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X-WR-CALDESC:Events for THOMAS YOUNG CENTRE
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DTSTART;TZID=Europe/London:20230502T130000
DTEND;TZID=Europe/London:20230502T150000
DTSTAMP:20260411T203152
CREATED:20230323T205931Z
LAST-MODIFIED:20230427T101632Z
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SUMMARY:TYC Early Career Researchers' Forum: Mustafa Abbas
DESCRIPTION:Home » Main event\n\n\nVenue: UCL Physics E3/7 \n\n\n\n\n\n\n\n\n\n\nTYC Early Career Researchers' Forum: Mustafa Abbas Share on X\n\n\n\n\nMustafa Abbas\, visitor of Alex Shluger and Sir Richard Catlow\, talking about the challenges of research at his university in Sudan. \n\n\n\nMustafa is visiting the Thomas Young Centre to learn how to confidently use Quantum Mechanics and Molecular Dynamics software tools and learn about the potential insight those software packages can provide. He works in the following areas: Catalysis\, crystallization\, adsorption\, and photovoltaics.  \n\n\n\nAbstractA Sudanese academic presents an overview of the challenges facing researchers in the midst of political and economic instability as well as years of civil wars\, revolutions and ongoing military conflicts. Based on the UNESCO report he conducted on nanotechnology challenges\, in addition to his 15 years of personal experience\, he presents the difficulties facing researchers in his country. He also shares inspiring success stories of his PhD students who have persevered against all odds. Despite the challenging circumstances\, the presentation highlights the importance of scientific progress and the potential for innovation to positively impact the future of Sudan. \n\n\n\nBiographyDr. Mustafa Abbas Mustafa is the Chair holder of the UNESCO Chair on Materials and Nanotechnology at the University of Khartoum\, Sudan. He is currently on sabbatical leave and is hosted by Prof. Alex Shluger and Prof Sir Richard Catlow. He has extensive research experience in Process Systems Engineering and Nanotechnology\, with applications in areas including the Oil and Gas Industry\, Bio-refineries and water/waste treatment. He has published extensively in top journals as well as received numerous awards for his scientific research excellence. He is a member of various professional bodies including the UKRI International Development Peer Review College. Furthermore\, he has also provided consultancy services to a number of national and international bodies\, including UNESCO and Hydro Industries Ltd (UK).
URL:https://thomasyoungcentre.org/event/tyc-early-career-researchers-forum-mustafa-abbas-2/
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20230504T160000
DTEND;TZID=Europe/London:20230504T180000
DTSTAMP:20260411T203152
CREATED:20230323T170552Z
LAST-MODIFIED:20230418T132951Z
UID:3901-1683216000-1683223200@thomasyoungcentre.org
SUMMARY:TYC Highlight Seminar: The Molecular Simulation Design Framework (MoSDeF): Capabilities and Applications
DESCRIPTION:Peter Cummings\, Heriot-Watt University \n\n\n\n\n\nVenue: G20\, Department of Materials\, Imperial College London \n\n\n\n\n\n\n\n\n\n\nTYC Highlight Seminar: The Molecular Simulation Design Framework (MoSDeF): Capabilities and Applications – Peter Cummings\, Heriot-Watt University Share on X\n\n\n\n\nMolecular simulation plays an important role in many sub-fields of chemical engineering\, just as it does in science and engineering in general. Soft matter systems (those easily deformed at room temperature – e.g.\, liquids\, polymers\, foams\, gels\, colloids\, and most biological materials) are ubiquitous in chemical engineering\, but they pose particular computational challenges since the differences in potential energy between distant configurations are on the same order as the thermal motion\, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation. Furthermore\, performing a molecular simulation of a soft matter system involves multiple steps\, which have traditionally been performed by researchers in a “bespoke” fashion. The result is that many soft matter simulations published in the literature are not reproducible based on the information provided in the publication\, and large-scale screening (as envisaged in the Materials Genome Initiative) of soft materials systems is a formidable challenge. \n\n\n\nTo address the issues of reproducibility and computational screening capability\, we have been developing the Molecular Simulation and Design Framework (MoSDeF) software suite\, including the open­source mBuild (https://github.com/mosdef­hub/mbuild) and Foyer (https://github.com/mosdef­hub/foyer) packages. We will introduce MoSDeF and its capabilities in this presentation. We will also illustrate how\, by combining with the Glotzer group’s Signac­flow workflow manager (https://bitbucket.org/glotzer/signac­flow)\, we have facilitated screening of soft matter systems over chemical/structural parameter spaces. \n\n\n\nWe will report results for two timely applications: lubrication of nanoscale devices featuring surfaces functionalized by monolayers in sliding contact\, and understanding diffusion of ionic liquids in organic solvents (related to energy storage devices). In both cases\, automation of the simulation through use of the MoSDeF tools enables screening and reproducibility.
