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SUMMARY:From Espaloma to SAKE: To brew\, distill\, and mix force fields wi
 th balanced briskness\, smoothness\, and intricacy. - Yuanqing Wang
DTSTART;TZID=America/Chicago:20230714T143500
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DTSTAMP:20261015T203023Z
UID:pretalx-2023-F9P3F3@cfp.scipy.org
DESCRIPTION:Force fields (FF)—the (parametrized) mapping from geometry t
 o energy\, are a crucial component of molecular dynamics (MD) simulations
 \, whose associated Boltzmann-like target probability densities are sample
 d to estimate ensemble observables\, to harvest quantitative insights of t
 he system. State-of-the-art force fields are either fast (molecular mechan
 ics\, MM-based) or accurate (quantum mechanics\, QM-based)\, but seldom bo
 th. Here\, leveraging graph-based machine learning and incorporating induc
 tive biases crucial to chemical modeling\, we approach the balance between
  accuracy and speed from two angles---to make MM more accurate and to make
  machine learning force fields faster.
LOCATION:Grand Salon C
URL:https://cfp.scipy.org/2023/talk/F9P3F3/
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