Anisotropic machine learning representations for coarse-graining

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• Arthur Lin, University of Wisconsin–Madison
• Monday 21 October 2024, 14:30-15:00
• Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT09.

If you have a question about this talk, please contact Eszter Varga-Umbrich.

Machine learning (ML) methods have revolutionized atomistic simulations, enabling highly accurate simulations and analyses at the fraction of the computational cost. Central to these advances is the use of atom-centered numerical representation of the atomistic system, where one transforms the coordinates and identities of each atom in a way that preserves 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 atoms interact with each other, either from a scientific or efficiency standpoint. To properly create a representation for groups of atoms, we introduce an anisotropic generalization of SOAP , which we deem AniSOAP. This generalized descriptor can describe the complex molecular geometries and capture orientation-dependent interactions that occur between groups of atoms. In this talk, I will present three different case studies that use AniSOAP, ranging from unsupervised analyses of liquid crystals to learning complicated 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 thoughts on how it can be used to quantify information-loss incurred within coarse-graining.

This talk is part of the Lennard-Jones Centre series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre
• Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT09

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