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SUMMARY:Accelerating Materials Science through High-throughput First Princ
 iples Computations and Machine Learning - Prof. Shyue Ping Ong\, UCSD
DTSTART:20190809T131500Z
DTEND:20190809T141500Z
UID:TALK127525@talks.cam.ac.uk
CONTACT:Bartomeu Monserrat
DESCRIPTION:In the recent decade\, materials science has seen a huge growt
 h in available data from combinatorial experiments as well as high-through
 put first principles calculations. With this data explosion\, we now stand
  at the cusp where machine learning techniques can make meaningful predict
 ions of many properties of materials almost instantaneously. In this talk\
 , I will discuss the potentially transformative impact that this “instan
 t” materials property prediction can have on materials research\, from p
 roviding new chemistry insights that will greatly improve our ability to 
 “guess” new materials with superior properties to accessing large leng
 th / time scales at near DFT accuracy. I will highlight some of the most p
 romising machine learning approaches thus far\, focusing\, in particular\,
  on techniques to address fundamental data size and diversity limitations 
 in materials science. Finally\, I will outline some of the key obstacles t
 hat still remain to ML-enabled materials science.\n
LOCATION:TCM Seminar Room\, Cavendish Laboratory
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