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UID:799daedcc224315fffb00c4e294eae47
CATEGORIES:Mathematical Physics Seminar
CREATED:20220221T201653
SUMMARY:Molecular Dynamics with Machine Learned Potentials
LOCATION:Zoom
DESCRIPTION:<p style="margin-bottom: 6px; text-align: center; background: white;">&nbsp
 ;</p><p style="text-align: center;">In the last decade, machine learning me
 thods changed substantially the way in which interatomic potentials are con
 structed from first principles quantum mechanics. In these approaches deep 
 neural networks, trained on electronic structure data, are used to represen
 t the potential energy surface. Molecular dynamics with machine learned pot
 entials has computational cost and scaling with size comparable to those of
  empirical force fields, yet it retains the accuracy and generality of the 
 adopted ground-state electronic solver. The scheme can be extended to model
  how the electric polarization in insulators depends on the atomic configur
 ation, making possible to study the evolution of the dielectric properties 
 of materials along atomistic trajectories.&nbsp;&nbsp;</p><p style="text-al
 ign: center;">&nbsp;I will present three examples of application of this me
 thodology, all of which are well beyond the reach of standard first-princip
 les molecular dynamics methods. In one, the homogeneous nucleation rate of 
 ice from supercooled water was calculated and found to be in good agreement
  with experiment. In another, the static dielectric constant of liquid wate
 r was extracted from the dipolar correlations using both periodic and react
 ion field (Kirkwood-Froelich) boundary conditions. In the third example, th
 e ferroelectric phase transition of lead titanate was studied, finding good
  agreement with experiment for the calculated enthalpy, the spontaneous pol
 arization, the specific heat and the dielectric susceptibility.&nbsp;&nbsp;
 &nbsp;&nbsp;&nbsp;&nbsp;</p><p style="text-align: center;">Finally, I will 
 comment on current limitations and challenges.&nbsp;&nbsp;&nbsp;</p>
CONTACT:Roberto Car – Princeton University
DTSTAMP:20260827T103135
DTSTART;TZID=America/New_York:20220302T104500
DTEND;TZID=America/New_York:20220302T234500
SEQUENCE:0
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