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Accelerating Material Property Prediction using Generically Complete Isometry Invariants
May 8, 2024, 4:43 a.m. | Jonathan Balasingham, Viktor Zamaraev, Vitaliy Kurlin
cs.LG updates on arXiv.org arxiv.org
Abstract: Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, …
abstract algorithms arxiv cs.cg cs.lg machine machine learning material objects physics.comp-ph popular prediction property replacement representation simulation type while
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