BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Ab initio thermodynamics with the help of machine learning - Dr Bi
 ngqing Cheng  (Trinity College\, University of Cambridge)
DTSTART:20200308T100000Z
DTEND:20200308T104000Z
UID:TALK140842@talks.cam.ac.uk
CONTACT:Trinity College Science Society
DESCRIPTION:A central goal of computational physics and chemistry is to pr
 edict material properties using\nfirst principles methods based on the fun
 damental laws of quantum mechanics. However\,\nthe high computational cost
 s of these methods typically prevent rigorous predictions of\nmacroscopic 
 quantities at finite temperatures\, such as heat capacity\, density\, and 
 chemical\npotential.\n\nIn this talk\, I will discuss how to enable such p
 redictions by combining advanced free energy\nmethods with data-driven mac
 hine learning interatomic potentials. As an example\, for the\nomnipresent
  and technologically essential system of water\, a first-principles thermo
 dynamic\ndescription not only leads to excellent agreement with experiment
 s\, but also reveals the\ncrucial role of nuclear quantum fluctuations in 
 modulating the thermodynamic stabilities of\ndifferent phases of water. As
  another example\, we simulated the high pressure hydrogen system\nwith co
 nverged system size and simulation length\, and found\, contrary to establ
 ished beliefs\,\nsupercritical behaviour of liquid hydrogen above the melt
 ing line.
LOCATION:Winstanley Lecture Hall\, Trinity College
END:VEVENT
END:VCALENDAR