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SUMMARY:Anisotropic machine learning representations for coarse-graining -
  Arthur Lin\, University of Wisconsin–Madison
DTSTART:20241021T133000Z
DTEND:20241021T140000Z
UID:TALK222889@talks.cam.ac.uk
CONTACT:Eszter Varga-Umbrich
DESCRIPTION:Machine learning (ML) methods have revolutionized atomistic si
 mulations\, enabling highly accurate simulations and analyses at the fract
 ion of the computational cost.  Central to these advances is the use of at
 om-centered numerical representation of the atomistic system\, where one t
 ransforms the coordinates and identities of each atom in a way that preser
 ves the symmetries of the system. However\, atom-centered representations\
 , such as the popular Smooth Overlap of Atomic Positions (SOAP)\, are not 
 as well suited for describing large macromolecular systems\; in such cases
 \, one would likely be more interested in understanding how groups of atom
 s interact with each other\, either from a scientific or efficiency standp
 oint. To properly create a representation for groups of atoms\, we introdu
 ce an anisotropic generalization of SOAP\, which we deem AniSOAP. This gen
 eralized descriptor can describe the complex molecular geometries and capt
 ure orientation-dependent interactions that occur between groups of atoms.
  In this talk\, I will present three different case studies that use AniSO
 AP\, ranging from unsupervised analyses of liquid crystals to learning com
 plicated benzene energetics. From these studies\, AniSOAP gives us a data-
 driven way to observe how the molecular geometry influences the formations
  of certain phases or the energetics of particular configurations. I will 
 then conclude the talk by describing how AniSOAP can be incorporated into 
 a generalized coarse-grained simulation framework\, and provide my thought
 s on how it can be used to quantify information-loss incurred within coars
 e-graining.\n
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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