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SUMMARY:Ab Initio Thermodynamics with the help of Machine Learning - Bingq
 ing Cheng\, EPFL
DTSTART:20190220T141500Z
DTEND:20190220T151500Z
UID:TALK108751@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:A central goal of computational physics and chemistry is to pr
 edict material properties using first principles methods based on the fund
 amental laws of quantum mechanics. However\, the high computational costs 
 of these methods typically prevent rigorous predictions of macroscopic qua
 ntities at finite temperatures\, such as heat capacity\, density\, and che
 mical potential.\nIn this seminar\, I will discuss how to enable such pred
 ictions by marrying advanced free energy methods with data-driven machine 
 learning interatomic potentials. I will show that\, for the omnipresent an
 d technologically essential system of water\, a first-principles thermodyn
 amic description not only leads to excellent agreement with experiments\, 
 but also reveals the crucial role of nuclear quantum fluctuations in modul
 ating  the thermodynamic stablities of different phases of water.
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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