URL:https://thomasyoungcentre.org/event/tyc-highlight-seminar-peter-cummings-2/
CATEGORIES:Main event
ORGANIZER;CN="Johannes Lischner":MAILTO:j.lischner@imperial.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20230512T100000
DTEND;TZID=Europe/London:20230512T120000
DTSTAMP:20260411T203152
CREATED:20230417T162035Z
LAST-MODIFIED:20230417T163126Z
UID:3980-1683885600-1683892800@thomasyoungcentre.org
SUMMARY:Theory and Machine Learning for Crystal Growth lecture (1/3)
DESCRIPTION:Venue: Online \n\n\n\n\n\n\n\n\nTheory and Machine Learning for Crystal Growth lecture (1/3) Share on X\n\n\n\n\nProf. Akira Kusaba from Kyushu University\, Japan will present an online course of 3 lectures on Theory and Machine Learning for Crystal Growth \n\n\n\nLectures will take place on Fridays\, May 12\, 19\, 26 at 10 am – 12 noon on Zoom at the following link:  \n\n\n\nJoin Zoom Meetinghttps://ucl.zoom.us/j/95072055014 \n\n\n\nMeeting ID: 950 7205 5014 \n\n\n\nThis lecture course aims to introduce students to theory and machine learning for crystal growth. It contains two complementary parts: qualitative understanding and quantitative prediction of the phenomena. In the first part\, students will learn how classical and analytical theories can be used to understand crystal growth phenomena. After introducing the concept of rate-limiting processes\, the formulas for the growth rates limited by nucleation\, step flow and mass transport are derived. Also\, the need to consider surface reconstruction is discussed. In the second part of machine learning\, students will learn how machine learning can improve crystal growth experiments. Emphasis is on the use of machine learning from the perspective of material scientists and material process engineers. \n\n\n\nSyllabus: \n\n\n\nPart 1: Theory for Crystal Growth \n\n\n\n\nBasic Concept and Early Stage of Growth (Elementary Processes\, Thermodynamics\, Supersaturation\, Nucleation)\n\n\n\nAtomic Models (Surface Energy\, Surface Reconstruction\, Surface Phase Diagram\, First-principle Calculations\, Statistical Mechanics)\n\n\n\nMesoscopic Models (BCF Theory\, Interplane Diffusion\, Monte Carlo Simulations)\n\n\n\nMacroscopic Models (Thermodynamic Analysis\, Driving Force for Growth\, Alloy Composition)\n\n\n\n\nPart 2: Machine Learning for Crystal Growth \n\n\n\n\nBasic Concept (Regression\, Classification\, Dimensionality Reduction\, Clustering)\n\n\n\nBayesian Optimization: After understanding how Bayesian optimization works\, we will consider how it can be utilized in our research.\n\n\n\nMulti-objective Optimization (and Data Assimilation): through examples\, we will learn how to use multi-objective optimization in materials process engineering and how data assimilation\, in which experimental data improves the predictive performance of simulations\, can be used.\n\n\n\nSummary\, Advanced models and Applications: will introduce more advanced and recent models and my own research applying crystal growth theory and machine learning.
