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DTSTART;TZID=Europe/London:20250206T160000
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DTSTAMP:20260410T141056
CREATED:20250127T115826Z
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SUMMARY:King's Physics Dept. Seminar: Quantum Technologies & Algorithms for Chemistry and Life Science
DESCRIPTION:King's Physics Dept. Seminar: Quantum Technologies & Algorithms for Chemistry and Life Science Share on X\n\n\n\n\nMartina Stella – Algorithmiq & ICTP\, Trieste \n\n\n\nPlease join us in-person for a seminar by Dr Martina Stella (Algorithmiq and ICTP\, Trieste) at King’s College London\, Physics Department. \n\n\n\n\n\n\n\n\nREGISTER
URL:https://thomasyoungcentre.org/event/kings-physics-dept-seminar-quantum-technologies-algorithms-for-chemistry-and-life-science/
LOCATION:Room S7.06\, Strand Building 7th Floor\, Strand\, London\, WC2R 2LS\, United Kingdom
CATEGORIES:Main event
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DTSTART;TZID=Europe/London:20250218T160000
DTEND;TZID=Europe/London:20250218T180000
DTSTAMP:20260410T141056
CREATED:20250124T112321Z
LAST-MODIFIED:20250204T154211Z
UID:6255-1739894400-1739901600@thomasyoungcentre.org
SUMMARY:TYC AI/ML Interest Group Seminar: advances in machine learning for electrochemical systems
DESCRIPTION:TYC AI/ML Interest Group Seminar: advances in machine learning for electrochemical systems Share on X\n\n\n\n\n \n\n\n\nJörg Behler\, Ruhr University Bochum & Clotilde Cucinotta\, Imperial College London\n\n\n\nAtomistic Simulations with High-Dimensional Neural Network PotentialsJörg Behler – Lehrstuhl für Theoretische Chemie II\, Ruhr-Universität Bochum\, Germany and Research Center Chemical Sciences and Sustainability\, Research Alliance Ruhr\, Germany \n\n\n\nIn recent years\, there has been tremendous progress in the development of interatomic potentials employing machine learning. High-dimensional neural network potentials (HDNNP) are an important class of machine learning potentials\, which allow to combine the accuracy of electronic structure calculations with the efficiency of simple empirical potentials enabling large-scale simulations. HDNNPs can be classified into four generations\, which allow to study different types of systems and physical phenomena. In this talk\, an overview about the methodical evolution of HDNNPs will be given along with typical example applications to condensed systems with a particular focus on chemical processes at interfaces. \n\n\n\n \n\n\n\nExploring the Pt(111)-Electrolyte Interface Under Applied Potentials with Ab Initio Molecular DynamicsClotilde Cucinotta\, Imperial College London \n\n\n\nIn this talk\, I will discuss some complexities in the simulation of electrified interfaces at the nanoscale\, focusing on the impact of applied potentials on their physicochemical properties. My approach is based on the development of highly realistic ab initio molecular dynamics models of charged electrode-electrolyte interfaces under bias. I will discuss recent advancements in modelling the double layer of the electrified Pt(111)-electrolyte interface\, particularly in terms of its response to the applied electrode potential. This is achieved through the application of electrode-charging and potential control methodologies developed in my group. If time permits\, I will discuss how the how insights from molecular electronics can lead to a more sophisticated understanding of electrochemical phenomena.
URL:https://thomasyoungcentre.org/event/tyc-ai-ml-interest-group-seminar-advances-in-machine-learning-for-electrochemical-systems/
LOCATION:228 Bagrit theatre (2nd Floor)\, Royal School of Mines\, Imperial College London\, London\, SW7 2AZ
CATEGORIES:Main event
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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250226T150000
DTEND;TZID=Europe/London:20250226T180000
DTSTAMP:20260410T141056
CREATED:20250109T140617Z
LAST-MODIFIED:20250130T151450Z
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SUMMARY:TYC Alumni Pathway Panel
DESCRIPTION:TYC Alumni Pathway Panel Share on X\n\n\n\n\nThe TYC is hosting the first in a series of alumni events\, led by recent former TYC member Vas Fotopoulos (now at MIT)\, at which former members come together to form a panel to present their career trajectory\, and to answer questions from current TYC students and PDRAs. \n\n\n\nThey will give a brief introduction to themselves\, share their journey so far and discuss their current work. The focus will be on career paths\, pursuing postdocs or industrial positions after completing a PhD\, and answering students’ questions. \n\n\n\nThe panel will be structured as an in-person event\, with the panel attending online and in person. \n\n\n\nVas has handpicked our first panel\, who we believe will inspire you\, and provide a multitude of fascinating insights into life after PhD. \n\n\n\nThe event will be followed by a drinks social in the Nyholm Room. \n\n\n\nRegistration is free but required \n\n\n\n\nRegister\n\n\n\n\nPanellists\n\n\n\nChair: Rashid E A M Al-Heidous – Lecturer at Qatar UniversityRashid achieved his Masters in nanotechnology at Imperial College London\, followed by a PhD. He took up a position as lecturer at Qatar in 2024. \n\n\n\nYasmine Al-Hamdani – Naples & UCLYasmine is a post-doctoral researcher focused on modelling materials with ab initio methods such as quantum Monte Carlo. Yasmine finished her EngD at UCL in 2016 and has since worked at universities in Luxembourg\, Switzerland\, the UK\, and Italy. \n\n\n\nAlex Aziz – Manchester Metropolitan Materials Chemistry lecturer and part of the Joint Education Institute with Hubei UniversityMy research focuses on utilizing density functional theory and molecular dynamics methods to gain a fundamental understanding of material properties and their optimization for applications in energy storage and generation. \n\n\n\nZachary Goodwin – Extraordinary Junior Research Fellow in Materials\, Glasstone Research Fellow in Materials at University of OxfordMy research focuses on the theory and simulation of materials of interest for applications in energy storage technologies\, from liquid electrolytes to low-dimensional layered materials. \n\n\n\nSean Kavanagh – Environmental Fellow at Harvard University\, hosted by the Materials Intelligence Research group of Prof. Boris KozinskyMy research uses computational methods such as Density Functional Theory (DFT) and machine learning (ML) to simulate and predict the properties of materials – in particular defects in solids
URL:https://thomasyoungcentre.org/event/tyc-alumni-pathway/
LOCATION:Ramsay Lecture Theatre\, G21\, Christopher Ingold Building\, Gordon Street\, London\, WC1H 0AJ
CATEGORIES:Main event
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