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SUMMARY:Machine learning as a solution to the electronic structure problem
  - Beatriz G. del Rio (Georgia Tech)
DTSTART:20210526T150000Z
DTEND:20210526T160000Z
UID:TALK160171@talks.cam.ac.uk
CONTACT:Chuck Witt
DESCRIPTION:An essential component of materials research is the use of sim
 ulations based on density functional theory (DFT)\, which imposes severe l
 imitations on the size of the system under study. A promising development 
 in recent years is the use of machine learning (ML) methodologies to train
  surrogate models with DFT data to predict quantum-accurate results for la
 rger systems. Many successful ML models have been created to predict highe
 r-level DFT results such as the total potential energy and atomic forces\,
  and initial steps have been taken to create machine-learning based ML met
 hodologies that can predict fundamental DFT outputs such as the charge den
 sity\, wave functions and corresponding energy levels. In this talk\, I wi
 ll present our latest results using deep learning neural networks to learn
  and predict the electronic structure of a large variety of carbon allotro
 pes\, and its extension to hydrocarbons.\n\nB. G. del Rio\, C. Kuenneth\, 
 H. D. Tran\, and R. Ramprasad\, J. Phys. Chem. A 124\, 9496-9502 (2020). 
LOCATION:Zoom
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