URL:https://thomasyoungcentre.org/event/theory-and-machine-learning-for-crystal-growth-lecture-1-3/
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20230519T100000
DTEND;TZID=Europe/London:20230519T120000
DTSTAMP:20260411T203152
CREATED:20230417T162247Z
LAST-MODIFIED:20230420T091922Z
UID:3983-1684490400-1684497600@thomasyoungcentre.org
SUMMARY:Theory and Machine Learning for Crystal Growth lecture (2/3)
DESCRIPTION:Venue: Online \n\n\n\n\n\n\n\n\nTheory and Machine Learning for Crystal Growth lecture (2/3) Share on X\n\n\n\n\nProf. Akira Kusaba from Kyushu University\, Japan will present an online course of 3 lectures on Theory and Machine Learning for Crystal Growth \n\n\n\nLectures will take place on Fridays\, May 12\, 19\, 26 at 10 am – 12 noon on Zoom at the following link:  \n\n\n\nJoin Zoom Meetinghttps://ucl.zoom.us/j/95072055014 \n\n\n\nMeeting ID: 950 7205 5014 \n\n\n\nThis lecture course aims to introduce students to theory and machine learning for crystal growth. It contains two complementary parts: qualitative understanding and quantitative prediction of the phenomena. In the first part\, students will learn how classical and analytical theories can be used to understand crystal growth phenomena. After introducing the concept of rate-limiting processes\, the formulas for the growth rates limited by nucleation\, step flow and mass transport are derived. Also\, the need to consider surface reconstruction is discussed. In the second part of machine learning\, students will learn how machine learning can improve crystal growth experiments. Emphasis is on the use of machine learning from the perspective of material scientists and material process engineers. \n\n\n\nSyllabus: \n\n\n\nPart 1: Theory for Crystal Growth \n\n\n\n\nBasic Concept and Early Stage of Growth (Elementary Processes\, Thermodynamics\, Supersaturation\, Nucleation)\n\n\n\nAtomic Models (Surface Energy\, Surface Reconstruction\, Surface Phase Diagram\, First-principle Calculations\, Statistical Mechanics)\n\n\n\nMesoscopic Models (BCF Theory\, Interplane Diffusion\, Monte Carlo Simulations)\n\n\n\nMacroscopic Models (Thermodynamic Analysis\, Driving Force for Growth\, Alloy Composition)\n\n\n\n\nPart 2: Machine Learning for Crystal Growth \n\n\n\n\nBasic Concept (Regression\, Classification\, Dimensionality Reduction\, Clustering)\n\n\n\nBayesian Optimization: After understanding how Bayesian optimization works\, we will consider how it can be utilized in our research.\n\n\n\nMulti-objective Optimization (and Data Assimilation): through examples\, we will learn how to use multi-objective optimization in materials process engineering and how data assimilation\, in which experimental data improves the predictive performance of simulations\, can be used.\n\n\n\nSummary\, Advanced models and Applications: will introduce more advanced and recent models and my own research applying crystal growth theory and machine learning.
URL:https://thomasyoungcentre.org/event/theory-and-machine-learning-for-crystal-growth-lecture-2-3/
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20230525T100000
DTEND;TZID=Europe/London:20230525T180000
DTSTAMP:20260411T203152
CREATED:20230327T123227Z
LAST-MODIFIED:20230518T113944Z
UID:3927-1685008800-1685037600@thomasyoungcentre.org
SUMMARY:TYC Postgraduate Student Day 2023
DESCRIPTION:Venue: Jeremy Bentham Room\, Wilkins Building\, University College London \n\n\n\n\n\n\n\n\n\n\nTYC Postgraduate Student Day 2023 Share on X\n\n\n\n\nSubmit your abstract and register here: \n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nThere will be 12 talks and almost 40 posters on display from across the four London TYC colleges\, LSBU and Brunel\, at the TYC Student Day.This year coincides with the 250th anniversary of the birth of Thomas Young – there will be cake!  We are very pleased to welcome Andrew Robinson\, author of Thomas Young: The Last Man Who Knew Everything. \n\n\n\nAbstract: The blue plaque on Thomas Young’s house in central London labels him simply ‘Man of Science’\, 1773-1829. There was no space to mention he was a physicist\, physiologist and physician; a classicist\, Egyptologist and philologist; and a prolific writer—probably the greatest polymath since Leonardo da Vinci. Young proved—contra Isaac Newton’s corpuscular theory of light—that light is a wave\, through his inspirational ‘double-slit’ experiment; he also explained elasticity—the ratio between stress and strain in materials—through Young’s modulus. He revealed how the human eye focuses\, perceives colours and suffers from astigmatism. As a Fellow of the Royal College of Physicians\, he practiced medicine for three decades. He launched the decipherment of the hieroglyphic and demotic scripts on Egypt’s Rosetta Stone. He compared 400 languages and coined the term ‘Indo-European’. His numerous articles and books included more contributions to the Encyclopaedia Britannica than any other contributor in its history. This talk on his 250th birth anniversary will introduce his life and work\, and consider why polymathy still matters in a world of specialization. For example\, his physician’s investigation of vision triggered his fascination with the physics of light. Not long before his death\, Young said: ‘It is probably best for mankind that the researches of some investigators should be conceived within a narrow compass\, while others pass more rapidly through a more extensive sphere of research.’ \n\n\n\n(The Last Man Who Knew Everything. 2023 edition: https://www.openbookpublishers.com/books/10.11647/obp.0344.) \n\n\n\nWe also welcome a team from Ab Initio Software Ltd. to discuss careers within their company\, and who are generously sponsoring cash prizes for the ‘Best Talk’ and ‘Best Poster’ awards. \n\n\n\n\nAb Initio’s customers use our enterprise software platform to build applications that tackle the largest and most complex data processing challenges. These applications are some of the most complex operational and analytical systems in the world – mission critical applications with demanding performance requirements.  The characteristics of these applications include the processing of huge volumes of data (petabytes are not uncommon); low latency real-time applications; applications that dynamically reconfigure themselves based on their data and applications that scale as data volumes increase. \n\n\n\nAb Initio has grown through reference rather than marketing\, so you may not have heard of us\, but our customers are among the largest companies in the world in industries such as financial services\, retail\, telecommunications\, transportation\, healthcare and high tech.  We are headquartered in Boston\, Massachusetts and have offices all over the world. Our UK office is in Weybridge\, Surrey.
