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DTSTART;TZID=UTC:20240311T100000
DTEND;TZID=UTC:20240311T110000
DTSTAMP:20260831T043159
CREATED:20230907T121250Z
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:20240318T141500
DTEND;TZID=UTC:20240318T151500
DTSTAMP:20260831T043159
CREATED:20230907T120034Z
LAST-MODIFIED:20240318T083359Z
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SUMMARY:Seminar by Prof. Volker Deringer on machine-learning-based simulation of amorphous materials
DESCRIPTION:OCAMM presents a special seminar by Prof. Volker Deringer from the University of Oxford\, UK on machine-learning-based simulation of amorphous materials. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 18 March 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 delve into the intricate atomic structure and fascinating properties of amorphous materials! \nTitle \nMachine-learning-driven advances in modelling amorphous materials \nAbstract \nUnderstanding the connections between the atomic-scale structure of materials and their macroscopic properties is among the most important research challenges in solid-state and materials chemistry. Atomistic simulations based on quantum-mechanical methods have played a key role in this – but they are computationally demanding\, and therefore they will inevitably reach their limits when materials with highly complex structures are to be studied. Machine learning (ML) based interatomic potentials are a rapidly emerging approach that helps to overcome this limitation: being “trained” on a suitably chosen set of quantummechanical data\, ML potentials achieve comparable accuracy whilst giving access to much larger-scale simulations – with thousands or even millions of atoms.\nIn this presentation\, I will showcase some recent advances in the modelling and understanding of inorganic materials that have been enabled by ML-driven simulations. I will argue that ML potentials are particularly useful for modelling non-crystalline (amorphous) structures that are difficult to characterise experimentally. I will survey recent work ranging from structural transitions in amorphous silicon [1] to multicomponent systems – specifically\, chalcogenide phase-change materials used in digital data storage [2]. I will also discuss methodological aspects\, including a perspective for using large synthetic datasets to pre-train neural-network potentials which can subsequently be fine-tuned on quantum-mechanical data [3]. The development of new\, accurate and efficient atomistic ML models promises a way to more fully understand the structure and properties of amorphous materials on the atomic scale.\n[1] V. L. Deringer\, N. Bernstein\, G. Csányi\, C. Ben Mahmoud\, M. Ceriotti\, M. Wilson\, D. A. Drabold\, S. R. Elliott\, Nature 2021\, 589\, 59.\n[2] Y. Zhou\, W. Zhang\, E. Ma\, V. L. Deringer\, Nat. Electron. 2023\, 6\, 746.\n[3] J. L. A. Gardner\, K. T. Baker\, V. L. Deringer\, Mach. Learn.: Sci. Technol. 2024\, 5\, 015003.
URL:https://ocamm.fi/event/seminar-by-prof-volker-deringer-on-machine-learning-based-simulation-of-carbon-materials/
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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