{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/polyatomic-complexes-a-topologically-informed","title":"Polyatomic Complexes: A topologically-informed learning representation for atomistic systems","arxiv_id":"2409.15600","date":"2024-09-23","proceeding":null,"authors":["Rahul Khorana","Marcus Noack","Jin Qian"],"abstract":"Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. 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