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CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20261020T074255Z
LAST-MODIFIED:20190311T141409Z
DTSTART:20190314T173000Z
DTEND:20190314T183000Z
UID:event2120@bu.edu
URL:http://physics.bu.edu/internal/events/show/2120
SUMMARY:Studies of 2D Materials Beyond Graphene: From First-Principles to M
	achine Learning Approaches
DESCRIPTION:Featuring Paul Hanakata\, Boston University\, Physics Departmen
	t\n\nPart of the Departmental Seminars.\n\nDissertation Committee:  David C
	ampbell\,Harold Park\, Pankaj Mehta\, Anna Swan\, So-Young Pi\n\nAbstract:\
	nMonolayers and heterostructures of 2D electronic materials offer promise f
	or observing many novel physical effects. Most theoretical studies of these
	 2D materials are based on quantum mechanical simulations described by the 
	density functional theory (DFT). This approach however is limited to small 
	simulation sizes (several nanometers)\, and thus inhomogeneous strain and b
	oundary effects that often dominate the behaviors observed experimentally c
	annot be simulated within a reasonable time. In the first part of the talk\
	, I will present a tight-binding model using continuum mechanics\, validate
	d by DFT calculations\, to predict strain-dependent changes in the spin and
	 electronic properties of ferroelectric Rashba materials. In the second par
	t of the talk I will introduce a machine learning approach based on convolu
	tional neural network to effectively search optimal designs in an exponenti
	ally large design space. With this machine learning approach\, one is able 
	to search for the most stretchable graphene kirigami design with 1000 train
	ing data in a design space of size 4\,000\,000. The continuum and machine l
	earning methods I have developed are important for studies and designs of g
	raphene and other 2D materials.
LOCATION:PRB 595\, 3 Cummington Mall\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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