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SUMMARY:Machine-learning-driven advances in modelling amorphous solids - V
 olker Deringer\, University of Oxford
DTSTART:20210222T163000Z
DTEND:20210222T170000Z
UID:TALK157573@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Machine-learning-driven advances in modelling amorphous solids
 \n\n \n\nStructurally disordered materials continue to pose fundamental re
 search questions. Some of the most central ones concern the atomic-scale s
 tructure: how can we quantify an amorphous (non-crystalline) structure at 
 all\; how is the structure linked to properties? In this presentation\, I 
 will showcase recent advances in the modelling and understanding of amorph
 ous materials that have been enabled by atomistic machine-learning approac
 hes. I will demonstrate how atomistic ML models have given new insight int
 o the complex structural and electronic transitions in amorphous silicon u
 nder high pressure [1]\, and I will discuss initial applications and futur
 e perspectives in the area of battery materials modelling [2].\n\n \n\n[1]
  Nature 2021\, 589\, 59 (https://doi.org/10.1038/s41586-020-03072-z)\n\n[2
 ] J. Phys. Energy 2020\, 2\, 041003 (https://doi.org/10.1088/2515-7655/abb
 011)
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, Passcode: 000042\, https:
 //us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09
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