URL:https://thomasyoungcentre.org/event/tyc-postgraduate-student-day-2023/
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20230526T100000
DTEND;TZID=Europe/London:20230526T120000
DTSTAMP:20260411T203152
CREATED:20230417T162350Z
LAST-MODIFIED:20230417T162913Z
UID:3985-1685095200-1685102400@thomasyoungcentre.org
SUMMARY:Theory and Machine Learning for Crystal Growth lecture (3/3)
DESCRIPTION:Venue: Online \n\n\n\n\n\n\n\n\nTheory and Machine Learning for Crystal Growth lecture (1/3) Share on X\n\n\n\n\nProf. Akira Kusaba from Kyushu University\, Japan will present an online course of 3 lectures on Theory and Machine Learning for Crystal Growth \n\n\n\nLectures will take place on Fridays\, May 12\, 19\, 26 at 10 am – 12 noon on Zoom at the following link:  \n\n\n\nJoin Zoom Meetinghttps://ucl.zoom.us/j/95072055014 \n\n\n\nMeeting ID: 950 7205 5014 \n\n\n\nThis lecture course aims to introduce students to theory and machine learning for crystal growth. It contains two complementary parts: qualitative understanding and quantitative prediction of the phenomena. In the first part\, students will learn how classical and analytical theories can be used to understand crystal growth phenomena. After introducing the concept of rate-limiting processes\, the formulas for the growth rates limited by nucleation\, step flow and mass transport are derived. Also\, the need to consider surface reconstruction is discussed. In the second part of machine learning\, students will learn how machine learning can improve crystal growth experiments. Emphasis is on the use of machine learning from the perspective of material scientists and material process engineers. \n\n\n\nSyllabus: \n\n\n\nPart 1: Theory for Crystal Growth \n\n\n\n\nBasic Concept and Early Stage of Growth (Elementary Processes\, Thermodynamics\, Supersaturation\, Nucleation)\n\n\n\nAtomic Models (Surface Energy\, Surface Reconstruction\, Surface Phase Diagram\, First-principle Calculations\, Statistical Mechanics)\n\n\n\nMesoscopic Models (BCF Theory\, Interplane Diffusion\, Monte Carlo Simulations)\n\n\n\nMacroscopic Models (Thermodynamic Analysis\, Driving Force for Growth\, Alloy Composition)\n\n\n\n\nPart 2: Machine Learning for Crystal Growth \n\n\n\n\nBasic Concept (Regression\, Classification\, Dimensionality Reduction\, Clustering)\n\n\n\nBayesian Optimization: After understanding how Bayesian optimization works\, we will consider how it can be utilized in our research.\n\n\n\nMulti-objective Optimization (and Data Assimilation): through examples\, we will learn how to use multi-objective optimization in materials process engineering and how data assimilation\, in which experimental data improves the predictive performance of simulations\, can be used.\n\n\n\nSummary\, Advanced models and Applications: will introduce more advanced and recent models and my own research applying crystal growth theory and machine learning.
URL:https://thomasyoungcentre.org/event/theory-and-machine-learning-for-crystal-growth-lecture-3-3/
CATEGORIES:Main event
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