BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Tess Smidt (LBL) DTSTART:20200701T204000Z DTEND:20200701T210500Z DTSTAMP:20260423T040228Z UID:SciDL/6 DESCRIPTION:Title: Eu clidean Neural Networks for Emulating Ab Initio Calculations and Generatin g Atomic Geometries\nby Tess Smidt (LBL) as part of Workshop on Scient ific-Driven Deep Learning (SciDL)\n\n\nAbstract\nAtomic systems (molecules \, crystals\, proteins\, nanoclusters\, etc.) are naturally represented by a set of coordinates in 3D space labeled by atom type. This is a challeng ing representation to use for neural networks because the coordinates are sensitive to 3D rotations and translations and there is no canonical orien tation or position for these systems. We present a general neural network architecture that naturally handles 3D geometry and operates on the scalar \, vector\, and tensor fields that characterize physical systems. Our netw orks are locally equivariant to 3D rotations and translations at every lay er. In this talk\, we describe how the network achieves these equivariance s and demonstrate the capabilities of our network using simple tasks. We ’ll also present examples of applying Euclidean networks to applications in quantum chemistry and discuss techniques for using these networks to e ncode and decode geometry.\n LOCATION:https://researchseminars.org/talk/SciDL/6/ END:VEVENT END:VCALENDAR