Switzerland)\nFederico Grasselli\, EPFL (Switzerland)\nKevin Kazuki Huguenin-Dumittan\, EPFL (Switzerland)\nVenkat Kapil\, University of Cambridge (UK)\nFelix-Cosmin Mocanu\, École Normale Supérieure (France)\nJigyasa Nigam\, EPFL (Switzerland)\nDavide Tisi\, EPFL (Switzerland)\nMax Veit\, Aalto University (Finland)\n\nSponsors
URL:https://ocamm.fi/event/machine-learning-interatomic-potentials-theory-and-practice/
CATEGORIES:Conference
ATTACH;FMTTYPE=image/png:https://ocamm.fi/wp-content/uploads/2023/08/mlip-workshop-logo-2023.png
ORGANIZER;CN="Max Veit":MAILTO:max.veit@aalto.fi
END:VEVENT
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