BEGIN:VCALENDAR VERSION:2.0 PRODID:-//jEvents 2.0 for Joomla//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VTIMEZONE TZID:America/New_York BEGIN:STANDARD DTSTART:20210301T104500 RDATE:20210314T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20211107T010000 RDATE:20220313T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20221106T010000 RDATE:20230312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20231105T010000 RDATE:20240310T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20241103T010000 RDATE:20250309T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20251102T010000 RDATE:20260308T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20261101T010000 RDATE:20270314T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20271107T010000 RDATE:20280312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20281105T010000 RDATE:20290311T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20210314T030000 RDATE:20211107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20220313T030000 RDATE:20221106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20230312T030000 RDATE:20231105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20240310T030000 RDATE:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20250309T030000 RDATE:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20260308T030000 RDATE:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:799daedcc224315fffb00c4e294eae47 CATEGORIES:Mathematical Physics Seminar CREATED:20220221T201653 SUMMARY:Molecular Dynamics with Machine Learned Potentials LOCATION:Zoom DESCRIPTION: \nIn the last decade, machine learning methods changed substantially the w ay in which interatomic potentials are constructed from first principles qu antum mechanics. In these approaches deep neural networks, trained on elect ronic structure data, are used to represent the potential energy surface. M olecular dynamics with machine learned potentials has computational cost an d scaling with size comparable to those of empirical force fields, yet it r etains the accuracy and generality of the adopted ground-state electronic s olver. The scheme can be extended to model how the electric polarization in insulators depends on the atomic configuration, making possible to study t he evolution of the dielectric properties of materials along atomistic traj ectories.  \n I will present three examples of application of this methodol ogy, all of which are well beyond the reach of standard first-principles mo lecular dynamics methods. In one, the homogeneous nucleation rate of ice fr om supercooled water was calculated and found to be in good agreement with experiment. In another, the static dielectric constant of liquid water was extracted from the dipolar correlations using both periodic and reaction fi eld (Kirkwood-Froelich) boundary conditions. In the third example, the ferr oelectric phase transition of lead titanate was studied, finding good agree ment with experiment for the calculated enthalpy, the spontaneous polarizat ion, the specific heat and the dielectric susceptibility.      \nFinally, I will comment on current limitations and challenges.   \n X-ALT-DESC;FMTTYPE=text/html:

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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.  

 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.       

Finally, I will comment on current limitations and challenges.   

CONTACT:Roberto Car – Princeton University DTSTAMP:20260827T094947 DTSTART;TZID=America/New_York:20220302T104500 DTEND;TZID=America/New_York:20220302T234500 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR