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SUMMARY:Ab initio thermodynamics with the help of machine learning - Dr. B
 ingqing Cheng\, Trinity College\, the University of Cambridge
DTSTART:20190603T131500Z
DTEND:20190603T141500Z
UID:TALK124504@talks.cam.ac.uk
CONTACT:Katarzyna Macieszczak
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.\nIn this talk\, I will discuss how to enable such pre
 dictions by combining advanced free energy\nmethods with data-driven machi
 ne learning interatomic potentials. I will show that\, for the\nomnipresen
 t and technologically essential system of water\, a first-principles therm
 odynamic\ndescription not only leads to excellent agreement with experimen
 ts\, but also reveals the\ncrucial role of nuclear quantum fluctuations in
  modulating the thermodynamic stabilities of\ndifferent phases of water.\n
 \nReferences:\n\n[1] B. Cheng\, J. Behler\, M. Ceriotti\, Journal of Physi
 cal Chemistry Letters 7 (2016) 2210-2215.\n\n[2] B. Cheng\, M. Ceriotti\, 
 Physical Review B 97 (2018) 054102.\n\n[3] B. Cheng\, E. A. Engel\, J. Beh
 ler\, C. Dellago\, M. Ceriotti\, Proceedings of the National\nAcademy of S
 ciences 116 (2019) 1110-1115.
LOCATION:TCM Seminar Room\, Cavendish Laboratory
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