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SUMMARY:Ab initio thermodynamics with the help of machine learning - Bingq
 ing Cheng  (University of Cambridge)
DTSTART:20200826T094000Z
DTEND:20200826T100000Z
UID:TALK150445@talks.cam.ac.uk
CONTACT:Joseph Nelson
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 [1]\, for the\nomnipre
 sent and technologically essential system of water\, a first-principles th
 ermodynamic\ndescription not only leads to excellent agreement with experi
 ments\, but also reveals the\ncrucial role of nuclear quantum fluctuations
  in modulating the thermodynamic stabilities of\ndifferent phases of water
 . As another example [2]\, we simulated the high pressure hydrogen system\
 nwith converged system size and simulation length\, and found\, contrary t
 o established beliefs\,\nsupercritical behaviour of liquid hydrogen above 
 the melting line.\n\n_References_:\n\n[1] B. Cheng\, E. A. Engel\, J. Behl
 er\, C. Dellago\, M. Ceriotti\, "Proceedings of the National\nAcademy of S
 ciences *116* (2019) 1110-1115":https://doi.org/10.1073/pnas.1815117116.\n
 \n[2] B. Cheng\, G. Mazzola\, C. J. Pickard\, M. Ceriotti\, Nature (_in pr
 ess_)\n\n*This talk will be held online using Zoom. Please "register your 
 email address here":https://forms.gle/9Rpf2BekocwLpmHD9 to receive Zoom li
 nks via email.*\n\n*Workshop website*: https://www.mtg.msm.cam.ac.uk/event
 s/msm-aimr-joint-online-workshop-2020
LOCATION:Online - Zoom
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