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X-WR-CALDESC:Events for Dirac Materials
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DTSTART:20200329T010000
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DTSTART;TZID="Europe/Stockholm":20200512T110000
DTEND;TZID="Europe/Stockholm":20200512T120000
DTSTAMP:20260411T164043
CREATED:20200511T103016Z
LAST-MODIFIED:20200511T111326Z
UID:1707-1589281200-1589284800@diracmaterials.org
SUMMARY: Nami Matsubara (KTH) - Neutron Diffraction and Battery Materials
DESCRIPTION:Speaker: Nami Matsubara (KTH)\nTitle: Neutron Diffraction and Battery Materials\n\nTime: 11am CEST\nZoom id: https://kth-se.zoom.us/j/61941862292\n\n \n\n\nAbstract: Increasing demand for high performance\, low-cost\, lightweight and long-life batteries\, it is essential to optimise the performance of current materials and discover new candidate compounds for future batteries. In order to improve the property o  materials\, a detailed investigation of crystal structure and dynamics on an atomic scale is necessary.\nNeutron powder diffraction (NPD) is widely used and well-established technique to determine the structure of materials and it has several advantages over X-rays and electrons for studies of energy materials since it is more sensitive to probe the locations of light atoms. NPD can also be a powerful method to investigate ion dynamics such as diffusion pathways through advanced maximum entropy method or Fourier difference maps analysis. \nIn addition\, neutrons are uncharged but do have a magnetic moment. As a result\, there is an additional scattering due to the interaction between the neutron magnetic moment and the magnetic moment of the atom. Many battery cathode materials show interesting low temperature physics\, which can be examined by using NPD.\n\n\n  \n
URL:http://diracmaterials.org/calendar2/nami-matsubara-kth-neutron-diffraction-and-battery-materials/
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DTSTART;TZID="Europe/Stockholm":20200512T160000
DTEND;TZID="Europe/Stockholm":20200512T170000
DTSTAMP:20260411T164043
CREATED:20200506T091653Z
LAST-MODIFIED:20200506T091653Z
UID:1702-1589299200-1589302800@diracmaterials.org
SUMMARY:Michele Ceriotti - Symmetry\, locality and long-range interactions in atomistic machine learning
DESCRIPTION:Symmetry\, locality and long-range interactions in atomistic machine learning\n================================================= \nMachine learning models are proving to be extremely effective in\npredicting the properties of atomistic configurations of matter\,\ncircumventing the need for time consuming electronic structure\ncalculations. The most succesful schemes achieve transferability\nby means of a local representation of structures\, in which the\nproblem of predicting a property is broken down into the prediction\nof local\, atom-centred contributions\, and fundamental symmetries\nare incorporated exactly. This approach is however not efficient\nin describing long-range interatomic forces\, such as those arising\ndue to electrostatics.\nI will present a possible solution to this conundrum based on the\nlong-distance equivariant (LODE) framework\, that combines a local\ndescription of matter with the appropriate\, long-range asymptotic\nbehaviour of interactions.  \n=================================================\n \nZoom Login \nJoin Zoom Meeting\nhttps://stockholmuniversity.zoom.us/j/67379248617 \nMeeting ID: 673 7924 8617\nOne tap mobile\n+16699006833\,\,67379248617# US (San Jose)\n+12532158782\,\,67379248617# US (Tacoma) \nDial by your location\n+1 669 900 6833 US (San Jose)\n+1 253 215 8782 US (Tacoma)\n+1 301 715 8592 US (Germantown)\n+1 312 626 6799 US (Chicago)\n+1 346 248 7799 US (Houston)\n+1 408 638 0968 US (San Jose)\n+1 408 652 8184 US (San Jose)\n+1 646 876 9923 US (New York)\nMeeting ID: 673 7924 8617\nFind your local number: https://stockholmuniversity.zoom.us/u/cbvOSpXxkD \n
URL:http://diracmaterials.org/calendar2/michele-ceriotti-symmetry-locality-and-long-range-interactions-in-atomistic-machine-learning/
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